350 is magic for me it's empowering people, getting people motivated and passionate about climate change and stopping climate change, building networks, building friendships, giving the best of your heart that you can to bring to and building movements. Rise definitely, break free even I wasn't a part of 350 at that time like big mobilization moments and small team discussions that we are becoming even more and more powerful and we hear people voices and we still keep we still organize grassroots all of this makes me very hopeful for our joint future.


Eight signs who have caused a narcissistic injury. The narcissist experiences a narcissistic injury when their ego has been bruised, when they feel less valuable or important, when something affects their sense of worth, the larger their ego is, the more at risk it is of being bruised, when they're entertaining their delusions of grandeur. That is actually when they are most vulnerable. Narcissists are very sensitive people. They always have to be right. They always have to be in control. So it is actually very easy to cause a narcissistic injury. You may find yourself walking on eggshells. You may become extremely cautious of your words and actions when you are around them because you're afraid of how they might react. But identifying if you have caused a narcissistic injury can help you to protect yourself, which is why I have decided to share these eight signs with you. One, narcissistic rage. The most obvious sign you have caused a narcissistic injury is narcissistic rage. They will get very angry.  They will say and do whatever they think will hurt you the most. In this state, they can be unpredictable. They can be very dangerous. They may throw things at you. They may damage your property and they may even become physically violent. Two, passive aggression. If the narcissist feels threatened by you, they will become passive aggressive. They will express their anger or annoyance indirectly in a way that is designed to hurt and confuse you. They will say something and then do something different. They will pretend to agree with you when they hold a different opinion because they're afraid of the consequences of expressing their anger directly towards you. Maybe they're worried that they might lose some of the conveniences that you're providing to them. So instead, they become passive aggressive. They push just to the edge of what you're willing to tolerate so that it's not enough for you to call them out on it so that you don't have a firm conviction to disconnect from them, which enables them to continue their behavior. Three, criticism. When the narcissist experiences a narcissistic injury, it's because they feel less valuable or important, but they don't want to reflect on these feelings. It's too painful for them to deal with. So instead, they try to put you beneath them by targeting your sense of worth. They do this by criticizing you by indicating your faults in a disapproving way to make you feel like you're not good enough because that's exactly how they feel. But if they can put the focus on you, it makes them feel better. It makes them feel like they're not the problem. And that's what their criticism is designed to do. It's designed to deflect the focus onto you. It's designed to keep you looking at yourself because they don't want to feel like they're at fault. They don't like how it feels. And they don't want to do the work to change their dysfunctional behaviors. Four, opposition. When you have caused a narcissistic injury, the narcissist will immediately become oppositional to you. They will refuse to do what you want them to do. They will be very disruptive. If you pick up on any of their flaws, there has to be something wrong with you. You have to be wrong because they just can't accept that they're anything less than perfect. When you question or confront them, they feel like they're losing control. They feel like you're taking back your power. So to regain control of the situation, they become opposed to whatever you stand for. Even if they secretly agree with you, if it's unfavorable for them, they have to be in opposition to it. They will only agree with something if it benefits them. Five, silent treatment. Sometimes the only thing the narcissist can do is be silent. If they know that the situation doesn't warrant a certain reaction, they have no choice but to be quiet. They have to accept it. But the silent treatment is also designed to punish you. It's a control tactic. It's designed to make you question yourself. It's designed to make you feel like you've done something wrong. Six, triangulation. When you cause a narcissistic injury, it affects the narcissist's sense of worth. It makes them feel like they're not good enough. It makes them feel like they're at fault, which is something the narcissist doesn't want to accept. So they run from the situation. It may seem like they're running from you, but they're actually running from themselves. It may seem like they've found someone else, but they've actually just found someone who reflects back to them this ideal version of themselves, someone who validates their false self, someone who supports the illusion. And this is what they then reflect back to you. It's meant to hurt you. It's meant to make you regret your decision. It's meant to make you blame yourself. Because now that we're someone else who doesn't see anything wrong with them, it might make you second guess yourself. It might make you think you made a mistake. But you didn't make a mistake. This new person has been manipulated by the narcissist. Just as you were when you first met them. They don't know what they're really like. But the narcissist will use this to hurt you and make you feel like you're at fault. Seven, victim playing. The narcissist will not take accountability for their actions.  They will lie. They will make false accusations. They will exaggerate your faults and mistakes, will minimize or deny their own. This is how they evade accountability. This is how they avoid any consequences for their actions. By passing the blame onto someone else. By holding you accountable. And they do that by playing the victim. By acting as though you did something to them. They will often blame you for the very things that they caused. The very things that they did to you. It's projection. They disown the parts of themselves that they don't like and assign them to you. This is designed to make you question yourself. It's designed to make you take responsibility to correct the situation while protecting the narcissist's fragile sense of self. Eight, smear campaign. If you don't fall for the narcissist victim playing, they believe that someone else will. So they manipulate other people. They show them the false character in the hopes that they will buy into the illusion. They will tell them horrible things about you. They will bring up things that you confide in them. They will bring up things from the past. They will fabricate stories. They will say whatever they think will make you look bad. While acting as though they did the best they could, they try to help you. This is designed to punish you. For not complying with their agenda. And it's also designed to take the focus off of them. And all of the faults they made. Narcissists run from accountability. They don't want to deal with the shame. They don't want to deal with the consequences for their actions. When you cause a narcissistic injury, it resurfaces everything that the narcissist tried to bury within. Everything they despise about themselves. Everything that they cannot control. When you cause a narcissistic injury, it may seem like the narcissist hates you. But they actually hate everything that you are resurfacing about themselves. That's what they're in opposition to. That's what they're running from. Because it's something they don't want to accept. It's something they don't want to deal with. But the truth is that when you cause an injury, it really has nothing to do with you. All they see is the ghost of everything they once were. Everything they hated about themselves. Everything they tried to hide. And nothing makes the narcissist angrier than that. Thank you for watching. I hope this video resonated with you. Please like, comment, share and subscribe. If you would like to donate, my PayPal link is in the video description. Thank you for watching and I'll talk to you soon.


Hello, everyone. My name is Devhavi Fadnes. I'm a Product Manager at Microsoft Excel, and I'm here today to talk with you about how to deal with uncertainty as Product Managers. Minimizing uncertainty and finding out the right next step for your product is something that Product Managers spend most of their time on in their careers. I thought that I'd like to share some of the learnings I've had so far in my career that helped me deal with uncertainty in an effective manner. I'm really excited to share my learnings with you, and I really hope that they add some value to you. So let's get started. Before we talk about how to deal with uncertainty, I think it's important that we spend some time talking about, why do we want to minimize uncertainty, and what are some of the areas that we must focus on? As a Product Manager, what do you think your most important task is? I think that it is to build a product that is successful. What I mean by successful product is a product that satisfies our customers and is profitable for our business. So this product success is really dependent on three things. Product desirability, feasibility, and viability. Product desirability means that there are enough number of customers in the market that have a specific need or a pain point that this product or idea solves for. Hopefully, that pain point is recurrent, meaning that customers will keep coming back to your product and using it for a foreseeable future. Product feasibility means that you have the means, resources, time, budget, partnerships, expertise, and dependencies you need to build a product that solves for the pain point that you have identified efficiently. Product viability means that at some point in the future, you'll be able to generate revenue streams and hopefully profit for your business using the product that you've built. So to build a successful product, it's really important that you are working with the idea or a pain point that customers actually need solved, something that you can build and something that you can monetize in the future. That's why it becomes really important for product managers to try to minimize uncertainty and risks associated with product desirability, feasibility, and viability and find that sweet spot at the intersection of these three that actually increases your chances of building a successful product. Well, in short, how do you minimize this uncertainty? Is you gather evidence that points you in the right direction. So when you're working with a new idea and your uncertainty is really high because you know very little about anything at all, your only goal should be to gather as much evidence as you can that points you in the right direction. So what are some of the sources that you can use to gather evidence and minimize uncertainty? Well, there are three major sources of evidence, customers, competitors, and internal teams. Each of these sources will give you varying degree of information about desirability, feasibility, and viability. And so depending on which uncertainty you're trying to minimize, you might want to pick the best source or sources to go to to gather more evidence. For example, like customers, I think are a major source of gathering evidence related to desirability and viability. Will they be able to tell you a lot about whether you as a team can actually build an effective product for them? Maybe not, right? But your internal teams will have the information you need to determine whether or not a building of particular product is feasible for you or not. So if you're working on gathering evidence for feasibility, your internal teams like design, research, engineering, data science, other teams that are maybe working on related feature, those are the ones that you might want to talk to to gather more evidence. And competitors are a moderate source of gathering evidence related to desirability and viability. When I say moderate is because really whether or not customers have a particular pain point is something that only customers can tell you for sure. Competitors will give you a good idea about where our competitors are headed, but don't just do something or follow something that your competitors are doing if you don't have the evidence for it from your customers. And in terms of viability, you can look at competitors and talk to internal teams again, such as legal sales, marketing to sort of determine what price point you want to set for your services or what is the expected cost or revenue or other things related to viability. All right, so when you're gathering evidence from customers, competitors or internal teams, just remember certain rules of thumb. One is that please hire importance to what your customers are saying compared to what competitors are doing for the reasons that I mentioned earlier. Focus on volume. OK, so even if you're gathering information from customers, focus on how many customers are pointing you in the