What is going on everybody? Astos here. Welcome back to another video. So in this video, we're going to be doing an overall market update, taking a look at the Dow Jones, the S&P 500 and the NASDAQ. We're going to be talking about an overall trading update as well. What stocks am I currently trading? We're going to be talking about that as well as taking a look at some earnings plays as earnings season is starting to unfold here in the stock market. Where are we going in terms of the markets? Where could we potentially be headed now that earnings is kicking off? So before we do get into those topics for everybody out there that finds value in these videos, you enjoy the content, all I ask you to do guys, the only thing I ask you to do is go down below and hit that like button. It really supports me and supports the channel in general. And if you're new to the channel, there are two links down below, one of them being the Discord group chat and the other one being the Facebook group, both of those being 100% free of charge. And I guarantee you guys, you'll find value if you do end up joining those communities. So today, it wasn't much of an eventful day in the stock market, right? The SPX, the 500 largest publicly traded US companies ended up closing the day literally at 0%. Up 11 cents at the close, not much movement at all here on the SPX. The Dow Jones industrial average ended up closing the day down about 14 points, down about 0.05%. Not much movement there at all in terms of the Dow. And the NASDAQ ended up closing the day a bit more red than the other indexes down nearly 0.3%. But still not crazy movement only down about 21 points on the day. So in terms of technical analysis here, in terms of a technical breakdown, there's honestly not much difference from what we talked about in yesterday's video, right? And for those of you all that didn't watch yesterday's video, let me just do a quick little rundown of what we were talking about, right? We really were trading above the 2890 level of support over the past couple of trading days, really during this time period of about 1-2 trading days. Now, we ended up breaking that level of support, making it a level of resistance at about 2890. And since then, we've really been trading between 2880 to 2890 and between this little horizontal pattern that we see here, right? This 10-point gap is where we've been trading and it's just been a lot of slow movement over the past couple of days, really just some consolidation, a lot of just consolidation. And we can see here exactly what I'm talking about, right? Pretty much over the past three trading days, we've been trading between these two trend lines, which again is just a 10-point gap in the S&P 500 here. So we can see again today, we popped up above that level, we tried holding it as a support again like we did a couple of days ago, but we failed as the S&P really tanked about nearly 10-11 points here at about 1140 AM Eastern Standard Time. And from there, we're really having difficulties breaking out of that level again to maintain it as a new support. So now it's just treating it as a resistance again, guys. So this 2890 level is a very strong level of resistance. And I mentioned that the markets have been boring, right? And you saw in the title of the video, you know, what direction is the market going? At this point, you know, with corporate earnings coming up, it's really just setting up for a pop or a big sell off, right? We're seeing the point in time that we're in right now is just a consolidation phase where we've seen in the past, you know, we consolidate and either we pop down or we pop up, right? So now that's what I'm waiting for. Are we going to end up popping up? Are we going to end up popping down? And a lot of this movement is going to be on the tail end of earnings, right? What are we going to start seeing in terms of earnings? Are earnings going to be strong, right? We have banking earnings tomorrow, JP Morgan, Wells Fargo. These are going to be two big earnings plays, not really earnings plays, but earnings from big companies that can really kick off earning season and that could really affect the overall sentiment that we're seeing here in the stock market, right? So hopping over here to the Dow Jones Industrial Average, again, just like the S&P guys, not much movement. It was a pretty boring day overall. And it seems like we're just maintaining that level of support that we were talking about in yesterday's video at around $26,100 right around here, which was an older resistance. Now we're maintaining it as a new support. And we're really just trading in between this 100 point window from $26,200 down to about $26,100. And again, corporate earnings are starting up. Are we going to start seeing some good EPS numbers? Is guidance going to be strong for these companies? This is going to have a huge effect on the markets, right? I was reading an article earlier, and I think it was about the EPS for the S&P or earnings for the S&P being down about 4% from last year, which makes sense because the economy is slowing down. So this is something that you have to take into account. So if these earnings drop again, this can again have a huge, huge effect on the overall stock market. So that's pretty much all I have to say in this video on the Dow Jones, right? Nothing crazy. We're still maintaining that level. We're pretty much consolidating here, getting ready for a big sell-off if earnings are disappointing or a big pop if earnings season does very well. So let's hop over here to the NASDAQ composite very quickly, and it seems like we got rejected again at that level of resistance that we've been talking about at around $7,630. And yesterday's video, we talked about how if we were to break out of this level, you know, that's a one-stop-shop to the all-time highs in my personal opinion, right? Because out of this resistance, there's really no other resistance levels until the all-time highs, and we're really extremely close at that point, right?  So just keep an eye on the NASDAQ. Again, it's going to be affected by these earnings. Let's say we have good earnings. You know, the big names do very well. Tech companies do very well. You know, you should expect the NASDAQ to be at all-time highs. And of course, just keep an eye on this 50 simple moving average support level as well, since it's been a support over the past couple of months, as we can see here. And, you know, keep an eye on this 7,500 level, which was an old resistance, which is now acting as a new support. So we're pretty much trading in terms of the NASDAQ, you know, in between this level of 75, 30, and 76, 30, about 100-point window there. So not much, again, to talk about in terms of the overall markets here, but what I want you guys to be focusing on and what I'm focusing on here, again, is the corporate earnings, because this is going to be huge in terms of the overall markets, the sentiment in the overall markets, and the way investors, traders, the people out there actually buying and selling stocks, the way that they view the market. And let's say, again, the earnings disappoint, people start selling out of stocks, taking profits that they've had, you know, over the course of 2019, you know, this could end up in a very bloody, you know, tail end of April, beginning of May, you know, in terms of the overall stock market, guys. So just keep that in mind. I can't drill it enough into your heads. It's going to be huge here in the next month and a half, you know, in the overall stock market. So in terms of my trading today, guys, honestly, I didn't take any day trades. I'm pretty much still sitting on my swing trades of NVIDIA and AMD. These are two stocks that I'm currently in, and these are the ones that I'm planning on swing trading here over the next couple of weeks. So AMD, the fact that it's still maintaining the 50 SMA is a very good sign for me to continue to hold this stock, right? We've noticed the 50 SMA has been a support here over the past couple of weeks. The only thing I want to see now is a break and a hold into the $28 level. And from there, I'm planning on adding more money into AMD. And if you guys want to see a more in depth video on three stocks that I'm planning on swing trading, one of them being AMD and the other one being NVIDIA, go check out the video I uploaded today actually before this video. I go more in depth on three swing trades I'm looking to make and continue to add money to in the month of April in 2019. So the gist of it is, guys, I'm looking to hold this one as long as it's maintaining this 50 SMA support. And NVIDIA, today it actually did decent, it fell down a bit, but it's still maintaining that level of support at around $188 that we've been talking about. And honestly, I want to see a break into the $192 level, $193 level before I do add money into NVIDIA ticker symbol or add more money rather into NVIDIA ticker symbol NVDA. And again, I break this one down further on that video that I uploaded a couple of hours ago. So go check that one out. So that's all I'm doing right now in terms of trading guys. Again, the markets have been a little bit flat, a little bit boring. There hasn't been much movement, which has really led me to be a bit more conservative and a bit more cautious here over the past couple of trading days and especially today until we see some distinct direction and where the market is going to be pushing. And again, the earnings are definitely going to decide where the market's pushing.  Not that those are completely correlated to the market, but these are ones that I'm going to be trading. And if the market explodes, I'm going to be pouring more money into stocks that do well when the overall market does well. So again, just keep an eye on those earnings. So now that we're at it, guys, I have some earnings pulled up here on my Safari. And let's just take a look at the ones for tomorrow. And then we'll look at some very quickly here. I don't want to spend too much time on this over the next couple of weeks. So tomorrow, oh my God, I went too far back here. But tomorrow, April 12th is the five earnings that we're going to be seeing here. And they're going to be mostly banks and financial industries, right? You see, or financial companies rather, you know, PNC, Wells Fargo, JP Morgan, and First Republic Bank, you know, JP Morgan and Wells Fargo being the main ones here. If we're hopping out a little bit to next week, you know, we see a big earnings day next week on Wednesday. We have 75 companies reporting earnings. And you can see them here, guys. And of course, if you want to look at this yourself, I highly advise you to do this. You can understand when your companies, the companies you're invested in are reporting their earnings. You can see all of the companies on Yahoo Finance. That's what I personally use. And it's a useful tool in my opinion, right? And if we hop over to the next week, this is when things start to get juicy, right? The 23rd of April, we have 150 companies here reporting earnings. The 24th of April, we have 233 companies reporting earnings. And if you just scale or scroll down here on Wells Fargo, Yahoo Finance, guys, you're going to see some big names, right? Let's just see what we can pick out here.   The 24th of April, we have 233 earnings.  We buy things on Amazon in 2019. You know, that's what most people are doing, right? We get our paycheck, right? Obviously, some of it goes to investments, savings, bills, stuff like that. But then people, you know, consumers out there, people are buying stuff on Amazon, right? So we're going to see, you know, what their earnings are looking like in their different businesses. And if we're looking down here more, we have another big business here, AT&T. You know, let's see some other ones very quickly. You know, Spirit Airlines, for those of you airlines, people out there, I know Warren Buffett himself is very heavily invested in airlines right now.  And again, the list goes on, guys. The list goes on. There's just so many companies and just think about it, right? You know, Moody's, all these big companies, they're going to fluctuate the market. It's going to be a point in time where, okay, we're going to understand the guidance of these businesses where everything is looking to push. And that's going to give us a better understanding of where the overall market is pushing. So again, I don't want to spend too much time on this. You guys can go through and look at the earnings yourself.  