same direction rather than rather than using a single data point or a single anecdote as evidence to proceed in a certain direction. And when it comes to picking which of the three areas to focus on or prioritize, I would advise you to prioritize desirability first and try to minimize risks associated with desirability and then sort of proceed with feasibility and viability because really at the core of it, you want to build a product that your customers want. You know, even if you build a very efficient product that that someone is willing to pay for, if it is not something that a lot of people actually need, you're not going to be very successful in building that product. So focus on desirability above feasibility and viability and then move on to those later. OK, so now you know what sources are there to sort of gather more evidence. Now what I'd like to do is I'd like to walk you through a framework to tell you how do you gather evidence from these different sources and try to minimize uncertainties that we spoke about. So the first step is to gather weak evidence. What the weak evidence is, is it consists mostly of people's opinions, their beliefs and their insights from their past experiences or past learnings. Some of the modes of gathering weak evidence are trend or competitor analysis, customer interviews, customer surveys, talking to teams internally or organizations to learn from them about their insights from past products, past experiments and past experiences. Weak evidence, you know, there are there are only so many customers that you can interview. There are only so many team members that you can talk to. There's only so much information available on the trend and the competitors. And this is why in weak evidence, you do not get significant volume of data points. You have fewer data points in case of weak evidence. And because of that, I would encourage you to not spend too much time on gathering weak evidence. Now, given all this, you might be tempted to just say that, hey, I'm going to skip this step and just gather strong evidence for my assumptions or hypothesis. Well, I would really encourage you to not skip gathering weak evidence for two reasons. One, gathering strong evidence for your assumptions is really expensive. And so you will not be able to gather strong evidence for all of your assumptions. And you can do that in this step of gathering weak evidence. Second is, although you have fewer data points in case of weak evidence, they can give you some profound insights and point you in the right direction. You know, you can you can discard things or assumptions that will absolutely not work for your product based on the evidence you receive in this step and minimize the hypothesis or assumptions that you want to gather strong evidence for. So this is really a critical step that can give you profound insights on to the about the overall high level direction that you want to proceed in. And so do not skip this step, in my opinion. The next step is to remove biases when you're deriving key insights from the evidence that you've gathered. And this is super important because as product managers, you know, you have this really cool idea that you want to work on, you have spent time in gathering weak evidence for it. And at this point, you are really married to the idea and you want it to work. And this can sort of this can make you analyze the data points of the evidence that you have gathered in a biased way. And so it is super important that you analyze the information and derive insights in a very non-biased, rational way. Some of the tips that I have for you to sort of remove your biases is, you know, consciously try to work to disprove your beliefs rather than proving your beliefs. If you're going in customer interviews or just talking to other teams to gather evidence, have more people join you in those conversations. That way, you know, if multiple people have different interpretations of the information that was shared in those interviews or meetings, it'll help you to sort of carefully consider different opinions and viewpoints and come to a rational conclusion. Don't share summaries with people about what your understanding of the evidence is. I would encourage you to share raw data or raw information that you have gathered as part of your evidence and ask multiple people to review it and share their interpretation of the takeaways from that evidence. So really cautiously make sure that you are analyzing evidence in a very non-biased way and driving insights rationally. Okay. So at this point, you have some assumptions that you think might work for your product. And so what you want to do is, you want to convert your assumptions into key hypotheses. Okay. So hypotheses that are related to desirability might include things like pain points, value proposition, something that will get you engagement. The ones related to feasibility will talk about resourcing, key activities, partnerships, and the ones related to viability will talk about costs, pricing, profit, etc. Now, when you are creating key hypotheses from your assumptions, make sure that your hypotheses follow these three characteristics. One is that your hypothesis is testable, meaning that you can measure your hypothesis and the outcome of it can be a true or a false. So you can confidently say whether your hypothesis failed or not. So make sure that it is testable. So, for example, a hypothesis that looks like redesigning the website will increase user engagement is a very poorly formed hypothesis that is not testable, right? And that is because you don't know how you're going to redesign the website or how are you actually measuring increase and user engagement. So if you modified this hypothesis a little bit and said something like redesigning the website with design A will increase total time spent on website per user. So now you have a measurable testable hypothesis that can either fail or succeed based on whether the total time spent on the website per user actually increased or not. The second characteristic of a good hypothesis is that it has a clear goal and a success criteria. So a hypothesis that looks like redesigning the website with design A will increase average time spent on the website per user per week by 10% for users between ages of 20 and 45. This is a very good hypothesis that has a clear goal and criteria. You know which customer segment you're working with. You know what design you'll use for your experiment. You know which metric you're going to focus on and you know how much you're looking to move that metric to be successful. And the third criteria is that your hypothesis must be atomic. When you're testing the hypothesis, what you're really trying to do is you're trying to figure out whether the cause has the effect that you were predicting, right? And so you should really have one cause and effect in a single hypothesis. Don't try to mix multiple variables in a single hypothesis that that's not going to give you clear results. Now, once you have your key hypothesis mapped out, what you need to do is you need to prioritize your hypothesis. You cannot proceed to gather more evidence for all the hypothesis you have.  So, you really want to pick a few hypotheses that you want to gather more evidence for and that's where prioritizing them becomes super important. One easy way to prioritize your hypothesis is to ask two questions. One is, which of these hypotheses need to be absolutely true for my product to succeed? So you're ordering them based on their importance for success. And second question you can ask is, which of these hypotheses I lack concrete evidence for? So you're really ordering them first by importance, second by evidence. And you want to pick the hypothesis that are really critical for your product success and you do not have the evidence for yet. And you want to sort of focus on those and then move on to gathering strong evidence for the hypothesis that you picked. Now, strong evidence usually is significant in volume. It consists of facts, real world settings, what people actually do instead of their beliefs and opinions. Some of the ways of gathering strong evidence are actually building experiments to test out your hypothesis in real world. Now, remember that building experiments is costly and so you really want to be careful when you're testing your hypothesis using experiments. So if you can, I'd encourage you to start with building scrappy experiments. Now I know that this is not always possible depending on which product you're working for or which organization you work in. But if you can, try to begin with building scrappy experiments that give you information that we need to proceed and then take the next step. So some of the examples for building scrappy experiments are Wizard of Oz or fake landing pages. So let's say if you are looking to test the desirability of a particular product and you don't know whether it's gonna work or not, right? So what you can do is instead of building the entire automated backend for that system, you can build the UI and as customers interact on that UI, you can have actual manual humans sort of interacting and proceeding or taking users through the next steps. And if that works, if you find a lot of customers engaging with that experience in a way that you were hoping, then as a next step, you can actually invest in building the entire automated backend. Or let's say if you wanna understand if you embed a URL in a webpage, will people actually click on it and then consume the content of that URL? If you wanna test that in a scrappy way, you can create a fake landing page. You can embed the URL in a webpage that you want.  And then just measure how many clicks you get and if you think that those are significant, then you can actually invest in building the experience that you want to build. So these are some of the ways in which you can gather strong evidence and because the experiment is sort of, experiment gathers significant data points. It is usually a very good indicator of what you should do next, depending on the result of the experiment. If you do not have a lot of users to give you data really quickly, you can choose to run experiments for a long time and gather more data points that way. So okay, so now you have gathered more evidence, strong evidence that you can use. Again, you have to make sure that you remove your biases. I cannot stress this enough. It is super important to remove your biases when you're analyzing your evidence. In cases of experiments, this is where having a hypothesis that has a clear goal and success criteria is really helpful because if you really have a clearly defined success metric, analyzing the results of the experiment becomes very clear. You either met the goal or you didn't. But let's say if the results of your experiment are sort of inconclusive, you might be tempted to say that, hey, let's just roll it out to production because it's not worsening anything if it's not making anything better. But this is where removing biases is something that we should remember. Don't do that. Just make sure that you sort of have multiple people take a look at the raw data and get their insights into it as well and remove biases and do the right thing for the product. Okay, so now you went from gathering weak evidence to strong evidence for your key hypothesis. Now what you can do is you can run multiple experiments for the same hypothesis to gather even stronger evidence that points you in the right direction. Or you can choose to repeat the entire process to gather more information to add features or to remove features that don't work any longer that you already have in the product. Remember that product building is not a linear process. There will never be a time when you have figured it all out. So you have to keep on gathering more evidence and minimizing uncertainty in an effort to build a successful product. So you have to keep on repeating this framework that we spoke about and keep on iterating on your product. Okay, so in summary, in order to build a successful product, PMs have to minimize uncertainties associated with product desirability, feasibility and viability. When you're uncertain, your only goal should be to gather more evidence that points you in the right direction. Some of the sources that you can use to gather evidence are customers, competitors and internal teams. Different modes of inquiry will give you a different degree of evidence. So you can begin with gathering weak evidence and then move towards strong evidence. And you can repeat experiments or run multiple experiments for same hypothesis to gather even stronger evidence. One thing that you have to keep in mind when you're analyzing the evidence you've gathered is to consciously remove your biases in order to really do the right thing for the product and for the customer. And lastly, product development is not a linear process. So you have to keep repeating these steps and keep gathering more evidence to reduce uncertainty and add to your product success. All right, that's it for me today. Thank you so much for joining this webinar. I hope you got some value out of it. If you have any more questions or any feedback for me on this presentation, please feel free to reach out to me on LinkedIn and have a great rest of your day. Bye.


Let's talk about the groups because the groups very interesting I just went through the betting I just went through the favorites very very even. Let's go see who has the best and the worst group for them Okay, so here are the groups so the Sabalenka group That's what I'm gonna call it because I'm not pronouncing those names again Sabalenka group the number two in the world Sabalenka She is going to have to play against number five Sarkari she's gonna have to play number six in the world Sfiantech and also the number eight in the world Bodosa that group for me is fire it's almost like that is the I Wouldn't say like that's almost like the new generation group and then the next group is the more of the veteran group because Sabalenka's early 20s I think Sarkari's in her mid 20s Sfiantech early 20s Bodosa early 20 So it's the young group up the top there. Then you've got in the second group the Kredjikova group She is number three in the world. She is gonna be playing plush Kiva who's number four in the world Muga Ruta who's number seven and a net conservator is number nine in the world and that second group I think Kredjikova is like 26 Plush Kiva Muga Ruta they're hitting 30 and Conturbates in her late late 20s as well. So the the bottom group are the experienced players the veterans I guess you could call them so it's weird that it's kind of worked out that way But like I said the two best players from each group. So let's say Kredjikova and plush Kiva Win the most matches. They will go into the semis and let's say they play against Sarkari and Sabalenka who played the best in their group. So that's how it works in this Okay, let's start with the world number two the number one seed the best player in the competition this year Sabalenka so here is Sabalenka's Resume I guess you could call it a profile against everybody else and a head-to-head record So her best result never played this event. This is her debut at this event now She's gonna be playing Sarkari, Bedossa and Sveontek. She's a 4-1 record against Sarkari So she'll be loving her chances against Sarkari. She has a losing record against Bedossa But they've only played the one time and she's never actually played Sveontek. She's kind of weird I would have thought they would have crossed paths seeing their similar age and they've also come up at the same time You know the both kind of over the last two years Really, you know shot up the rankings never played. They are gonna be playing though at least once in this tournament. So Not a bad not a bad draw for Sabalenka Not a bad group because you think she can beat Sarkari Bedossa is gonna be a tough one because Bedossa is in good form and with Sveontek she's never played So that's gonna be a maybe a random that she could win that one as well usually if you win two matches you get to the semi so You think she's gonna get at least one of them and maybe she can get either Bedossa or Sveontek if she can already beat Sarkari The second group she's playing. This is the semi finals of the final who she might meet Kredjikova 2-0 record wouldn't mind playing her plushkiva. She has a 2-2 record They've actually played a lot this year as well including that Wimbledon semi-final then Mugaruta She has a losing record against 1-2 so she's probably lucky that she's avoiding Mugaruta and Contivate she has a 4-0 record against so Contivate gets through to the semis and plays against Sabalenka Sabalenka would love that so there you go Sabalenka that is her Record by the way her combined record against everybody combined 13 wins 6 losses And that is a 68% win-loss ratio percentage I should say against everybody in the competition, which is actually the second highest out of All the players so she has the best record against everybody And that's why she is the one of the favorite or the favorite Maria Sarkari. She is again a debutante at this event hasn't played this event before We've just went through the Sabalenka Matchup that's her worst matchup 1-4 Sabalenka is gonna be a tough one. So if Sarkari Doesn't it does in fact lose to Sabalenka. She's gonna have to beat Bedossa and Sveontek. Luckily against Bedossa She's never played Bedossa. So that's always gonna be a Fresh, you know a fresh start. We don't know what's gonna happen in that one But she has a good record against Sveontek 2-0 so she could get those that win and the Bedossa win might be the crucial one If she does end up losing to Sabalenka So never know maybe she upsets Sabalenka and then she'll be liking her chances even more against the other group She has a 0-3 record against Kredgikova So she's probably really excited that she doesn't have to play her until maybe the semis She has a 2-1 record against Pushkova 1-1 record against Mugaruta and she actually has a pretty decent rivalry with Contovate 6-5. They played more More times than any two players in the competition or in this competition So if she does play Contovate in the semis, that'll be an epic matchup as it has proven to be over their career so far So Sarkari look she's the second Second least favorite to win the event behind Mugaruta. So it's probably because she has a pretty tough tough group to play through Egas Sveontek she is also making her debut here She would have played last year of course if she had had the tournament, but the French Open champion from last year She gets her try this year She has never played Sabalenka as I said before. It's very kind of crazy to me I would have thought they would have at least crossed parts once but no they are crossing parts for the first time Today or this week. She has a losing record against Sarkari So that's gonna be a tough one for Sveontek to get through. She also has a losing record against Bodosa So she's gonna have to beat Either Bodosa or Sarkari and if she does get the Sabalenka win So it's gonna be a tough one for Sveontek. So a lot of people that you think that Sveontek is gonna win I don't know if she can get through the group. You know, she has a losing record against two of the three And she hasn't played the other one. So I don't know Could she win? Of course she could. Of course she could get through all of those players She could be 3-0 against all those players, but just based on head-to-head record It's a little bit a little bit dangerous against the other group She's actually got a decent record, which is kind of a shame for her 2-0 against Kredjikova 1-0 against Pleszkova. 0-1 against Mughurutha and 2-2 against Conservate so she would have really liked her chances against the other group Unfortunately, she doesn't get to play any of them until maybe the semi. So if she does sneak through She might like her chances from the semi finals onwards, but it's gonna be a tough one for Sveontek to get through the group The final player of this group Is Bodosa who snuck in because of the Indian Wells win So a great win to finish the season and has a pretty decent record against the players in her group She's beaten Sabalenka 1-0 never played Sarkari and she's beaten Sveontek 1-0 So she does have winning records against two of the three players So she might be able to get through and with the form of Indian Wells and the confidence of that and the way that she won The final against Azarenka She probably like her chances against everybody in her group and she could definitely make the semi-finals No doubt about that against the other group if she does make the semi. She's gonna have to play One of these players Kredjikova. She's got 2-0 Record against so that's really good to see against a fresh open champion So we'll see if she can do well if she does play her a 0-2 against Pushkova So she wants to avoid Pushkova never played Mugavruta and against Contavate 0-1 So she's lost had the loss to Had the loss against Contavate So like I said, she has a good record against the players in her group and she'll be liking her chances And who knows semi-finals are definitely on the cards, but who knows maybe she can make the final All right, let's go to the other group now the Kredjikova group. So let's start with Kredjikova French open champion. It's her debut as well. So look at that. Everybody's making their debut in So far right against her own group. She has a pretty good record. It's it's it could it's okay Mugavruta, she's got a 2-1 record against and who doesn't want to see that match up I mean, we all know what happened at the US Open. These two players don't like each other Well, at least Mugavruta doesn't like Kredjikova. So we're gonna see the fireworks in that one I'm sure 2-1 record as well. So she liked that her chances there 0-2 against Pushkova. They're not not a great record there, but You know, maybe she could Maybe she could get through Pushkova. I don't know it depends on I guess it depends on how Pushkova is feeling and Contavate she's never actually played. So she'll be liking her chances I reckon Kredjikova and a lot of you like her chances, too The only problem I have is that she's playing this the doubles as well So is she playing too much tennis? Remember, it's usually one day on one day off She'll be playing every second every day So she's gonna play twice as much as everybody else in the entire competition. Is that gonna be too much? We'll find out against the other group Sabalenka. She's got a 0-2 record against so she's happy that she's avoiding her Sakuri, she's actually got a 3-0 record against so she wishes she could have played Sakuri 0-2 against Bedosser and 0-2 against Feontech So to be honest if you'd replaced Pushkova with Sakuri, it would be the perfect group for Kredjikova but She avoids a lot of the killers that she's lost to in the past And has a bad record against she's gonna have to play some of them Maybe in the semis well, she's gonna have to play one of them in the semis But who knows it might be Sakuri if she gets lucky. So there you go, Kredjikova Her group is is decent, but she's playing a lot more tennis than the rest All right, Caroline Pushkova the first player in our group here to to actually have played this tournament before Her best result has this is the semi-finals. She made it three times in a row 2017-2018 the last one was 2019 so two years ago and she has a really good group like I don't know if anyone said Pushkova to win But Pushkova could get out of the groups quite easily I think she's making the semis again. She has an 8-2 record against Muguruta So that's a confidence. I mean that's not a fluke 8-2. It means that she knows how to beat Muguruta Kredjikova, she's got a 2-0 record against and a 3-0 record against Contobate. So I Don't think Pushkova is I think Pushkova is getting out of the group I think she's coming first or second She might be the favorite to get out of the group based on those head-to-head records. So yeah Muguruta That 8-2 is is massive It's a massive Muguruta's gonna have to do something special to beat Pushkova in that one against the other group She actually got a decent record against the other group as well So Sabalenka, it's 2-2 as I said before they played a couple times this year as well against Sarkari 1-2 Against Padosa, it's 2-0. So that's not bad And some of you think Padosa is gonna get through the group which means that she'll be playing possibly Pushkova Anyway, so Pushkova like her chances there and against Vjontek 0-1 and of course against Sveontek It was this year in Rome and she lost six love six love So she does not want to play against Vjontek and remember those nightmares that she had during that event So it's obviously a hardcore event. So it's a little different But still you don't want to play someone who beat you up six love six love at all So yeah, so I look I think Pushkova is a legit chance of Getting out of the group minimum But like probably maybe even making the final if she gets a good matchup in that semi If she plays Sabalenka in the semis, I'm put money on Pushkova. Pushkova beats Sabalenka in the semifinals I just I just think Pushkova is a very She's actually the the second least likely to win With Sarkari, but I would put money on Pushkova.  