There's a ton of earnings.  Guys, this is the earnings season is rolling up. So be careful and be, you know, mindful of what you are trading, because let's say, another tip I want to give you guys, let's say, you know, you're looking to hop into a stock right now, you're looking to hop into a swing trade, but they report earnings in a couple of days. Be careful, guys, because when companies report earnings, and let's say their guidance goes down, their numbers aren't that great, and you're holding that as a swing trade, and you don't realize they're reporting earnings, that can really burn you, right? You can lose a ton of money, you know, if you're holding through earnings, and you don't realize earnings is coming up for your particular swing trade, a big example of me right now is, and I'm in AMD, right? And I understand that earnings is coming up for AMD, I believe it's on the 24th, and that gives me about 13 days. So hopefully, I can get out of AMD by then with a little profit here, and I can reevaluate the situation after they report earnings. That's ideally what I'm looking to do. So that's just a couple of, you know, tips here, a couple of things that you guys should be looking out for, again, you know, earnings, make sure you understand your companies that you're in, especially for short-term trades, understand, you know, which one of them are reporting earnings, be mindful of this, because it can push the stock up a ton, if you're lucky, right? If they do really well, and the stock goes up a ton, that's great for you, right? But if you get burned, that sucks, right? So you just have to be mindful. So I hope you guys enjoyed this video. If you did, feel free to go down below and hit that like button, guys. I really do appreciate every single one of you all that do that. It really means a lot to me, and it shows me that you enjoy the content, and you want to continue to see more content for me, quite frankly, right? And if you're new to the channel, feel free to subscribe down below, hit that red button, and hit that notification bell while you're at it, so you're notified every time that I do make a video, and drop a comment. What do you guys think about corporate earnings right now? Are we going to be going up? Are we going to be going down in terms of the stock market? Are we going to see a bloodbath right since 2019 has been so great so far? I would love to see what you guys have to say about that. And again, if you want to see three stocks, I'm swing trading and more in-depth analysis on those three stocks. Go check out the video before this one. It honestly should be right up here, so go check that out. And again, I appreciate you guys watching. Good luck tomorrow. I'll talk to you all in the group chat. Peace out.


We're at Roy Arles. Let's see what burger we've got today. Hello. Can I get the ODB please? No problem. Is that a Foodie Ranger? Yes please. One of. I have the same burger. And a few chicken wings. Night in Signing Armour. Oh. Do you know whales are the largest mammals in the world? This is the best burger. The ODB. Nothing to do with whales. Triple cheese. Keep it simple. Majestic.  Triple cheese. Pickles. Mustard. Ketchup. With a bit of. Onion. Bit of mayo.


I already made a video on why I chose to do Air Force ROTC at Yale, but today I'm going to talk about what exactly the life of an ROTC cadet entails. Let's get into it. In most cases, ROTC is a four-year program. You can join in during your sophomore year, but most people experience a program for all four years. That means the detachment is split into four main groups. AS100s are freshmen, 200s, sophomores, 300s, juniors, 400s, seniors. I'm currently an AS100 as I am a first year at Yale. I don't really feel like I'm qualified yet to speak on what it's like being a higher-ranked cadet in the program, but I will share with you guys what my experience as an AS100 has been. All right, there are three main components to my ROTC life at Yale. Those are PT, which is physical training, leadership lab, and then class. At Yale, we have PT twice a week. Currently it is on Tuesday and Thursday mornings. It starts at 6 and goes till 7, but of course you got to be there at 5.45 because if you're not early, you're late. It is a little bit of a rough wake-up, but I think I've finally gotten used to it. Like most elements of ROTC, PT is cadet run. We have a PT commander that comes up with the workouts for each week. It also leads us through each part of PT, whether that be warm-up, stretching, the actual workout, or the cool-down. Most of the time we like to mix it up with PT, but no matter what the day, you can guarantee that you're going to be doing a lot of running, a lot of pushups, and a lot of core, like sit-up type stuff. Those are the three main components of our fitness test that we have to do each semester. An example workout might be a 25-minute Indian run up on the track, and then we'll come down and do like a 20-minute high-intensity interval training circuit that might involve some ab workout too. We also have our own special PT flights where cadets either choose to be in the cardio, the agility, or the bulking flight. So you can kind of set your own PT goals and develop how you want to. We don't split into these groups every time, but a couple of times a month you can guarantee on being split into your groups and where you're able to work on what you want to work on, which is kind of nice. Overall, I enjoy PT a lot. It is never easy waking up that early, but you always break a sweat and get a good workout in. So it's worth it in the end. All right, the next component of ROTC is leadership lab, which takes place immediately after PT on Thursdays. As soon as we're done with PT, we run up to the locker rooms, jump in the showers, and then change into our actual ROTC uniform, which for the AS100s is the blues. Either long sleeve or short sleeve, depending on how cold it is. As soon as we're dressed, we meet back down in the gym, form into our flights, and conduct the opening ceremony. For AS100s and 200s, leadership lab is basically the place where you learn all your drill and marching skills. You learn customs and courtesies of the Air Force and many other skills. But most importantly, leadership lab's purpose is to develop you as an Air Force leader. Does that be participating in activities as an AS100 or 200, or leading the AS100s and 200s in their activities as a 300 or 400? Like PT, leadership lab is also cadet run. It's led by the AS300s and 400s. An example, leadership lab might be 45 minutes of marching and drill training. Then we might have a 30 minute GLP, which is a group leadership project, which kind of tests our ability to lead and thank on a moment's notice. Then we might finish up with a quick lesson on a specific Air Force skill like impromptu briefings. At the end of leadership lab, we all gather up at the end. We have announcements for the week.  Basically leadership lab is just the place where you learn all the Air Force skills that you can't be taught in the classroom. Speaking of classroom, as soon as leadership lab is done, all the AS100s head over to another building where we have our classroom session. First at 930, it's only 50 minutes long, and it goes till the 1020. Also keep in mind at this point, we're still in our ROTC uniforms, and we're required to be until 4pm that day. Right now my Air Force class is entitled Introduction to the Air Force Part 2, I guess. It's the same curriculum, just split into two different semesters. We've learned a lot of interesting stuff so far, but for the most part, the class kind of takes a look at the Air Force from a very broad perspective. As we talk about the Air Force's history and strategy on war and combat, but we also get a chance to learn some of the more specific skills that we need to become an Air Force officer, like dress and appearance standards, and customs and courtesies. After class is finished on Thursday, I am done with my ROTC duties for the week, I guess. I still have to wear the uniform till 4pm, like I mentioned, but I don't have anything again until that next Tuesday, unless we have like a service project or MWR event over the weekend. I guess that's about it. Keep in mind that it was just a brief overview of what ROTC life is like at Yale as a first year. If you have any questions about ROTC, Yale, me, or just want to say hi, make sure to leave them down below in the comments section. If you enjoyed the video, give it a nice big thumbs up. It helps a lot more than you think. If you're new and you haven't subscribed yet, you've been making a big mistake and you should definitely do that as soon as possible. I will continue to post daily content as long as I possibly can. I am trying to balance schoolwork and everything with it, so we'll see how far that goes. Anyways, enough with me rambling. I'll be back soon with another video, so see ya.


Today marks the International Day of Charity, set aside to remember the death of Mother Teresa, and honor the tireless work people do to help others in need. Plus TV Africa's Ngoziqa, OHS, went to speak with the co-founder of a foundation that caters to the well-being, education and empowerment of homeless children and other young people in disadvantaged circumstances. Every year, charities all over the world help to save and improve people's lives, fight diseases, protect children and give hope to millions of people. The International Day of Charity raises awareness about the work other people do to help those in need. Kemi Ojenike is a lawyer and co-founder of Destiny Trust Children Foundation, which currently houses over 50 children and takes total care of their well-being and education. To her, the Day of Charity presents an opportunity to think about the needs of others beyond ourselves. The International Day of Charity is a good opportunity, in my opinion, to shed light on some of the work that goes on within the non-profit space. Within this space is one where you have individual organizations coming together to address a societal problem and to move the society forward in their own way. And there are so many people that are doing a lot of amazing work in this area. There's also an opportunity to think about what we are doing in our own little way, to lift others up in our society. As most charity organizations go the extra miles to help the needy, they face challenges and crises which they have to find ways of overcoming. The challenges that charities face, particularly in the area of funding and support, for instance, one way that that problem is being addressed is through the use of social media. It has made it a lot easier. Things that we could not do, like when we started, there was probably, we didn't have as much of a reach as we did now. There are people from all over the world that can learn about our work, learn about what we do and participate in ways that they can. Data from the World Bank put the number of people who live in extreme poverty as 619 million, with more than 10% of them living in Nigeria. At these, the high unemployment rate in the country, the displacement caused by terrorism and mandatory, the economic effect of COVID-19, charity work could be done too. But Jenike had some advice for humanitarian workers thinking of giving up. Just continue what you are doing. It may seem like you are dropping the ocean, but it makes a difference. The things that matter the most in life, they are not the things that give you the immediate reward. The things that will last are the things that take time. And charity work takes time, because transformation takes time. But in the long run, it will be worth it. Jenike believes the International Day of Charity importance lies in its ability to form the mindset that people need to be their brother's keepers. The day was originally set aside to honour Mother Teresa's timeless work to help people. But it's also to celebrate charities all over the world. For Plus TV Africa, Ngoziqa or HSE.