So yeah, Pushkova and also her recent results as in at this tournament She's done well here in the past. She's always made she's made three semi-finals. I mean, that's not a fluke Okay, let's go to Muga Ruta now another player that a lot of you think it's gonna get through but look at her record It's not great against the team or the players in her group Her best result here was the semi-finals back in 2015 So the only other player besides Pushkova to actually have played this event and that'll count for something But against Pushkova like I said 2-8 is the record against Muga Ruta So that's not great against Kredgikova 1-2. That's not great either because remember they played the US Open Kredgikova was kind of dominating until she had that, you know, until that second set and then she was in trouble Then she time out. We all know what happened but Kredgikova, she knows how to beat Muga Ruta and it gets Contovate. It's even at 2-2 It's gonna be a tough one for for Muga Ruta, right? I can't see her getting out of the group Unfortunately, she's gonna have to beat a couple of players that she struggled in against in the past It's gonna be tough. It's gonna be tough for Muga Ruta And that's probably why she's the least favorite to win because her group is probably the worst for her It's the worst for her but when she if she does get through the semis She's gonna be playing one of these players in the other group Sabalenka. She's got a 2-1 record against. So that's good Sarkari, it's 1-1 but Dosa never played but Dosa and 1-0 against Feontex So if she does get through the this group and make the semis She might be able to go on and win the whole thing based on her results against other players from the other group But unfortunately, she's gotten the probably the worst group out of everybody. She has gotten this short straw that's for sure and Contovate a player that everybody in the chat was going crazy for when she won the event back in Transylvania a couple of weeks ago to get Into this WTA finals and it's her debut of course just like everybody else pretty much and she's got an okay record, but it's Not great. She is the second favorite to win as well. By the way, so a lot of people think that she is going to win But she her record is okay. It's okay 2-2 against Muga Ruthas. So that's a 50-50. She's a losing record against push cover zero and three So there's something to worry about if you're if you're going to for Contovate and it gets credit covers She's never played her so there's always a chance if you've never played somebody there's always a chance You could win that of that first matchup So look Can she get through yeah, of course she can She might not beat push give up, but she has to beat Muga Ruther and Kredgikova. Look, maybe she can do that But it is a pretty tricky group for Contovate She would have liked to play in the other group I think some of the other players from the other group that have maybe a little bit less experience then got like she's got to play two players that have played this event before and A Grand Slam champion in Kredgikova. So she's playing against players that know how to play on the big stage And you know not only play on the big stage, but also win on the big stage Against the other group if she does make it through she'll play one of these players Sabalenka she has a terrible record against zero and four so she wants to avoid Sabalenka at all costs She has a like I said a decent rivalry against the Sarkary Five and six so that'll be a blockbuster. I hope we get that I don't know if we will but if we get Sarkary versus Contovate that is an absolute blockbuster She's a 1-0 record against Podosa and 2 and 2-2 Against Sveontech so look if she does get away out of the out of the group Then maybe if she avoids Sabalenka, maybe she can get into the final and win the whole thing, but I don't know if she gets out of her group That's the only thing I'm a little worried that you know those 50-50 matchups if she does lose against Pleskov She's gonna have to win those two 50-50 matchups because I like I said two wins gets you through Three guarantees you through but two gets you through pretty much So there it is That is the WTA finals and they're the groups now.  It's all about matchups It's all about head-to-head records and head-to-head records matter Pleskova is my Based on all of that Pleskova is looking Strong to me just because she's got a good record against everybody in her group She's got a good record at this event. She's made the semi-finals here three years in a row


Not everyone Sam here with late and sports cards We were up in two boxes of 2022 pretty immaculate baseball first off the line a hobby for James M So good luck here James. Let's pull you a What's it called the Out of two variation or out of three excuse me. There's your first off the line So excited for release days tomorrow spectra basketball three and two baseball and Certified football big release day tomorrow actually Got Dylan Carlson. That is Three out of ten, I'd say Player worn on that one. It's a little bit of Cardinals patch there and we've got now. That's awesome Maglio Ardonias That is 75 out of 99 We've got to go with the one you pulled last night this time one out of two Josh Jung For the Rangers and you're gonna have a great Josh Jung PC I have to say James. That's awesome though with part of the star from the Rangers logo That is nasty. Congrats, man Very nice patch for sure. We've got that is nine out of ten. We've got a Connor Wong on the iBlack Jersey Auto, that's what I'm saying Mike. He's a good player Nice Connor Wong there. This one's nasty 44 out of 49 how about an Adley Ruchman on the Prospect Auto Prospect Jersey Auto Player worn on the Adley. Very nice Then we've got 44 out of 99 Jose Devers going to the Marlins on the acetate shadow box auto Yeah, there's your first off the line sticker. We pull another one out of two. That'd be cool So I guess they are out of two. I thought they were out of three But there's they're out of eight and out of two first off the line Brandon Lau That is a six out of ten for the raise player worn on that one. Nice one there We've got a eight out of 25. That is moose scouring For the Yankees game worn on that one too. Very nice We've got nice first off the line RPA three colored is Jaren Duran Congrats on the JD Nice Jaren Duran RPI for you there James. Take that for sure We've got going to the Dodgers. That is eight out of ten Ryan Pepe or Pepeo Next up we've got for the Red Sox two out of ten. That is Tristan Casas On the Prospect Auto a lot of good really good prospect. I was gonna have this I have to say that's solid And to finish it off we've got for the Phillies 33 out of 99 Reese Hoskins shadow box signatures Very nice there his home Ronnie. He hit Got Citizens Bank Park absolutely rocking Right exactly exactly That will do it for your break there James Congrats on the hits man. Thanks again for the support We'll get those hits right out to you


Thank you. Thank you everyone for coming. My name is Arun. I am a senior engineer at Yahoo's OpenStack team from San Francisco Bay Area. My talk is on things we have learned from scaling ironica to Yahoo. My talk has three, four parts. First, part is on our cluster architecture and background. Second part is on ironic and ironic conductor. Third, we'll discuss about neutron and neutron DSP agent issues. And fourth, we'll discuss about the importance of doing a density test. And finally, there will be time for questions. Also, please feel free to ask any questions in the middle. So Yahoo has hundreds and thousands of servers in its data centers. These servers are used to serve anything from Yahoo's friend page, Yahoo Finance, Yahoo Mail. And these servers needs to be managed by a coherent system. So we decided to use OpenStack ironic to manage all the compute resources at Yahoo. This talk is about things we learned from scaling ironic at Yahoo. Let's dive into our cluster architecture. So, hopefully it's visible there. So we have ironic cluster in every major data centers, Yahoo data centers. And in the middle, in the middle, this is our, we have like three API boxes. All these API boxes run most of our ironic API services. It runs Keystone, Glance, NOAA, Ironic API, all the APIs. Apart from APIs, these API servers also run NOAA scheduler and NOAA compute. So NOAA scheduler and compute are behind a leader election script. So we have some kind of HA going on there. And the users access the APIs through a VIP and a ATS. And these APIs are connected to a database. There are two databases in master and slave modes. And we also have two message queues. And on the right side, we have ironic conductor. So ironic conductor, we have two machines. These basically create the pixie configuration, always, you know, creating the things needed to boot and OS in an old. This ironic conductor is also connected to our OuterBand network. So this is how we basically turn on and off machines over IPMI. And ironic conductor generates all the pixie configuration and the OS kernel RAM disk and all those things and rise into an NFS share. And this NFS share is read write mounted on each and every conductor. So this NFS share also read-only mounted on a box called ITS. We call it the ironic transport service. We do not have ironic transport service in an upstream. So this is a Yahoo! specific thing. So basically, ironic transport service runs DHCP, TFTP and Apache to serve the operating system files. Since DHCP runs there, we also have the Neutron DHCP agent running there to receive and DHCP notification from Neutron. We also use IPXY. So whenever a node boots, the IPXY binary is sent over TFTP and everything from there is HTTP. So before ironic, Yahoo! had a mix of grizzly bare metal and a legacy imaging system. So we had to move all these machines, which are actually serving production traffic, powering yahoo.com and yahoo.mail to the new system. Yahoo! has an inventory database called OpsDB. This DB has information about each and every compute resources at yahoo. So each and every OpsDB entry can have a host name, macros of that machine, even it has information about in which rack, which data center this machine is on, and which switch this particular server is connected to. So we, to import nodes, we basically leveraged our internal OpsDB, imported this node, created this ironic node create node using the information from OpsDB. Once the ironic node is created, we also set the driver to initially to fake Pixy. We do not directly use the Pixy IPMI tool driver, so we switch to fake Pixy first. And then we also know the IP address, the existing IP address and the macros of the machine. So we can also create a Neutron port. So create a Neutron port as well.  As the name implies, it creates a NOVA instance and associates this instance with the ironic. So NOVA instance have a UID and to associate something with ironic, the instance UID will also show up in ironic. So this is similar to the new adoption state in ironic. So ironic supports this new thing called adopting. So you can put a node in from end roll to manageable and to adoption. And then without ever touching the node, you can turn the node into active state. So the similar thing happened here. This is because these machines are serving traffic. We do not want to reboot them. We do not want to like remage them because it will affect production. So once everything is successful, we clean up all the metadata about images. Once the instance become active, we switch to the pixie ipmi tool driver. So if a customer, let's say, belongs to yahoo mail and now we fake booted a bunch of yahoo mail nodes into ironic. So when he uses OpenStack, he will access the OpenStack APA through NOVA. So the customers do not directly have access to ironic APA since ironic is an admin only API. So when the customer does NOVA list with the mail tenant, they will see the new ironic nodes imported and running. They can now do rebuilds, delete, do whatever they want with the nodes. So this is how we imported existing nodes that are running in production to ironic. Let's talk about ironic service itself. We have ironic service, ironic API, running behind Apache servers. So the three APA servers initially we saw, all these three APA services run ironic APIs behind Apache. We also have two ironic conductors. We initially started with just two conductors in two separate machines. These machines have something around 24 gigs of RAM and 24 core CPUs. So what could possibly go wrong? That did not happen. Similar to something happened. When we imported around 10,000 nodes, so we were ramping up adding new nodes into ironic and booting them. The ironic boot started to fail. We also saw the ironic conductor had high CPU usage. So ironic conductor is a conductor process. We saw the top, it was always at like 90, 90 to 100% CPU. And our service engineers complained that it took so long for the ironic API to respond. Obviously, ironic conductor is busy. So any conductor operations like ironic node set power state, node set maintenance, all these things took a long time. So they were complaining. So first two address is the high CPU usage. We wanted to know why ironic conductor was basically taking high, consuming a lot of CPU. Enter the sync power state. We can also call the, call it a DOS, DOS API for ironic. If you want a DOS, ironic API, you can trigger sync power state. So what sync power state does is it basically, if you have pixie IPMA tool driver, it's going to use IPMA tool, get the status of the ironic node, the power status. So the ironic API also has a power status field. This field has the current power status that should be the node on. So a customer can set ironic node, set power state, power on. And the power status will be power on. So sync status runs, it's a periodic task. It runs once in a while and checks the current status of the node and syncs those. The sync is from DB to the actual node. So for example, if somebody powered off the machine, the machine should be powered on, it's he or she powered off the machine outside ironic. The sync power state will come back and power on the machine again. It's also a good, it's also a good periodic task to have because if your IPMI BMC fails, sync power state will eventually find that this particular machine is not accessible through IPMI. So we cannot eliminate sync power state because we will not know the status of our BMCs, how things run in data center. So sync power state, why it took a long time. So the default interval for sync power state is around some, initially we had around like an hour. So every hour this periodic task will run and sync the power state from DB to the node. So at 10,000 nodes in a cluster, so this is like initial time, you know, like almost one year ago, when we had 10,000 nodes in the cluster, the conductor was busy just doing IPMI tool commands. So it was just forking IPMI tool and checking the power statuses. So obviously we can reduce the periodic task interval. So we reduced it to somewhere around every 24 hours sync power state, run sync power state. We also noticed that in a data center, if your server runs for a long time, BMC could fail. The firmware in those BMCs leak memory. So whenever you say do IPMI tool command after 2,000 days of uptime and your BMC will say, okay, leak memory and I cannot respond to you. So it took longer than expected. So whenever we ran the IPMI tool command, instead of immediately sending the power status, the IPMI tool command and the BMC will return the status very late. So we cannot do anything about that other than resetting the BMC or working with the hardware vendors to fix it. And it's a hard to reproduce problem as well because this does not happen. The BMC failure does not happen often. It takes long time to reproduce these problems. And I do not really know why we pay $15,000 for a machine that fails like this. So second approach is to increase the number of conductors. I said we started with two conductors. And obviously that's so the conductors use a data structure, this is called hash ring. So the nodes are divided between, so let's say 10,000 nodes, maybe 5,000 is handled by one conductor and another 5,000 is handled by another conductor, which is obviously very low. So we wanted to increase the number of ironic conductors. So one solution is to run multiple conductors on the same host. This is tricky because how the ironic conductor is designed. Ironic conductor, whenever it comes up, it automatically fetches the host name of the machine and uses it as an identity. So we, as I said, we only had two servers and these two servers had 24 cores of CPU. So we have 48 cores of CPU and Python processors normally are bound to one CPU. We weren't using the CPU much. So we wanted to spawn multiple conductors on the same host. So we added a small patch. It's almost like an APO worker patch, which will spawn a number of conductors. So the problem with running multiple conductors on the same host is there could be some race conditions. There is another periodic task called sync local state in ironic conductor. This sync local state, what it does is, if one conductor dies, it checks whether it needs to take over the nodes that were managed by the conductor that died. And that is a good thing, right? So if one of the servers is down, we need to basically move other conductors to manage the service. But that could be some race conditions if you're running multiple conductors on the same host. So we haven't tested that. So we disabled sync local state. So we just make sure that ironic conductors are up and running all the time. We have better monitoring on ironic conductors. We are running right now around almost 40 conductors per host. So we have, like, 80 conductors in one cluster managing things. And obviously our SE team, site operations team were happy because they get results faster from the ironic API. Second, Neutron. To give you a, let me first give a background about our Neutron setup. So again, we had three API servers. All these three API servers were running Neutron API. We had 24 API and RPC workers. And we have four Neutron DACP agents. So these are the ones that actually serve the DACP. And all these Neutron subnet and network are managed by all agents. So which means any new network on the Neutron side, the notification goes to all four agents. So we have some kind of HA. So we replaced, so that by default Neutron comes with the driver for DNS mask. So we wrote our own driver for ISC, DACPD. So this is our Neutron setup. So to talk details about scaling up Neutron, I need to talk about sync local state and the issues with sync local state. Sorry, sync state. Whenever a Neutron agent, DACP agent restarts or when it dies, a new network could be added to the Neutron. So somebody could create a Neutron subnet create and your agent could be down. It will never receive that notification. So when it comes back up, it needs to know that this new network was added to Neutron and create and do specific action on the DACP side. For example, if a network, if a subnet is added and it comes back up, it needs to create that subnet entry in DACPD.conf. Your agent is basically talks to the DACPD to do things. So what sync state does is it gets all the network info whenever the Neutron DACP agent is restarted, gets all the network info, goes through every network, every subnet, every port and recreates all the things it needs to do. So it writes to the DACPD.conf, writes to the lease file. So this happens every time you restart Neutron DACP agent, which means every time you do a new deployment, you need to restart DACP agent and this needs to be done. So we had two drivers to interact with the DACPD itself. So Neutron DACP agent talks to DACPD and it talks to DACPD using a driver called OMShell driver and we also had a driver called PyPuraMapy driver. Let me talk about OMShell driver and let's see what it is. OMShell is a command, is a shell command. You can basically run that OMShell and you can connect to your DACP server which is running on the local host on port server 911. It also has a hash MAC based authentication key so that other users cannot log into a DACP server and do bad things. You can search by MAC address in your lease file, delete the MAC addresses, create new leases, all these things can be done through OMShell and OMAPY. So OMAPY driver, so OMShell interacts with the OMAPY drivers in the OMAPY API in the DACP side. So as you can see, this is basically spawning shell and basically process.popen and OS.communicate and write directly into that process. So when we had the