Good morning, sir. My name is Srinu. So, here I am working under FATXA. So, I am presenting my report as a final PPT. So, my topic is massive open online courses enhancement to edX platform. So, here you can see the outline. The first section is about to massive open online courses. So, in this section I am going to introduce what is MOOC and what are the advantages and what are the challenges and few existing MOOCs. So, just go to the introduction to MOOC. Here you can see the picture a MOOC which is taken from Wikipedia and it is explaining each and every letter in MOOC. First, what is the M in MOOC word? M is massive. So, before 2011, massive, massive users, it is about 1000 people maybe below that, but now it is a crossed lack of people, lacks of people in some MOOCs. So, M, we are considering him massive is a unlimited participants in online courses and which is taken from I have given some, given a citation here. You can see it massive is unlimited participants and open. Second, second letter is open. Open, it is free and not limited by the geography accessible, no prerequisites required as traditional methods and content will be shared. Next, the third letter online, it is mostly based on internet based. Some of the MOOCs are offering offline learning also, but mostly on internet based. So, it also maybe work with mobile technology. The last letter in MOOC word is courses. Courses, learning will be involved by the interaction between the instructors and students and with the help of forums and chart, chart boxes and extra. So, we will go to the next slide. What are the advantages of the MOOC? The main name of the MOOC is large scale interactive participation of users with the help of web. We can see some other learning management systems also available on the web. Those are Moodle and DotLine, E-Front and etc., but these are a lack of large scale interactive participation. Maybe they can up to support around 2,000 to 2,500 users at a current stage. But here, when you come to the MOOC, we can support up to lakhs of people, maybe 1 million or more than that. So, the main aim of the MOOC is interactive participation. Next, what are the various features offered by the MOOC? First one is videos and study materials will be available and quizzes will be conducted online exams and also the MOOC developers trying to make it more efficient than the traditional education systems, which is a brick and mortar face to face learning. And free access to organized content. Students can make global connections. There are the areas which are not aware of quality of education because of the resources available in that areas. And these kind of areas can access quality education from the established universities with the help of MOOC systems. But the internet should be accessed in those areas. And these MOOC systems also helping carriers, carriers of the students. For example, audacity. Audacity offering online courses for free and anyone can offer the courses. And after that, after that, proctored exams also offered. Proctored exams means student will be over at the exams under the supervision of somebody. So, after that, they will offer certificates and also they will provide some provision of jobs, provision of career opportunities. So, after that, they can use any online tool that are relevant to these MOOC systems. As I mentioned, people have no access to foreign higher education, where the corners of some countries can participate actively in MOOC systems. This is informal and can be organized easily. Which is informal means a student can watch the videos, student can attend the quizzes based on the requirements of that MOOC. This is informal. One can learn anytime, anywhere. And learning will be possible by the collaboration of learners and instructors. And it supports career opportunities. Still, MOOC is coming with day by day with the help of advanced technologies. Still, the dropping high dropout rate because of the lack of motivation of the students. So, how to increase the throughput of the e-learning industry? How to utilize it in a good manner? So, some of the challenges are we listed here. The first one is the accreditation. Accreditation, which is saying that how you will grade when you conducted a final exam for a large scale of people. Let us say 1 million people finished one final exam. Then, how can you grade them? Whether peer-to-peer evaluation or instructor evaluation or automated grading, which one you will choose? So, ultimately the best relation will be automated grading. So, that will be the one challenge. And next one is the self-regulation and motivation of the participants, which means students the successful completion rate in e-learning industry is about 5 percent and below the 5 percent because of the lack of motivation and the regulation. So, how to increase the motivation of a student? And how to regulate them? Maybe this will be one of the problem and a high dropout rate and assuring academic integrity, learning analytics. So, based on the learning analytics, we can come to know that how a student is behaving on the learning objects. Learning objects means here anything, anything which is giving information to learn something. Maybe one PDF file will be the learning object and one video lecture on us, one video lecture will be the learning object. So, how a student is attempting the learning objects first, what he is doing? Whether he is going to directly assignments or he is listening to the videos or he is reading the documents, provided documents. So, these will be the learning analytics. So, by doing the learning analytics, how most of the students are behaving? If you know that, then we can give a proper way to motivate them. So, next as I said automated grading, this will be the one of the challenge and standards on grading. So, maybe MOOC is available to all over the world. So, standards are different from the university to university because of that we are getting old rankings these kind of all these things. So, when one world top class university offering MOOC courses, then how the other university students will receive it? This will be the one of the problem. So, whether that students need to come up to these level, best universities level or then these lectures should go to their level to explain in better. And existing MOOC systems, the one is LEADME and Coursera, Audacity, ADX and ADC. I am not going through the detailed in these MOOC systems because we have all less time. And so, I am skipping these things. I will explain these things clearly in my main presentation. Next one is the related work and literature survey. So, in the literature work and literature survey, you can see here outline. First, we concentrate on feature based survey of different learning management systems. The first one is open source LMS management systems and the second one is appropriate to based learning management systems. The second one is targeting the profit and these are the profit based systems. So, we will go to the feature based survey. So, we are we surveyed around 13 LMSs of open source. The first one is here you can see Moodle, Ephraim, Sakai, Converse by Instructor, Fadena, Olat and ClaroLine, DotLine, Lugais, Etuter, Chemilo, Ilyas, Totara LMS and ETC. So, what we did, we just we given introduction to these systems where they came up and know who developed it and after that we listed some of most important features to each and every LMS. So, I am not presenting here it will take too much time. So, the next and finally, we considered some of the features from this entire 13 LMSs and we concluded that features of those are going to implement in our model. So, that is in our model which is I am going to present in later. These are the proprietary based LMSs like Bull Learning System and AdModo Informatica, My Big Campus, Sclipo and ETC. These are all based on profit based profit based companies. So, next the concluded features. Here I concluded some around 8 features from those total 13 are open source LMSs and 12 are proprietary based LMSs. So, from the 25 LMSs we made some comparison roughly I am not presenting here. So, we concluded 8 features. So, first these 8 features are going to implement in our model. So, the first one Learning Paths. Learning Paths will given by the instructor. So, what these learning path saying that how student will proceed when he learns a course, when he learns some topic. So, first you do these things you read some materials after that you see these videos after that you go to the assignments and you do it and after that attempt these features. These way of path. So, these way of path will be given by the instructor and the student should be followed that to get the better results and the next one is better navigations. So, we are trying to implement we are trying to give a better navigation in existing MOOC system. So, that MOOC system is EDX. We are going to use the EDX in our campus that is IIT Bombay. So, in that so we are trying to implement a better navigation system and the next one is a self-paced learning self-paced learning. So, based on the student requirement learning will be proceeded. So, these things so when he want to learn something then that those kind of information will be provided to him. So, after that when he finishes it then he can learn anything. So, no deadlines will be there. So, this is the self-paced learning next one is the offline learning. So, currently offline learning is not available in the EDX. So, the this one also we are trying to make it and the next one is about presentation authoring SMS alerts, mobile access, competency management and adoption of learning styles into elements. First of all, what are the learning styles? Learning style is one's learning preference. So, how he would like to learn something in which way? So, actually everyone has their own learning style. So, what are the main point is here our teachers can assess the learning style of students in a class and then they can adapt the classroom methods to suit each and every student. So, this is the main aim of the thing. So, to get the better results. So, general learning styles one is visual and next one is auditory and the last one is a kinesthetic and or tactical. So, what the visual is doing? So, in visual category students will try to understand things by the diagrammatical and pictorial representations and or by watching some videos in the next auditory. In auditory category students are trying to attend the lectures mostly. So, they are listening by vocabulary or listening classes, listening to somebody. The last one kinesthetic and tactile. These people are learning by doing something active with the course. They are good at solving problems. Why we considering these learning styles? So, because why we are considering these learning styles? The dropper rate is very high in e-learning industry as I mentioned in earlier slides. So, we would like to improve the successful completion rate using these learning styles. So, how we proceed will be given in later slides. So, to increase the successful completion rate we consider some who progressed our model. So, before that I am going to discuss a few models. So, this is the first model I am going to take is a David Cubs model. So, from these are David Cubs model there are four categories of students. The first one is converger, diverger, estimulator and accommodator. The first one they are good at practical applications and solving problems. The second one is a diverger they are good at imagination and come up with the new ideas and estimulator. They are good at inductive reasoning and creating theoretical models accommodate the learns by doing things and engaging with others actually. So, you can see here a picture how they are classified based on concrete experience and reflect to and act to experimentation and extract conceptualization. By these four categories based on two of them there will be one category. So, this is what say what says David Cubs model. The next one we consider is the Fielder-Selverman learning model. So, here he has also come up with four categories. The first one is sensing learners and intuitive to learners. So, the sensing learners what they like facts and whether here they like discover possibilities like to discover possibilities and relationships. These are the differences I given here and you can see here also the next one is act to learners and reflect to learners. Act to learners they learn by doing something active with it. The reflect to learners prefers to think about it first after that they will do it. But these two kinds of these two kinds of learners belongs to only one category. They will fluctuate between these two things and the third one is sequential learners and global learners. When they learn they like to follow a sequential steps