OMShell driver, this is how our CPU utilization on Neutron DACP agent, this is during sync state. So whenever we restarted it, the Neutron DACP agent, this happened. So the green line here is the CPU system. So whenever the CPU was executing in the kernel space, the red line here which is at the very bottom is the CPU usage on the user space code when CPU was executing user space code on Neutron DACP agent. Since the OMShell is a process, we needed to fork a lot of these process and write to it. We had seven threads doing, you know, seven forks and syncing various things. As you can see, the CPU usage went up. The x-axis here is the duration and the y-axis is the load of the system. The load of the system went up especially and it was spending too much time on the kernel space. Also, you can see that the bottom line, it's 3,500. So basically, it ran for an hour. Actually, it ran for several hours. This was with 2,500 subnets and 45,000 nodes. So you have like 2,500 subnets to create and 45,000 ports to create. It was very slow. And all these hours, the usage was high. So if somebody booted a machine in this time, it would fail because it was busy doing this thing. It might not get the agent notification or it will take some time to process the notification. So this is bad. And then we discovered this new thing called pure Python implementation of OMAPI. It's a library. And this is how the CPU load and duration went down. So again, see the red, sorry, wrong button. So the red line on top is the, again, the user space, CPU usage. The green line is the kernel space. The x-axis is the duration of the sync state. And y-axis is the load. As you can see, this number is 600. So for 45,000 ports, it took almost 10 minutes instead of several hours. So the model of the story is never ever fork processes and write, you know, do things, to do things. Instead, if there is a pure Python implementation, use that one. So where do we go from here? So as I said, with OMAPI API, you can write things to the DSCP lease file. But whenever you want to add a new subnet, you need to write to the dgcpd.conf file. Whenever you modify the dgcpd.conf file, we need to restart the dgcp server. And this is not ideal. So we had a VIP. Actually, we have a VIP between dgcpd and rest of the infrastructure. So whenever the dgcpd restarted, VIP thought the dgcpd service was down. And it would not send packets to the dgcpd server for a minute. Now, if you look at pixie, the pixie actually tries to do multiple dgcpd requests.  But VIP wouldn't send any packets back to dgcpd, because it thought dgcpd was down. We were actually not serving dgcp to the machines that were booting. That's one of the reasons for boot failures. Another good approach is to move to the KEA dgcpd server. So this is a new dgcp server that is written for high availability by ISC. So any changes to here, any changes to dgcpd.conf doesn't need any restarts. And it has a nice JSON API instead of some OM API, which you need to process some binaries. And fourth, let's talk about density test. So before we onboarded a lot of machines to Ironic, we did something called a scale test. And so basically what we did was we booted like 100 machines. We'd know a booted 100 machines and see how the system performs, whether it can do 100 parallel boot. It worked well. And then we also did a density test, which means we added a lot of machines to the OpenStack system. We imported around like 50,000 nodes into Ironic and see how the system performed. At 24,000 nodes, when we were importing 24,000 nodes, the APS server started swapping. So this is how things went down. So on the top is the memory usage, the bottom is the swap usage. So the top, the orange or saffron, is the used memory. The used memory went up over time. And also you can see the swap usage. The red is the swap usage. Green means no swap usage. The swap usage went up as well. All happened on week 13. 13 is a bad number. It's really true. So we had 24. This is our staging environment. We were doing the density test. We had 24 gigs of RAM. Obviously the best way to solve this is to increase the amount of RAM in that machine. And who's the biggest user of memory is Neutron. So Neutron was consuming around 1.4 gigs. And we had 24 APA workers, which means it was just eating a lot of RAM. So obviously the solution, so this was at 2400 subnets and 43,000 ports. And the easy fix to basically reduce the amount of APA workers and RPC workers. So we reduced it from 24 to 10. So instead of taking a lot of memory, it would take 10 gigs of memory. Another long-term fix we did was to basically, we still have investigated why it was taking so much memory usage. But we want to isolate Neutron to a separate server so that it does not adversely affect other APAs running on the APA servers. I think the Neutron is still, it's causing problems in upstream as well, especially with the gate. Their solution is to also reduce the amount of APA workers, which is not a scalable solution. So what we learned from running Ironic at scale. So do both a density test and scale test before onboarding new machines. So scale test is, by scale test, what I mean is we basically booted 100 machines and thought, oh, 100 machine booted up, came up, everything is fine. And we started onboarding machines and saw issues. So let's say you want to add 50,000 nodes into your Ironic cluster. Do a density test with 50,000 nodes and figure out what happens. This is like a one-on-one. And if you're using custom code or anything, avoid using, avoid spawning processes. So if you want to do a task, especially the periodic task, if you want to do a thing, never spawn processes. Use native Python libraries as much as possible. Also pay attention to periodic tasks. The default interval for sync power state was something around 60 seconds. It does not scale, it does not do well for a 10,000 node cluster or 50,000 node cluster. So pay attention to periodic tasks and all those configuration files. And be prepared to scale horizontally. Your database could swap. Your APA nodes could end up swapping because of high memory usage. So always have these additional machines in the subnet so that you can quickly scale up your infrastructure and the APA clusters. And pay attention to the number of APA conductors, APA workers, and RPC workers. And always don't forget to have one. Any questions? Yes. So we assumed there was memory leaks. So these machines, we looked at a lot of these machines and the uptime was several hours. The machines were never rebooted and we had to reboot them during the dirty call. When we tried to reboot them with IPMI, these machines would take several seconds to respond. So we assumed we are working with the vendor to figure out what the problem is. But we assumed it's a memory leak or the BMC just crashed. Investigate on it. Yeah. So the only thing we could do was do a MC reset code, basically do IPMI tool command MC reset code and pray to God that it works. And otherwise somebody has to basically go there and pull the plug and back it in. It's bad. We are also managing the same thing with basically we have this active process that scans the machines in data center. And before the customer even reboots the machine, we know that this machine has a problem and we raise tickets for the data center operation folks to go and fix it. So that is one way of coping with that problem. Thank you. Can you talk a little bit how you set up this and the problems that you found about scaling and reliability? For example, all Nova computes you have for this kind of infrastructure that you have? Yes. So the question is about Nova Compute and how it scales. Yes. Obviously, in the upstream code, you have the resource tracker, which basically runs and tracks all the resources. So we implemented something like claims API and entirely eliminated resource tracker. This was based on a older spec that was proposed to address that problem. The new way to fix Nova Compute is to basically run multiple Nova computes that is supported as well. So Nova Compute now supports that same hashing that irony conductor supported. That is one thing to look at. So we eliminated resource tracker. So the Nova Compute usage was very less. And we had two keepers behind Nova Compute. So whenever one dies, another comes up through the leader election. That is how we solved it. Okay. Thank you. Thank you. Hi. I think you are talking about the scale. When you have more machine learning there with the Neutron DHCP agents, have you ever encountered the problem that the DHCP, the address action has never been refreshed so that the VMU creator actually has a problem getting the IP address? Right. When you have more and more machine machines get created? Yes. So in ironic, we are not talking about virtual machines. First, we are talking about bare metal actual server hardware. First, yes. So using that OM shell driver, basically we are spawning multiple OM shell processes. And spawning, forking a new process takes time. If the system was busy doing a lot of that stuff, a boot comes in, a new port gets created. It could take some time for the port to be actually reflected back on the DHCP server. Right? Because it was doing other things like the sync state. On that case, we have seen the machine already got powered on, and it started doing pixie boot and never received DHCP. That is one. Another thing we have seen is through IP helpers. So sometimes the IP helpers were never on the switch. Second one. And third one is basically that HA. So you need to tell Neutron to send DHCP notifications to all the agents. There's a setting in Neutron.com to send how many DHCP agents you want to send the notification about ports and networks. That is another thing to look at as well. The root cause is basically OM shell and forking a lot of processes. And then we move to the native Python implementation. And things started to improve there. Are you running on V6 or V4 or mixed network for the Neutron site? And then also based on your experience, what do you think in terms of footprint that you can control with Ironic, how far does it scale on a robust single deployment cycle? Yes. So first question is whether we support V4 and V6. So we support V4 and V6, yes. Neutron ports can have V6 and V4 IP addresses, but we only support internally V4 DHCP. So there is no V6 DHCP yet. And then what is the second question again? So based on your experience, what's your projection in terms of the current rate that you can scale robustly before you have to scale out a complete style of horizontally? What's that footprint look like? Somewhere around 10,000 to 15,000 nodes. Even then we had problems. But to scale, we want to have somewhere around 70 to 100k nodes in one cluster. We are going there. We are getting there. And it's interesting. Yes. Thank you. Thank you. Any other questions? The next conference is ready. Thank you.


from the CUBE studios in Palo Alto and Boston. It's theCUBE, covering IBM Think. Brought to you by IBM. Welcome back everybody. This is Dave Vellante of theCUBE and you're watching our continuous coverage of IBM Think, 2020, the digital version of Think. Deb Bub is here. She's the HR VP and Chief Leadership, Learning and Inclusion Officer at IBM. Deb, good to see you. Great to see you as well. Thanks for having me. You're very welcome. While we're in the same region of New England, wish we were face-to-face at Moscone, but we're doing the best we can, right? Absolutely, absolutely. So I got to ask you, so one of your roles is you're responsible for executive leadership succession. So I remember I was in a lobby hotel in Barcelona when I heard that Arvin Krishna was taking over as the CEO of IBM. And I sat there and wrote a blog, tapped out a blog on my mobile phone now. But a little did you know, and he know at that time, and we had a glimpse of what was coming, but I don't think we really fully understood. And so I'm wondering, how do you prepare for that type of succession? Well, I think our leaders now are all encountering unexpected circumstances where we have big plans and big actions we've planned, but the current context is asking us to rethink them in all kinds of ways. So of course, IBM is the kind of company who had a very well thought through kind of world-class succession process, but none of us thought that we would be integrating Arvin and launching him into his new role as the CEO, working from home. So we had to do what every leader at IBM is doing right now, which is starting from a position of resilience, taking a deep breath, thinking through what's really happening to me, to my work, to my situation right now. A lot of us are working from home. A lot of us are adjusting to physical distancing. There are many leaders here who are deeply worried about their families, their lives, their situation. And so starting from a position of personal resilience, making sure we put our own oxygen mask on so we can think clearly and make the decisions was an important first step. Second, focus on empathy. Leaders across IBM right now are really focused on making sure they understand the situation people are in, that they understand the physical, emotional, mental health and needs and requirements of every IBMer. So they can make really good decisions about priorities. And then it's time to focus on what's mission critical, what's urgent to compartmentalize and relentlessly prioritize so we can all be successful. All of those lessons applied to succession like they do to every other kind of work and led us to reimagine and create really interesting digital intimacy opportunities to connect Arvin with every IBM around the world through new kinds of social channels. And overall, I think it's been a really incredible experience. Yeah, yoga breaths are a good thing at this time, aren't they? We're all taking a lot of deep breaths for sure. Right, no doubt.  And so you were probably a little bit ahead of other US-based companies. Is that fair? Well, we certainly are a global company. And so the idea that everyone is going through this in the same way at the same time is just not accurate. We have people all over the world. And I think we did have early lessons from our colleagues in China who are incredibly resilient, who showed us the way with great social distancing discipline and really working hard together to help each other be successful and challenging times. And we've learned that in every community around the world that's been impacted. And I think that's been one of the most surprising and amazing things about this whole experience is the way we've been able to leverage digital technologies at scale to connect with one another, learn from one another and support each other through a very, very challenging experience. So Deb, you've got inclusion in your title. And so this is a relatively new thing. I wonder if you could address sort of what that means for you to IBM and why is it so important right now? Well, inclusion is sort of the core of what makes it possible for us to benefit from each other's incredible talents. I like to say, diversity is important to make sure you have the right people at the table, but inclusion is how you turn that talent that's at the table into magic. Inclusion is what allows every one of us to bring our uniqueness to the table and to contribute. And it couldn't be more important than right now. Inclusion is the most important ingredient to helping people thrive in difficult times. It allows team members to quickly orient to new ways of being together. An inclusive leader is able to manage in a digitally distributed environment and create a new context for people to connect with one another. Ask the right questions to allow team members to manage the competing priorities of homeschooling, working, living, all in the same environment. So inclusive leaders really create a context for each other's contribution and success. You'll hear again and again in the description of how IBM leaders are thriving in this time, how we're stepping up and stepping in for our IBMers, our communities and our clients and finding ways to include, learn, take the best insights and accelerate productivity and the right solutions in this challenging environment. Inclusion is one of IBM's most asset right now. Well, you mentioned that you're kind of connecting Arvin digitally with the broader IBM community. So that's kind of interesting, right? I mean, leading digitally, he has no choice. Other than he's not the only leader at IBM obviously, he's the top leader, but there are many, many leaders at IBM. So how is this sort of, we're talking today through the cubes digital platform, how is this digital revolution really affecting people's ability to lead? How are they stepping up to that challenge? Sure, well, IBM like all our clients have been on a journey of digital transformation for the last several years, but this is really putting it to test in a very different way. It's presenting new challenges and new opportunities. The opportunities are incredible, new tools, like we're using, you know, WebEx and Trello and Slack and Mural and all based in the, you know, the IBM cloud really enabling full digital collaboration at a whole different scale than ever before and leveraging new kinds of leadership insights and new kinds of leadership mindsets to benefit from all that great ability to collaborate asynchronously, to create digitally distributed creative conversations. And then as leaders, knowing how to harness all that creativity and provide the right context for people to share, to move product quickly, you know, be more agile in our production of outcomes and solutions. That's right at home in my group, for example, we're creating new digital communities and coming up with new solutions with our, you know, inclusion communities, new solutions with our teams to help enable leadership and new learning solutions all over the company. It also, you know, working digitally presents some new challenges, trying to figure out how to help people balance the challenges of being at home and things that we might have relied on face-to-face contact for to create different levels of trust and interactivity, learn new skills, et cetera. Some leaders have sort of recognizing some of those challenges gotten together and, you know, taking a work from home pledge, you know, helping each other figure out and co-create with parents who are working at home how to navigate this new digital, totally distributed remote work situation we're in or, you know, figuring out how to teach each other how to use new tools. So I think, you know, if I were going to give advice to any leader now, I would say it's a good time to assess your digital presence and your digital savviness and then think about how you're showing up in these digital forums. Are you trying to do things in the same way you were doing them, just doing them online? Or have you really rethought your digital presence and are you really using that environment to create the maximum context of creativity and inclusion you can for your team?  IT practitioners are putting out fires and, you know, in some industries, you know, just trying to keep the companies alive. And I joked it's kind of what they need is some adult supervision. And what do you see as IBM's role in this sort of during this crisis and maybe even post this crisis? How would you define that? Absolutely. So look, IBM is a trusted partner to the companies of the world who are facing the same challenge we're facing and trying to digitally transform themselves and thrive as the world continues to grow and change. And certainly this current context puts the whole thing in a different relief. But fundamentally, IBM is the most important technology company in the world because we have the technology that industry expertise and the position of trust with our clients, they don't need, what they need from IBM is not us selling them something, but they need our partnership to imagine themselves in the future, reinvent themselves toward that future to thrive during this incredible challenge and maintain business continuity while they become who they're going to be in the next era. So, you know, it's a challenge for all of us. We are a huge global company in 173 countries and 350,000 people uniquely positioned to help. We have incredible technology. We have the call for code with our developers all over the world helping to solve these issues. We have many ways in which IBM is positioned socially to make a difference in helping with skill acquisition, super compute capacity. I mean, in many, many ways that we can help as a business. But closer to home, we are also able to help companies imagine how they can emerge stronger by reinventing their, digitally reinventing their business processes and their leadership and talent cultures for how they can thrive in the new era.   Certainly people are more willing to look at the cloud. You know, this whole work from home infrastructure seems to be for something that has legs. Do you think inclusion is going to be one of these things that gets accelerated as a result of this pandemic? I absolutely do. I mean, I think we have lived in an era where this kind of concept was sort of nice to have or viewed as important, but maybe not essential. I think that's really being transformed by the current environment and people are expecting their companies to provide a context that is psychologically safe, inclusive and help some do their best work when it matters most. Those are the companies that are going to emerge from this challenge stronger. And so IBM's culture, I mean, last year, we talked a lot at Think about the compelling call to action to be equal. That comes from IBM's deep commitment to diversity and inclusion. And in every era, challenging ourselves to do more to create a context of full inclusion and equality. Well, this year, we're expanding that concept to include all forms of equality. We started with gender equality. Now we're looking at full inclusion for all. And in this circumstance, it could not be more important. And so I do think, you know, you said it well, you know, there are all kinds of capabilities that will be transformed and scaled as a result of this. Our technology environment will be different. Our commitment to infrastructure, working from home, lots of things will be different. I think one of them is a call to action for leaders to be more inclusive and to create the context where everyone can be equal. Well, I think it's important that companies like IBM lead in this regard.  They may not have the resources, they may not have the network. And so, you know, setting the example as IBM is very important. Debbub, thank you so much for coming on theCUBE and you know, sharing your philosophy, IBM's philosophy and your best practice, et cetera, with us and theCUBE. Appreciate it. Thanks so much for having me. Be safe and be well. Yeah, back at you. You too. And thank you for watching everybody. This is Dave Vellante for theCUBE's continuous coverage of IBM Think 2020, the digital thing. Keep it right there. We'll be right back.


The main campaign of our movement is called create safe harbors and in this campaign the movement expresses its solidarity with all the refugees who had to flee their home countries. For any reason it doesn't matter for us. To us there are no migrants. There are people who used to live let's say in Aleppo and now they are Berliners. So as a human rights movement we are convinced that all you need to know is that humans are tortured and killed in the Libyan detention camps to trigger political action against it. Their origin or background of any sort is absolutely irrelevant in this situation. Politicians could end their suffering in the Libyan camps or the tragedy in the terrain you see overnight or tomorrow or the day after tomorrow if they want to. So organize, organize initiatives, groups, build campaigns or solidarity events.  He has a project started with a small group of dedicated activists and volunteers who took a stand for the first time in their lives.