and they will understand each and everything in every step and they will conclude the whole concept at the end. But in the global learners they will get a big picture first and they will get some idea whole idea first and then they will understand each and every functionality in that picture. So, these are these things and the last one is visual learners and verbal learners. Visual learners as you can see here they learn things best by watching pictures and diagrams, flow of films and demonstrations. Verbal learners learn more from written and spoken explanations in the next one. Unreal, a flaming swag and walk model. So, here are the same visual learners, auditory learners, reading, writing, preference learners, any kinestrical learners or tactile learners. As I given here the as you can see here I given already definitions of these learners and we are concluding of the learning styles. So, I consider these learners learning styles and also I have seen these learning style candidates in my educational career. So, some of my friends are like to learn while looking at the pictorial representations and visual things like when somebody explaining on the board instructor. So, they like to learn at the time, but some of my friends they do not like to attend the classes. They will they will learn things by reading textbooks, they will not attend the classes, but the end of the semester they will read 2, 3 textbooks and they will attend exams and they will finish the exam with good grades. These kind of learners are same. The third one is experimental learners. First the third category is we will go to directly with the problem assignments, because here the problem is experimental learners they will need some prerequisite knowledge about the course. So, they will have some knowledge suppose somebody did the course, but they he did not he is not sure about that course completely. So, what he do he will go somewhere online MOOC and he will attend the assignments and quizzes to learn best. So, he has already prerequisite knowledge then he will do assignments directly no need of listening all of the videos or no need of reading all of the materials these will. So, we consider 3 these 3 learning styles. So, we proposed our model here. So, first of all how we find the learning styles. So, in order to find the learning style a questionnaire will be provided to the student at the time of the course registration in a MOOC system. So, that course will include may be 10 to 15 sorry that questionnaire may include nearly 10 to 15 questions based on that responses we will decide in which category that student will be there. So, based on these 3 categories. So, after the finding of learning style of student we will come up with these proposal. See here we can see CS 101 the course first on the main course. So, after this course we will create 4 different courses based on that CS 101 the first course as you can see here CS 101 visual CS 101 visual. So, this course mainly contains videos and pictorial representation and mostly this is this is to target visual learners the second one is read and write CS 101 read and write course these courses mostly targeting the read and write type learners. So, in this course there will be no videos, but there will be mostly read and write materials and the third one is CS 101 experimental. So, in this there will be all assignments and the solvable problems these kind of things will be there. So, based on that after we the last one is CS 101 common. So, this will contains entire the rest of 3 courses. So, this is CS 101 common. So, when one student is registering the course he can skip the learning style thing and he can go to the direct CS 101 common course. If you want to try it and he can go and attempt the questionnaire and after that we will classify him based on our based on his responses then we will have one of this course whether visual or read and write experimental. So, apart from this way we would like to maintain a repository which will contain each and everything related to a course. So, suppose when a student attempting visual course. So, he is some uneasy with the visual kind of course. So, he can go to the main repository of that course and in that course everything will be available reading materials everything. So, he can go on he can check anything. So, at that time we would like to track how that student is going to attempting the learning objects based on that. If you attempting more on reading materials when he is attempting to visual course then we would like to suggest him. So, you would like to change your learning style or not. So, that is up to based on his choice. So, first we would like to find learning style after that we will suggest one of this course and after that if he wants to attempt the repository main repository and we will track the activities of the student and we will suggest him after that. So, the way name here is we would like to motivate him in order to complete the course. The next one is EDX MOOC. EDX is the youngest MOOC we can say like that, youngest MOOC in e-learning industry. So, we will see the first EDX is a non-profit organization which is founded by major universities in the world which are Harvard and MIT. So, this has open closed in June 1 of 2013 and all over the world people are trying to make more effective than the traditional education. So, these as developed based on the Django framework the user technologies are Python, Ruby, Perl and Node.js. So, the database is MongoDB and SQLite. So, along these EDX MOOC developed as a different modules at a time and after that they are integrated together to make a complete system. So, here you can see that these are the main modules of EDX MOOC. So, these modules need to be integrated to come up with a complete system in order to offer courses and in order to host somewhere. So, you can see here EDX or EDX platform code gel, XQ and X server CS comment service. So, I will introduce I will explain it in my main PPT clearly as you know already them. So, here you can see the interface of the LMS of a EDX. So, one can log one can register now here with a register now button and after login and he will go to the church search courses and he can up the courses and some of the features. Self-paced learning, online test and assignments, subtitles of videos, search will be there activation emails and a number of students and of course you can see that ring and online discussion groups and these are all features and this is the CMS and this part is mainly for instructor. One can sign in one can register here and sign in here after that he can create a course and after that he can invite students all over the world. So, these are the some main features adding videos, creating assignments and import export course, add course team, live view on preview. This live view preview is how students is viewing that course when you create a course this will show that thing and adding grading policy course updates, update updating static pages also and here outline you can see. Here yeah based on EDX MOOC we are trying to implement one blended learning model. We are considering it as a new methodology in education system in the world. So, when we go to the introduction to blended learning. So, here the blended learning is a combination of two aspects. First is learning which is a brick and mortar method and the next one is online learning. So, the combination of these things will make blended learning. In our model what we are going to do first CSUNOM on computer programming and utilization course will be offered by professor Deepak B. Fartak from IIT, Mumbai and after that we will approach different colleges in India with our proposal. Here different colleges will include well established universities and affiliated colleges under that universities. So, these universities so has to collaborate with us to implement our model. So, what is our proposal? All students related to the all engineering colleges need to opt this course CSUNOM. It is mandatory under pre-different terms of conditions. So, what students do actually in every engineering course there will be one CSUNOM programming course in the first year of BTIP. So, we would like to replace that course with our CSUNOM computer programming and utilization course. So, after agreement of the colleges we will train the local instructors at face to face interaction with a super instructor. Here the super instructor will be from IIT, Mumbai. So, training includes maybe so how the training process will go goes. So, we will select places based on a poll or convenience of the local instructors. Then we will conduct the training process and after that the training process will includes introduction to UNIX OS, programming tutorials and a grading system. ADC will be trained to them. So, after that entire information related to CSUNOM course will be given by IIT Mumbai only. Assignments, quizzes, final exams will be by IIT Mumbai only. So, after this when we go to the grading of assignments how what it will do grading, how we can do the grading of for a mass amount of people, students. So, we would like to implement one method P2P revolution which was offered by Professor Deepak B. Fatak in CSUNOM course for three years in IIT Bombay. So, how it will do? First in a group of students, in a branch of students, students will be divided into groups. So, let us take three groups. So, one group will be graded the other group and the other group will be graded the some other group. So, finally these three groups need to communicate with the local instructor in order to in order to give the final grades. So, this will we got fruitful results when we applied here. So, and the entire 100 percent marks of a course will be divided like this 80 percent of marks from IIT Bombay and 20 percent of marks from the local instructor. So, final grades will be sent to respect to colleges after the final exam. So, grading can be either automated or P2P grading. So, this is the hierarchy you can see here in the advantages of our model. So, elimination of duplicate work for local instructors in a massive around 10000 instructor only to prepare documents and P2P for day by day and day by day classes. And you can see some other better training and better teaching, better learning successful completion rate can be increased up to 99 percent because of this is mandatory to every student. So, if everybody participated then without dropouts then we can increase it up to 99 percent, but presently successful completion rate is about 5 percent. So, you can see the main difference here. So, the main aim of this model is to increase the successful completion rate with a quality of education. So, minimum pass grade will be given because people will afraid standards of IIT Bombay. So, that is why we are including this minimum pass grade on the next challenges. So, the main challenges does UDX MOOC supports our model or not. What are the architectural technical administration issues? What database tables need to be created? What to be modified? After that implementation of our model, testing and deployment. So, how we can assure the academic integrity and automated creating? These are our challenges and we would like to cross these barriers in our next stage. And the last one is my problem statement. So, the main aim is to provide motivation. What is the motivation behind the problem statement? The main aim is to provide quality education all over the world with a successful completion rate, increase successful completion rate. So, the dropout rate need to be decreased and throughput of the e-learning industry is very low currently. So, we would like to increase it to improve successful completion rate of a course. And so, we come up with our model blended learning MOOC model of IIT Bombay learning with the help of UDX MOOC. First we would like to implement in Indian colleges. After that we will approach respective people for the worldwide use. So, my problem statement will be here in implementation of blended learning MOOC model of IIT Bombay. We are trying to implement this fast and after that integration of all modules, what I presented in the later, what I presented in earlier slides. These slides are CS command services, CodeGL, X server, EDX. These are to improve the functionality of EDX MOOC system. So, still if time permits us, after the completion of ever two tasks, we will consider the following issues. Adaption of learning styles into EDX MOOC and into our model also. And the implementation of following features in the next slide. Implementation of the features of which are presented in which are presented in the related survey. Those are here, concluded features. These eight features we are going to implement if time permits us along with the learning styles. Thank you, sir.


What's going on guys, today I am back with another Destiny 2 video and today I'm going to showcase to you guys this epic glimmer farming spot, you could call it a loot cave as it kind of is, but whatever, now before we go any further guys, do you want to win some juicy loot? I've got emblem cores, I've got spiral cores, I've got gift cards, I've got much much more, to win some simply drop a like on this video, leave a comment down below and join my discord server, I'll link within that video, just to go with the reruption. Ok so I'm not going to lie to you guys, doing public events in a fireteam or doing a raid will earn you just as much glimmer as doing this, but then again, public events you normally have to wait for, this is unlimited, raids you need a fireteam for, this can be done solo, quite easy, it's basically for the people who want glimmer fast, it works super great for that, now before I show you this location, you can actually get glimmer quite easily, another way and that's by deleting shaders you won't ever need, odds are you might have hundreds of a certain planetary shader which will give you 25 glimmer each for, probably means you have to delete them one by one and that takes ages, this farming spot I'm going to show you guys is even faster than deleting shaders, it seriously is, also guys you guys know me, I give credit where credit is due, but this is actually a farming spot I found on my own accord looking for a glimmer farming spot, so if anyone else uploads this they better give me credits, ok so basically you want to head to Titan, once you are there you want to load into siren's watch, once you've loaded into siren's watch, you want to head in a location I take right here, you'll come to this room and you'll be welcomed by Hive Enemies, one wizard and about six or seven other Hive Enemies, which you take care of each giving you ten glimmer, once you've taken care of these you head to the room in the far back corner, this again will be filled with between six and ten more Hive Enemies, take care of these, again they will each give you ten glimmer, best part about this is though each of these rooms normally spawns in a high value target and when that happens they're normally spawning at the same time one in either room, so if you're quick to take care of one you can head to the other room and take care of the other, equaling out in you earning even more loot, epic engrams, tokens, even more glimmer and much much more, so it's a win-win situation, so once you've taken care of one room you simply head back to the first room and all the enemies will have respawned, you take them out again, head to that second room and take out that second lot of enemies and for eliminating each of these enemies you earn ten glimmer, now you can do this in a fireteam and it's even quicker and don't forget guys if you're in a fireteam each of the kills your teammates do will count towards your glimmer count, so it is a win-win, it's obviously faster as a fireteam, on screen I'll actually showcase to you guys about a minute of me doing it in a fireteam and you'll see how fast and efficient this can be, it seriously is a great glimmer farming spot and what you're looking for basically is an endless spawn of enemies, now there's one on the EDZ but it's slow to spawn them, this is super super quick, you run from one room to the other and both are filled with enemies, equaling out in you earning unlimited glimmer as long as you stay here and grind longer enough, but yes guys it is a place for you to earn easy fast glimmer, if the video helped you out help me out by leaving a like, I do appreciate that support, hope you enjoyed the video, hope you enjoyed your stay, if you're new around here and enjoy daily Destiny videos be sure to subscribe, if you never want to miss a video I upload hit that bell button next to that subscribe button to receive email notifications of when I do upload but hopefully this video helped you out and hopefully I will see you on my next video


How's it going, everybody? My name is Anthony Messerly. I'm a principal engineer at Rackspace. And today, I'm going to be talking about stateless type advisors at scale. So a little bit about myself. I've been with Rackspace for almost 14 years. I originally started out heading out in hardware development for Rackspace. And then I transitioned over to work on the initial cloud servers product offering. We acquired Slicehost, so I transitioned over to that full time. And then that eventually became working on heading up the engineering team that launched the next gen OpenStack public cloud. Some of my passions are just R&D and prototyping out new products and just kind of playing around with things in general. A little bit about Rackspace. Our OpenStack public cloud has been in production since August of 2012. We have six regions across the world. We have tens of thousands of hypervisors and over 10 different hardware platforms. And primarily, we utilize Citrix Zen server on our virtualization product today. So let's talk a little bit about traditional hypervisors and kind of what they're about. If you think about the components of a hypervisor in OpenStack, they typically run as bare metal. They have an operating system. You run some sort of configuration management on there. You run a Nova Compute, of course. And then when you talk about the instances, there's usually the settings and the virtual disks on there. And the mission of a hypervisor, it needs to be stable, it needs to be secure, it needs to provision and run instances reliably, and also needs to be consistent with other servers. If you're running code against servers, you want to make sure that they're predictable and look the same. So some of the problems we had with hypervisors at scale, we had multiple versions of Zen server. We launched in 2012. So over time, new versions came out, a lot of new performance optimizations and enhancements. So with that comes with a lot of different patch sets, a lot of different security fixes, a lot of kernels and Zen hypervisors that need to be kind of patched. There was a lot of Zen vulnerabilities the last few years. So the more versions you have, the more versions of that you have to patch. So the point is the more variations, the more work. And then when you talk about server hardware, you have the incorrect bio settings and firmware that come from the factory sometimes. Old servers have been running for a while. They've never gotten the updates, but new servers do. So there's a lot of inconsistency there. And then when you think about operational issues, a lot of OpenStack and hypervisor bugs can kind of leave things in an undesirable state because there's constant bugs over time. So how we solve some of these problems, we built a provisioning system that uses IPXE within the BIOS and then uses Ansible within a utility disk to kind of do like a factory style provisioning process. So every server that comes through or any server we reprovision goes through this process to kind of make sure that the firmware in the BIOS is all up to date. Then we consolidated hypervisor versions just to kind of try to reduce the number of variations. And we used Live Migrate on the ones we could to move instances off. And then we refreshed that host and added back to the pool. We also automated a lot of operational tasks on the hypervisor to try to reduce inconsistencies. If you see a hypervisor in a weird state and there's something that's not right, we try to resolve it. The tool is actually called Resolver. And if we can, then we manual intervene. But ultimately, we still have the issue where we're kind of running a traditional operating system. You have to install it to the machine. You have to update packages. So our goals were to, with this project, try to rapidly deploy hypervisors. We wanted to take advantage of every single server reboot that happened, either from a maintenance, a hardware failure, to try to bring the system up to the latest spec. These systems usually run a long time without a reboot. So it's kind of imperative to kind of take that opportunity to kind of bring up the date when you can. We wanted to have a reproducible image of our build that we could pass around to developers, engineers, quality engineering, just so that they could work on what exactly is in production. We also just want to ensure consistency on the hardware platform and the operating system. These are cattle, not pets. They have one goal in life, and that's just to run instances. So we want to kind of treat them that way. So the concept is live booted hypervisors. So if you're not familiar with LiveOS, it's a bootable image that runs in system memory. And the image you always boot is usually predictable and portable. And lots of OS distributions kind of use this today for installation or rescue. And you usually boot them from a CD network, USB key, and so on. And it lets you kind of boot an operating system, kind of look around, kind of use some functionality with that actually modifying the running state of the system. So what if we applied the same concept to run our hypervisors? So as Bill Riley would say, we'll do it live. We use a stateless live OS of our hypervisor that boots from the network to try to promote consistency across the board. Everything boots that same image, so everything's consistent. And we're using Ansible to build that operating system image from scratch. So every time a commit gets checked in, it kicks off our CI CD process that runs the build process through to generate an image and then spits it out to our deployment server. Let's just separate the operating system from the actual customer data and basic configuration on the server. So we basically take the thing that every server has and kind of separate it, put it off to the side, and then just the uniqueness of the server along with the customer data that's all kind of separated. And updating to the latest image is done by either rebooting or doing a K exec. And you can also catch up the image while it's online by just by adding packages and then updating packages to your image. So the next reboot, we'll get it. So where does the persistent data go if it's a stateless image? You can still tell the image where to look for its data. What we did was we created a systemd unit file early on in the process, which mounts the local disk, and then it creates sim links to the local disk from that. So as services fire up, they look for the location that you sim linked, and it redirects it to the persistent store. So in this case, your second partition is mounted to slash data. So the script will just generate a varlib nova sim link to data varlib nova. So then you can create a sim link for every directory you want. Usually there's not a whole lot of directories you need to sim link out. Some common ones are eti nova, your networking configurations, and just maybe some logging if you don't want to. You can either send your logging to a syslog server, or you can persist it to that local store. So how is this possible? We're leveraging the draka project. Some of you might be familiar with it. It runs in the init ramfs during boot, and its main goal is to transition to the real root file system. It's got a lot of functionality for retrieving the root fs over the network with HTTP, FTP. We recently added torrent support to it, so you can actually point to a root fs torrent and retrieve it that way, which is really useful for when you boot a lot of servers