Hi everyone, happy Monday. I'm Joe for jaspyscasebreaks.com coming up with jaspys five box big hit baseball mixer featuring a couple boxes of 2022 tier one nice couple boxes of 2022 Bowman sterling and a box of 2022 tops sterling to pick your team mixer and Big thanks to this group for making it happen Here on a Monday. Thanks for spending a bit of your MLK day with us Dilo Daniel going big on this one Thank you Good luck to him. Good luck to everybody who got teams for your shade. No filler either another five box break loaded up on jaspyscasebreaks.com if you want to run this back tonight Check it out. All right. We'll start with the tier ones Then we'll do the bowman sterling and they'll close with the tops sterling All of these boxes with big hit potential here a little prospecting with the bowman And who knows what we'll get out of that top sterling Let me put this on mute while I snap this open Maths asking you about what about spectra football? Yeah, I'm down to do that If you want to fill that last filler We've got time to do that tonight if you want to Got plenty of time for everything that's available on jaspyscasebreaks.com except for the new releases obviously 384 out of 399 it's a piece of Buster Posey's Jersey It's gonna go to Daniel and the Giants We got Reese Nair 159 out of 299 for the Friars And it's gonna be for Daniel and then we got Brooks Robinson 21 out of 75 That's for Daniel and the Orioles a personal box for Daniel He's heating up. He's on fire Next box says sold out with the filler on your Unless someone just bought it out No, it says last filler 3x spectra R&B spots given away Prism blaster box 14 spots left. All right game use material Garrett Cole 300 out of 399 Tristan of the Yankees 51 out of 299 Ian Anderson tier one talent auto for Daniel and the Braves and Whoa Wow, I Told you there could be monsters in here Mario on a Rivera Derek Jeter dual auto five out of ten and that is Tristan You're welcome man Tristan who bought the Yankees straight up Nice. All right, but wait, there's more now. We got two boxes of bowman sterling Well some bit of prospecting here There you go Tristan staying up late on the East Coast for this you were rewarded this time. Thanks for staying up There you go, man To two of your favorite players of all time all on one card Congrats very nice Yeah, I mean May as well get a super factor now at this point. All right, so this is bowman. So we're more prospecting here See what we got. There's milcar Perez for Tristan and Seattle, so I've got three out of three cards behind All right beyond Coulson Montgomery is Harry Ford another mariner for Seattle Another one for Tristan Speckle parallel kind of hard to see the numbering right there, but it's right there near his hands to 99 He's heating up he's on fire and we got Hyro Pimares For my rivals the Giants D Lo Daniel with the Giants redemption Two autos. There's a Brian Acunia For the twins Ronald's brother, that's Eric with the twins nice and a bonus autograph It's itty cap prospect auto for the Marlins. That's gonna go to Daniel extra auto. I'll take it there's a Julio Rodriguez rookie card and Armando Cruz for D Lo and the Nats And Tristan with the Julio Rodriguez rookie card All right another box of Bowman's turn We've got another one of these pick your teams in the store right now jaskiescasebreaks.com Daniel reminding us that Sterling also grades pretty well for whatever that's worth. All right. Good luck. Wow Tristan is from Queens Which is Mets country his whole family's Mets fans, but you're the you're the Lone Yankee fan How did that happen? It's just just rebellion. Well, first of all, we got a O'Neal Cruz rookie card Up here for the Pirates. That's gonna be for Jeremy. We've got out of 199 Jose Ramos for the Dodgers and Mycel Urbina for the twins Eric Leal, Minnesota. You got some color back there There's a wander Franco up front here That's for Daniel and the Rays and there's Marcelo Meyer 22 out of 75 Red Sox at P with Boston Nice So Tristan saying you've just grown up watching your dad screaming crying at the TV watching the Mets And meanwhile the Yankees were winning titles. So Your dad's a Mets jets fan. That is rough. All right, speaking of Mets is Francisco Alvarez up front And a Wilman Diaz For the Dodgers that'll be for Daniel and the Dodgers Got a Marco Luciano for the Giants to 199 and an Errol Vera For the Angels that's from Mark and the Halos. I got Colton Welker to 150 Hard to see the number there, but it's up there and Jordan Loller nice Darren McKenzie DMACC with the snakes He's a cold hearted snake Look into his eyes Nice. All right now 2022 tops sterling Eric. What's going on? Just in time to see some 2022 tops sterling Good luck everybody. All right. We got 90s all-stars and Silver slugger award winners Go 90s all-stars first. All right 90s all-stars it is Wade Boggs out of five Quad relic on-card auto Teddy Jaspie would love this one He's fit perfectly in a 180 top loader a couple pieces of his lumber a couple pieces of his jersey Three out of five Bogsie. What a great hitter This is Boston Boggs For Ed P. Ed All aboard the Big Hit Express All right now Silver slugger Dual relic dual autograph. Is it the same team? It is Wow Chipper Jones, Ronald Acuna, Jr. That's numbered one out of 15. That's for Daniel and the Braves No, random. I love that sliding around a little bit there, but still really cool might be able to nudge that over But real nice There you go gang I'm Joe for Jaspie's case breaks calm and that was our five Boggs big hit baseball mixer to pick your team mixer Version number two in the store right now. I'm Joe. I'll see you next time for that one. Bye. Bye


4,000 internally displaced persons leaving in Kudenza, Maribain, Rideau, IDPs camp in Chican-Locogam and area of Kaduna State has received food, non-food items and relief materials to children, women, Rideau and aged parents to cushion their challenges of lack basic necessity of life facing in the camp. Plus TV correspondent Habila Daroufai was there and filed in this report as presented from our studio. The displaced persons from 20 communities are children, women, widows and aged parents seeking refuge following bandits' attacks on their communities. Most of the affected persons have fled their ancestral homes to the urban centers to seek refuge without means of livelihood. A 13 year old child in the camp narrated how a mother usually puts salt in water for her and her siblings to drink in order to prevent them from contracting diseases. Because of no money to go to school, my mom used to help us very well. We are ten in our family, in our mom's children we are ten, but in our dad we are more than ten. My mom in Appu Falls saw it in the water so that we would take them and sleep. We do not have food to eat and I cannot sleep like that. The place is not fine. Some people, some give us small money so that we can use and buy something that we can eat for a day, some we don't get. Now I started, I was in private school but since when it started my parents don't give money to pay them. They decided to remove me from the private. Speaking, the coordinator of justice development and peace characters, Robin Father Joshua Ache says donation of the relief materials is to show love to the IDPs and empower them. It's about 20 communities in this place. About 4,000 of them, you know, settling here in Melbourne, Rideau, from different communities. And for quite some time they've been here, you know, some of them having to feed from hand to mouth. Some of them crying in there, some of them not having even places to lay their heads. So we have just come, you know, to be our brother's brother and our sister's sister, to see how we are able to gift them these items we have brought so that we can cushion the challenges that they face. Also, the leader of EcoSmile Support and Empowerment Initiative, Blessing Sunday, says the IDPs are in dire need of basic amenities to live a better life as one of the beneficiaries lords the organization's kind gesture. We want them to be self-reliant, to be able to stand on their feet. That is why this training is going to commence next week or so so that we can see how we empower them. We train them on different skills, different vocational skills. They need medical care. They need some of these children are not going to school. They bring these things for us. We so feel happy too. We don't have food. Three, we don't have house. Four, we don't even have house. We have the general brothers that I get that are there for village, the children. So I don't have, I don't have general brothers now. I need government to help us so that we can go back to our village. The victims who are taking refuge in IDP camps for more than two years remain hopeful of returning to their ancestral homes one day if the government finds a lasting solution to insecurity. Thank you.


So, students last lecture may be, we have read about production function. So, if you recall, we have read that production function is basically a relationship among output and the factor of production. So, this is the mathematical expression, which relates the amount of output with the amount of inputs. So, in this y is the output, real output produced in a given time period and a is the number which measures the overall productivity. So, a represents the state of technology, the state of knowledge, efficiency, the factors of production, how effectively they are being used. And k is the capital stock, our quantity of capital used in that period and similarly n is the number of workers employed in that period. So, f, f is the symbol used to show the functional relationship between factors of production and output. Students technology a, which represents the state of knowledge go, technology go, effectiveness go, how efficiently effectively factors of production are being translated into output. So, this a, which is production function may different forms may have been introduced. For example, first form is this one, which is called as a neutral technological progress. This means that any improvement in technology will affect the marginal products of labour and capital in the same way. For example, if technology 10 percent, if there is an improvement in it, then marginal product of labour, b 10 percent will increase and marginal product of capital b 10 percent. So, this affects both of them in the same way. That is why we say that this is a neutral introduction of technology. The other is capital augmenting. In this, we will see that we are multiplying a with k. So, multiplicative form a into k. So, this means that any improvement in technology will affect only the marginal product of capital and labour will not be affected by this. For example, if technology 10 percent changes, then this marginal product of capital will just increase, marginal product of labour will not increase. So, in this context, we always say that capital augmenting technological progress. The other form is labour augmenting. So, you will see that in this, we have multiplied technology a with n. So, any improvement in technology will affect only the marginal product of labour. Capital will not affect this. So, I am recapping these three differences. That technology can be introduced in three types of production functions. The first one is the neutral way. The second is capital augmenting. And the third one is the labour augmenting. And I have explained this in the previous lecture. So, we will see that this is a marginal product of labour. Capital augmenting and the third one is the labour augmenting. And I have explained that neutral means that in technology, improvement will affect both of them in one way. Capital augmenting will only affect the marginal product of capital and labour augmenting will only affect labour. Another dimension which we can classify production function. You read this in microeconomics. Long-run or short-run production function. So, in long-run production function, all factors of production are variable. So, a can change, k can be changed, and n can be changed. So, we will use this. For example, if we want to discuss returns to scale, return to scale means that if you double all factors of production, then how much output will change? If we want to discuss this, then we will use this kind of long-run production function. Similarly, we will study growth theories and we will use long-run production function. And the second way in which we describe this is short-run production function. In short-run production function, for example, we put k over bar. This means that we are keeping this k constant, n can be changed, and a also assumed in the short-run constant. So, technology is not changing. We have fixed capital stock just by changing the value of n. We are seeing how output will change. So, this short-run production function. We will use this specifically in business cycle theories. We will use short-run production function chain or laugh variable proportion or factor productivity for short-run production function. Thank you very much.


Yeah, you're talking about Troisi and his creativity, you know, you could almost say the conductor in midfield, but it's difficult if the band is not going to play as Nisbet slipping out wide to Miller. And now he's Goddard! Finally! The Central Coast Mariners get one on target and Cy Goddard with his first goal in the A-lead men's. So simple, wasn't it? The Harvey Norman replay as they drive forward. It's played out wide and it's just deflected inside Goddard. Just waltzed in at the back post. Stretching on that left foot up underneath the bar, down into the goal. Nisbet assessing the situation, Miller always looking to get forward and that's a thunderous finish and there's nothing that Meijers can do about that and it's one nil Mariners. Troisi, this is better. Petratos, Petratos opens up the space finally and Biragini made himself big and that is the sign of a great goalkeeper called upon at a crucial moment and he comes up with an important save. Pitches the ball off Petratos. Nisbet looks as though it's go to the corner option for the Mariners and Gwent maybe not. Nisbet, he should appear. Well it is now. It took a while and this could be the final chance of what has been a frustrating afternoon for Western Sydney Wanderers. Billy just fell over on the first opportunity, got back up. So it is the captain! Central Coast Mariners, Oli Buzanik delivers once again.  I have to be Oli Buzanik. The leader of this team stood over the ball said everyone get away and we talk about the final nail in the coffin. Well wham! There it is. Full time in Gosford. Central Coast Mariners to Western Sydney Wanderers nil.