at once. And then there's many tunable options that can be set via the kernel command line just to kind of control how it boots and how the behavior works. And you can find more information at their wiki. So why use a live OS? Everything boots from a single image. You can make changes without a reboot, but you should update the image. Security updates can be rolled out to a live OS to avoid a reboot. But you can also add it to the next reboot, so the next image will get it. And you can update to a new release of the OS and roll back to the existing one if you need to. It's portable and easy to test and develop on. You know exactly what's in the build. Everything is version tracked. You can tag tickets to it. If you're from a Jira, you can mark all that. So if you had a feature or something, you know who added it, you know what it was for. And memory is cheap. It was constantly in the past. Back in the day, there were 16 and 32 gig boxes. Nowadays, everything's 128, 256, and up. So it's really cheap just to run the file system in memory. So let's talk a little bit about how the image process, the image build process works. So we put together a tool, internally a rack space, called Squashable. It's kind of a combination of SquashFS and Ansible. And it's just a bunch of Ansible playbooks that automate the build process of creating the images. And it supports multiple versions. I think right now it supports Sentos, Debian, Fedora, OpenSUSE, and Ubuntu. But you can actually add additional OS support to it, as long as they have Stratkut or another way to live boot. When we started down this project, we originally started with Debian Live. And it worked really well. The problem was, I think Ubuntu tried to use Casper. And then Fedora was using Stratkut. So there was just a lot of different distributions using different things. So we wanted to try to find a way to unify that. So we started using Stratkut. And for right now, it seems like most of the operating systems have a way to use that to create a live OS image. So the one we have internally, we kind of have been pulling bits out of it, trying to make it really generic, try to support a lot more operating systems. And that's the one we put on GitHub. And I'll get to that later. But really, the bulk of our configuration management is really done during the image build time. So any kind of packages we want, any kind of optimizations and customizations, we can do at that point. And then once the image is online and running, then you can deploy the configurations to it. And that's where it'll persist on the store. So if you reboot the image, it'll still pull the configs that you deploy to it. So all changes in the build, like I said, live within a repo, they're fully tracked, very reproducible. So you can really just kind of spit out an image of the latest build. Or you can go back to a previous image that maybe you're running production and try to reproduce that. So let's kind of just walk through the image process. So we start out with an initial bootstrap. So we use Docker to kind of make sure that we don't have to use multiple build servers for each one of the OSes. So we kind of use that to abstract the OS we're building in. And we use it to create a minimal truth. So the minimal truth initially consists of a package manager and an init system. And really, it's just enough packages to run Ansible within a Trude. Once we're done with that, we tear down the container. We copy the tar ball to the Jenkins server. And then we go on to the next step. So the next one is we prepare the Trude. So we use the Ansible Trude module to kind of catch up the OS. We throw some version tracking metadata. So it's basically some information from the build server. We throw any kind of package manager configurations to where to go pull your packages from, any kind of pinning we might need. We throw that in there. And then we kind of update all the packages at that point in that stage. And then the next stage is the common configuration. So really, this is just a bunch of different settings or packages you might have within your company where you might want to have one image that everybody can log into. It's really just all the stuff you have within your company, like your logging practices, your auditing practices, security, auth, all that stuff. You'd want everything in this common playbook. Next, you'd have the personality you'd want to apply. And that could be a KVM hypervisor, Xen hypervisor, whatever you want to do, whatever kind of makes it different from the common configuration or from other images. And there you'd have all of your performance optimizations, maybe libvert configs, and just different things in there to make that the actual image that you want to boot. So at this point, the image is done. So you'd copy the kernel and the init ramfs that were in that route out to the deployment server. You would tarball the file system that you generated and copy that to the deployment server too. And then in our case, we use MKTorrent to generate a torrent file of that tarball. And then we have our deployment server start a torrent using our torrent to create that initial seed. So then as servers come online from the deployment server, the first thing they'll do is go retrieve the kernel, do init ramfs, and then they'll go hit the rootfs torrent and retrieve that. And as the more servers come online, the more servers start seeding that torrent. So you actually add more to the swarm. And it's really just a quick retrieval of the torrent initially, and then it stops seeding it. But once your OS is booted, you can actually fire up another seed again just to continue on that seed to speed up the whole process. So let's touch really briefly on the boot process and what it looks like. So we built an image. Now what are we going to do? Well, let's try and boot it. You can use iPixie or PixieLynx to kind of boot it over the network. You can also use it. You can also boot it from the local disk using grub or extLynx. And you can actually boot it into the image from a running server using kexec. It's very easy to fall back to the last booted images, too, if your network boot has somehow failed. If you have your boot order and your BIOS set to network boot and then local boot, it can actually go to the local boot, hit grub, and then go talk to the last known image you pulled previously. This is a matter of just scripting that out and just kind of making sure it's pointing to that. There's, of course, lots of open source provisioning systems out there, so I'm not going to really go into too many of that. There's a lot more homegrown ones. I'll touch on our homegrown one in a second. But this is basically the iPixie config you'd have. You're basically setting up some draca commands that basically go on the kernel command line, retrieves the kernel, retrieves the initRD, and then you can set it to pull from HTTP or Torrent or whatever. This is some of the booting from extLynx. Like I said, what we're doing is we create a boot cache. So once it retrieves that image the first time, it caches that on the first partition of the disk. And so then we also generate the grub config. So if the server does have to reboot and local boot, it can pull that last known image and at least get the server back up and running. That kind of helps out with the case of a boot storm where you lost power, everything's trying to boot up. You at least want to get everything back up and running without having to worry about things pulling the network. And you can also roll out images ahead of time and just skip the network boot entirely if you wanted to. And then you can boot from kexect, which is really useful if you're trying to iterate on the image and try and see how it works. And the big trick to kind of make sure you kind of work on is to make sure your hardware drivers work with kexect. There's some issues with some drivers where if you kexect, it'll just hang because it doesn't release the driver properly. So it's just one thing to kind of watch out for. So our primary boot method that we use at Rackspace is called Terraform. In a nutshell, it makes a DHP request and retrieves an IPXE kernel. It identifies itself using LDP. So it listens on the network for packets. LDP is the link layer discovery protocol, if you don't know. So it listens for packets from the switch that say what's switch name and what's switch port that the server is connected to. So we use that information to go look up where that server is in the data center from our inventory management system. And we call that inventory management system Galaxy. But once it goes and finds its server number and all its attributes, it pulls that down and it throws those into an IPXE template. And from there, it looks and sees what its boot status is, what boot operating system it's supposed to run. And if it's a new deployment, we'll typically run it through the utility disk that we have. It's just a utility live OS. And it'll catch the firmware and biosettings up to date. It'll configure the storage if there's a RAID. It'll configure OBM, the auto band management. It'll capture all the inventory and it'll push that to Galaxy. And then it'll load K exec into the Hypervisor OS or at that point start the install of our older builds. So some initial scale tests. We tested with about 200 servers on x86, running Fedora 23 base live OS. The time to build the whole package and run it through the Jenkins system was about 10 minutes. So it's relatively quick. It's about 60 seconds to boot once the post completes. And then from reprovisioning servers are already in a cluster to rebooting them, wiping the disks, and redeploying the code to it and all that. It's about 15 minutes. So 15 minutes, you can wipe an entire cluster of 200 servers and be back up and running provisioning instances. So it's really quick. You could probably scale this up to a lot more servers. And because it's going to have torrent in place, it really speeds up things and lets things network pretty quick. Currently, right now, we're testing on OpenPower, code named Barilie. It's basically a Power 8 build that we've been working on with IBM on. We're testing OpenStack KVM with Fedora 23. And it's been working really well. We're still hammering out a lot of hardware issues with the unit, but we're making good progress. And if you want to find out more about Barilie, there's a lot of information on the blog of Rackspace. We recently announced an initiative with Google to work on the next Power 9 platform. So I think we'll have some good stuff coming from that. Future ideas. I'd like to see some embedded configuration management inside the image and just see how that works. Essentially, I want everything to live in the image where the image comes up and knows where to go pull playbooks from to automate that configuration, where to go pull its inventory information from. So really, you could just reboot the instance and reboot the server at any point. It would just go rebuild itself automatically. I think that would be pretty useful. Because then if you can get to that point, you can kind of start maybe getting rid of disks and getting rid of another failure point that you might have in your system. The other thing, too, is you might be able to do stateless instances. If it works for hypervisors and bare metal, it works great with just regular instances. We could potentially do boot from config drive where you network boot from your config drive that we already have, maybe using SysLinux or IsoLinux since config drive's already in ISO. So that might be a possibility. And if you had like 100 web servers, you could essentially just reboot them all and bring up the latest build without having to have a disk. I mean, you could probably find some other place to store your data. If you have a web head, you could have that persistent. But for the web heads, I mean, they're usually just throw away for the most part, and they usually are the same, so there's a lot of things you can do with that. And I actually even tested this on metal or on metal product, which is running ironic. And it worked great. I mean, I just modified the grub bootloader. Rebooted into LiveOS, and it just worked. So I mean, if you want to go check it out, you can give it a try. I got the website up last night. It's squashable.com. It's also on my GitHub. So you can kind of kick that around and give me some feedback if it sounds cool, if it sounds crazy. Let me know. I think it's a really useful tool for just a large-scale environment where you have lots and lots of the same kind of appliances, so to speak. So it works pretty well. And then I have sample boot menus. So I'm using Travis CI to kind of generate images for each one of the ones that we support. So they may or may not work, but you can use the IPXE boot menu to kind of boot into those. And then you can just boot the LiveOS and log into it and just kind of kick it around. So that is pretty much it. Make sure to check out the Rackspace Cantina. They have refreshments and drinks and stuff. Thank you very much for coming. If there's any questions, I think there's mics. Morning, Anthony. First of all, thank you for really your presentation. Thank you. I wanted to kind of take a little different angle and kind of get your thoughts on what Rackspace is seeing. So looking into the industry, we're seeing things like HP Moonshot going after complete ARM infrastructures. And also seeing things like Digital Ocean providing ARM as a new platform. Is there any thought about Rackspace looking at ARM, whether it be in a consolidated environment or ultimately even kind of a scale out? I know we're always looking at multiple technologies. I think right now, one of our main focuses is just on open power. Just because we see a lot of good stuff there. A lot of the BMCs are open. Everything is just on GitHub. So you can actually contribute toward a lot of that. A lot of stuff in the past has typically been kind of closed sourced. So it's really hard to kind of contribute to it. ARM architecture within itself can also lead to that. Definitely see what you guys are looking at, how you can have kind of that multiplier effect by being open. I guess the other half that I look for is, you know, everybody's carrying one around. So trying to play that forward, not only in the consolidated racks like the HPs or the Digital Oceans, but trying to think of where is Rackspace looking at that further. Yeah, we're definitely looking there. The funny thing is, we're on Zen primarily today. So they have Zen on ARM support, but they don't have Zen on Power. So we're actually having to look at KVM for that piece. Interesting. Yeah, it's brilliant. Thank you again. Love fun. Thanks for the question. Hi there. So what's the memory footprint on the server once you've booted the LiveOS? Yeah, I mean, so you can set up the size of the root FS. So I've been setting it up as like 2 to 3 gig or so. I think it actually takes about 4 gigs to 8 gigs, depending on what's being loaded up. But it's actually pretty small. It depends on what you're putting in there. So for just a base OS, KVM, LiveVirt, and an open stack virtual environment, I'm getting about a 500 gig compressed file. So it's not really that big. 500 gig, 500 meg. 500 meg, sorry. Yeah, it's really small. And how much are you using it as a production out of so? How much? We're not using a production yet. It's mainly just R&D. Really what it comes down to is that we run Zen server primarily today. So we haven't, we've looked at doing this on Zen server. We have some examples on that GitHub repo as well. But obviously it wouldn't be supported. And the current versions are sent us five, which don't really have a lot of good support for system D and Draca and all that. Their next version is supposed to be sent us seven, which has all that support in there. So I think we could probably do a lot more work with it. And then we'll probably be able to start looking at doing production. Thanks. No problem. Hey, do you have any guest testing after you reboot a hypervisor to see if guest instances have any issues with a newly booted, you know, oh, a new KVM came up and now Windows doesn't work or something? Yeah, so we did scale testing on this 200 node cluster. We did a test of, we spun up by 8,000 VMs, running Burn-In and all that, you know, just power cycle the cluster, started it back up again, let Libvert start everything up, Burn-In started fine again. We didn't really try to do mass upgrades of like going from multiple versions of Libvert and all that. But that's something we could probably test in CI. I'm sure we could probably just, you know, if we have a new version, make sure everything's solid on that and the same kind of virtual machines work. And we'd probably be OK. Thanks. Any other questions? What kind of demand do you see with power? I mean, it's a cool platform, but like, is there really enough demand to kind of qualify your R&D on it? Yeah, I mean, it's kind of like there's a lot of applications that can take advantage of how many threads it has and just how the performance is on it. So we kind of see it being a really good place to kind of start digging into and looking at. And really, we really like the open platform. We like OpenStack. We like just being able to contribute to our hardware as well. For a lot of years, we've had to kind of work with Dell and HP. And if we have an issue, we are kind of at their mercy. So we kind of want to take things under our reign. And we thought open power was a really good place to kind of start with. Anything else? All right, well, thank you.


And it's a really great conference with a lot of nice talks. I will speak about the rigidity for five endpoint processes. And this is giant work with Alexander Bufetov and Jan Shishu. So first of all, Sasha told us already what the rigidity is, but it was the very beginning of the conference. So let me remind you how we'll speak about the point processes on R. And the corresponding Borel sigma algebra is generated by the set of hash functions where A is bounded Borel. And I will consider the corresponding restriction. I take some set C. And my subalgebra will be generated by hashes where I is inside C. Let's continue. So my definition is as follows. I take some B bounded. And if for any such C, corresponding hash function is F R minus B E. This is the corresponding completion measurable. E is rigid. In other words, here is my R. I take my set B. I take some configuration. I fix the outside. And this is exactly the picture that we've seen at the end of Sasha's lecture today. What he wanted to tell us is, can we see a lot about the corresponding conditioning inside? And my question is much more simple. OK, I fixed the configuration outside. Do I have some invariance? And it turns out that in the case of determinantal processes and when we try to generalize slightly, the processes I usually have only one invariant. And this is the number of points here. Hence the definition of the rigid. Perfect. Now it seems that we've mentioned during the conference several times the orthogonal processes. But if I'm not mistaken, we didn't mention the preference. So let me recall what do we have here. So we've seen several times that if I start, say, with GUI, then I consider the corresponding distribution, which is just the product of the square of the wonderment times the product of the weights. Gaussian weights for GUI. And we know that this is written just, can be written just as the determinant of the, when we have the corresponding Christoffel Darbou kernel for the orthogonal polynomials with this particular weight. Now, if I want to move forward, I can consider GUI, Gaussian orthogonal ensemble, GSE, Gaussian symplectic ensemble. And in this case, what I have here will be modulus times the weight. And in the symplectic case, here we have real symmetric matrices. And here we have quaternionic matrices. I will have the product of the fourth powers. It turns out that we can rewrite it. Not as a determinant, but as a faffin. I will recall what is a faffin in several minutes. Of some kernel, let me put for a moment one here. So this is the first here. This is a better ensemble with better equal 1. And here we have better ensemble with better equal 4. So I will put one here with some xy. And this will be k4 with some xy. And as in the determinant case, I have this formula for the corresponding distribution. But if I have a projection or a kernel of the projection, then I can integrate out my variables one after another. And what I actually get is that not only here I have the determinant of faffin in my case, but for all correlation functions, I will have a similar formula. So it will be kx lambda i lambda j i k from 1 to k. And so we can generalize it and get the definition if there exists such a kernel that I can write all my correlation functions in such a form. Yes, I will tell it just in a second. So what is faffin? I take a skew symmetric matrix. A transposed is equal to minus a. We can some kind of normal form, for example, this one. Then we definitely have that the determinant of a is equal to the determinant squared of b. And as I can get this by elementary transformation, in fact, what I have that my determinant as a polynomial with respect to the elements of matrix is a square of some other polynomial. And this is a definition. I should tell what my sign is. And the usual convention is that the faffin of j is 1, where j is matrix of this kind. And you can see that if the size of matrix is odd, then I have zeros on both sizes here. This is not an interesting case, and so we'll consider only the case when the sizes of matrices are even. We have the definition of the faffin point process, and we can move to the results. There are several results on the rigidity for the determinant point processes. The first result on the sign point process is due to Gosh how Sasha told us already. And there is some general proposition due now to Sasha. And our first interest are the corresponding universal processes, signs, Bessel area. And they are covered by this proposition, as well as some other. If we are moving from the determinant case to the faffin case, then we'll have several difficulties, and as we will see just soon. And so at the moment, we don't have some general proposition at our disposal. And what we can do, we can prove rigidity just case by case. And so our results of first theorem is that we distinguish these two limiting case. We have sign 1 faffin point process and sign 4 point process. These processes are rigid. And our second result is if we can see the corresponding Bessel processes, Bessel 1 point process and Bessel 4 point processes, and then these processes are also rigid. How one can prove rigidity of a point process? Main tool is the following proposition. It's due to Gouche in Paris, as well as the definition of the rigidity. I take any B bounded. And if for any epsilon, I can construct a function f that depends on B and epsilon, of course, such that my f restricted to B is identically 1. And the variance of the corresponding linear statistics with respect to the point process is less than epsilon. Then P is, I recall that the linear statistics is just the sum of the values over my configuration. Yes, yes, I wanted to come to this. So in fact, if we speak about sign process, so our main tool is this proposition. And sign processes can be done using this tool. But nevertheless, sign processes are translationally invariant. And in this case, we also have another nice tool to deal with the rigidity. I'll try to come back to this tool at the end of the lecture. And also for the sign process, we have a more general result due to Chaby and Snadl. It says that, in fact, sign better processes are rigid. OK, if I want to use this proposition, I need some nice formula for the variation. What variation of the SF? It's definitely the expectation of the square minus the expectation. And you see that here we can just use the definition of the first correlation function. And here it's close to the definition of the second correlation function. I want to rewrite it in some peculiar way. And to be sure, let me just rewrite it. So what we actually get is the integral of f squared plus the integral of the second truncated correlation function and minus 1 1⁄2 integral squared times the hated correlation function again. Why do I write it in this particular way? Because it turns out that this term is usually zero. Let me write it as a separate truncated. And this property is called the book of Forrester on log gases. Let me say, just for sure, that the second correlation function truncated is the second correlation function minus the product of the first ones. Here we have the second correlation function if the particles doesn't fill each other. And so now we see how far we're from the independent case. To use it now, we need the formulas in our cases. And first of all, let me remind what is the, let me write it maybe here, what is the second truncated correlation function if I am in the determinant situation. I should write the determinant. And what I get is actually minus k x y k y x. So what is written here? My star is just the statement that integral of k x y k y x d y is equal to k x. That is this perfect screening in the determinant case is just equivalent to the statement that we have a kernel of a projection. And we remember that sine, ary, basso, and we have a lot of examples when we have even an orthogonal projection. What do we have in the Fethian case? And I will write it here. So what about the Fethian? I will probably just write you the answer. My second truncated correlation function is a very nice form. It's just minus determinant of my, I forgot to tell you that if I am in the Fethian point process, I need an even matrix. And the usual way to do it is I just say that I have a matrix kernel. My corresponding kernel is just some 2 by 2 matrix, m 11 by m. So on I have 2 by 2 matrix. It's definitely we can take a determinant of this 2 by 2 matrix. And what we get is the second correlation function. Now the perfect screening in the Fethian case. Now just a very short digression in the determinant case. We have a general theorem of existence, Maki-Soshnikov theorem that we heard probably yesterday. And in the Fethian case, there is no such theorem. And in fact, it seems that there will be no such theorem. And we have either some explicit construction of a Fethian process. Or we can obtain one by going to the corresponding limit. Now we're speaking about the limits. And it turns out that if we start with this orthogonal or symplectic ensembles, then we have some particular form of the limiting kernel. I will write it in case kx will fall because it's a bit simpler. Let me write it like this. Say it will be m xy, m yx. This is the derivative, y, and this is the integral from y to x of m xp. And a short proposition, if my m, if this element, is a projection itself, plus some additional constraints that's always hold, then the whole kernel is also the kernel of a projection. How do we see it? We multiply rho by column. First, we obtain an element like this. And this is just the standard reproducing property. Or we multiply derivative by an integral. We integrate by parts. We always have in our situation some. We have no additional term because everything is 0 at the infinity, and thus this is sufficient. The first and the easiest example is the symplectic sine process. Here we just have minus y over minus y. And here we have the derivative. This is by definition my sine for xy kernel. And we see that in this case indeed what we have here is just the standard sine kernel. So it is a projection. So we have that the corresponding operator here is a projection, and it easily follows that we do have this perfect screening property. And coming back here, we have 0 here. And what we actually have is, maybe I write it also, this blackboard. In this case, my variation is just minus, no minus. OK, I have a nice formula at least for this particular case. Now I want to construct. Let me recall that I have a proposition, and I need to construct a function with small variance. Obviously, it's sufficient to do it just for the increasing set of intervals. So I'll take an interval from minus r to r. I say that my function is 1 here. I will say that it is 0 if x is sufficiently large, or sufficiently small. And it turns out that logarithmic decay is sufficient. Namely, let me write phi of x. So what do I have? It is 1 where I'm on this x. I did that logarithm. Yes, it seems that everything is correct. First of all, let us briefly see what do we have in the determinant case. What I actually have is the double logarithm. Let me put 1 over n plus 1 squared here. OK. In fact, I consider the case when x and y are greater than 0. It turns out that I actually have several domains of integration. And the only interesting part is, as I have different squared, is just the part where I have non-zero term either here or there. It is this one. So what do I have? The difference of two functions of such kind. That is x squared times. Now we can get a simple estimate that what we actually get is just constant over the square of the logarithm. I will rewrite the first part as just. And I will forget about the sign. And let me rewrite this part just as the double logarithm, dy over y. Here I have lambda minus 1 squared lambda. This is from r to t. And I forgot about the square of the logarithm. And lambda is just x over y. So I have one logarithm here, but nevertheless, I have the square of the logarithm and the denominator. And this part is convergent. So tends to 0 when t tends to infinity. I just got some nice estimate of my kernel. And that's it. And it turns out that we can generalize this argument that it works for most of the determinant kernels that we're interested in. Now, let us look at the fifth n case. What do we have here? First of all, we're interested to take the determinant of this two-by-two matrix and put it inside the same integral. What do we get? The square of the sign is just the same. This integral, we can write just as the difference of a constant minus some integral of minus x to infinity. This is of the integral. This is of the size matrix bounded. And what we get after the differentiation, we will have a sine over x minus y squared. We can forget about it. Everything is also convergent here. And cosine over x minus y. What I want to tell you, that if we're interested in the variance of my s phi with respect to p sine 4, then the only part I'm interested in is of the following form. And once again, it's constant over logarithm squared of t. What can I do in this particular case? I rewrite it as n. You can easily see that I can integrate out cosine from this formula. And I obtain once again an estimate of the form. But let us look carefully at what has just happened. OK, now we're happy because we have the kernel of such form. We can just integrate it out. And because of this rapid oscillations, we have an additional term in the denominator, and everything is OK. But if you think about any more general kernel, there will be no such a tool. You will have some oscillations, and you shall deal with them somehow to obtain a result of similar kind. And if we now pass to the Bessel case, OK, let me write you. So this will be, say, m Bessel for x, y, and what we have. All other places, my m Bessel. It is just how to write. Let me write k Bessel to xy. So I have the standard determinant of Bessel kernel here, as in the case of the sine function, plus some additional term. I don't want to write it explicitly. Let me write it like m1 of x, m of y. And it means, first of all, well, it doesn't mean.  Then my k is also a projection. If I have some additional term, it's not that obvious how we can get a projection in a result. And actually, it doesn't. And it's not a miracle. Well, I have a sequence of finite dimensional projections. I'm interested in the limit. And well, what I get in the limit is not a projection. It happens sometime. But nevertheless, what we get as a result, my perfect screening in the Bessel case, it doesn't work. It just doesn't happen, so to say. And in fact, most of these formulas are written in Professor Forrester's book on log gases, initial formulas for the Phef and kernels. And if we combine several passages from several parts of the book, actually, it is written that it should hold. And we expected that it is, but in fact, it doesn't. So we needed some additional step to deal with the first integral, if you remember it. And it turns out that we have a weaker integral version that is just the corresponding integral is 0 in our case. And it is sufficient to deal with the first term. But moreover, let us come back for a second. We have just one summand here that was difficult to deal with. If we have an additional term here, and then we have integration, differentiation, and everything, we have a lot of terms we should deal with one by one or in some more clever way. We have a number of propositions how we can estimate such integrals. But nevertheless, it is a long paper. And so at the moment, only sine and Bessel cases are done. I have, say, five more minutes, right? Of course, even six more minutes, perfect. In fact, if we consider sine process, which is translation invariant with more or less simple formulas, then we can deal with it in several different ways. And it turns out that there is a nice idea to pass to the corresponding Fourier transform. So we can consider the OK. So if something is translation invariant, definitely we have that the first correlation function is just equal to some rho. The second correlation function is the right is just C. And now we can consider the second truncated correlation function once again. We'll be just now we take the Fourier transform, my second correlation function. And it turns out that we have the sufficient criteria for the translation invariant processes. Namely, if we subtract my rho, sorry, we should have my rho. The result should be positive. And how to write? Let me rewrite it this. I put lambda. What I want to say that actually we have only one parameter here. Let me say puts, I don't know, z here, z here. And so only one parameter here. And there should be some linear bound. There exists C such that I have such bounds. And it means that the corresponding translation invariant process is rigid.