Hi, everyone. Happy Saturday. Joe for jaspyscasebrakes.com coming at you with yep We did it 2020 Bowman transcendent collection baseball Random hit break number two now. We turned this into a 10 spotter ladies and gentlemen So higher price point, but chances to get a lot of big stuff Only they only made a hundred cases now obviously since it's Bowman It's gonna focus more on the youngsters, right?  So what's slightly different is that we group the autos together So one person could land right into that autograph spot right there, and you're gonna get seven random autographs So what we're gonna do it. It's gonna you'll you'll see what's gonna happen. We explained it before the video, but Basically, we'll just multiply your name seven times if you land in group a Multiplier names eight times and then do a randomizer between that list and the list of 30 autographs Which will type in and all that jazz right so it'll be a separate randomizer all the rules are right there It's a lot easier than it looks. There isn't there's a visual example for visual learners Right there. Yeah, if it's an R&B spot, we'll just multiply R&B You know seven times in this particular example And then we'll rent you'll get randomized that group will get randomized seven autographs and then we'll go by number Now one person will still get the icon set sealed if an R&B spot lands on there It'll go to the number etched on the box So if it's 81 out of 100 it'll go to one if it's 99 out of 100 it'll go to nine if the R&B spot lands there Of course, these are obvious if the R&B lands here They'll be they'll go to one right or an individual person to just win these The graded bowman buy-back card if it's serial numbered if the card is serial numbered and an R&B spot lands there Works just like a random number block rate If there's no serial number, let's say it's like a old card that no serial number something like that number zero We'll get it The VIP invite obviously one individual can win that or if the number block spot lands there then spot zero will get it Think that makes sense. So thanks everybody Big thanks to these folks whether you bought a spot straight up whether you won spots whether you participated in the number blocks Thank you Thanks for getting in. There's the final number block lists right there one and two you can see the tab on the bottom of your screen one And two this is where I'll type in the autographs right here These are the printouts of the number block lists All right, so what I'm gonna do is we're gonna pause you see on the top of your screen right there We're gonna pop that case open first show you everything and then I'll I'll take the autos We'll show you all of the autographs right because it's a 50 name set But there's only 30 autographs so the could be we can see who's in and who's out and they're all different numbers too So we can see what they're number two as well We'll pause the video type them all in or also show you all the in case stuff as well as well as the cut out Then we'll type everything in when the video is paused then we'll come back Then we'll do randomizing and see what you get matched up with pretty exciting. All right. Thanks everybody Let's pop this open. Let's see what we got. All right, so you should be able to see on the top camera there So it's got those fun pieces there Bowman transcended cases are a little less less bulky this year This cartel you know All right, so on the top camera you can see the event invite which I'll slide over to the main camera There you go Sealed up on the side unfortunately we can't look inside it, but there it is got the MLB hologram seal right there Excellent We'll set that right there It sells the outer case has that MLB hologram sticker on the top left Pretty nice stuff All the all the usual trademark stuff that you find on the back. There's more More sponge right here to protect the case very well protected So you can see on the top camera right there So there's the cut auto the graded cards here. I think that's the graded card And those are either of the one-of-ones right here. This fit right inside this cart. Oh, that's perfect This U-line cart was made for transcended. There's the entire base set Right here. So if it lands on a number block spot, it'll go to four Obviously if an individual person gets it You're gonna get a sealed set, which is pretty nice This right here is the autograph set in the gold frame And then we'll reveal these after I show you all of the autographs keep these on camera right here What could be that cut auto right there? It's gonna be exciting. All right, now these will be an alphabetical order But which let's see which of the 50 there'll be 30 of the 50 right here. There's the front right there Okay. Yeah, that if someone lands that entire set back there. That is pretty that is pretty great All right First one out of the gate is gonna be CJ Abrams 19 out of 25 and we've got Joe Adele 12 out of 25 We should see a couple one of ones in this entire set, right? Then you're done Alvarez That 23 out of 25 Ryan S is there will be two one of ones in here. Okay This one's an out of five. That's a receipt is a keynote Two out of five nice design looks really nice this year or in this set I guess the first time they're doing Bowman and that circle sort of medallion looking thing right there almost looks like a watch There's Joey Bart Seven out of 25 he's been getting a little bit of playing time for the Giants lately Another out of five another train whistle for someone Brett baddie four out of five Then we got Boba Shet 21 out of 25 Do you like this Bowman set a lot of prospects near up-and-coming stars rising stars future stars There's Hunter Bishop five out of five Another giant prospect. Excuse me This is my voice already It's been a long week folks too many breaks There's JJ Bladet six out of 25 and we got a Jason Dominguez Two out of ten That blue looks really nice against you sort of that sort of navy blue of the Yankees. That's pretty sweet We've got wander Franco four out of five Nice seeing a lot of low numbers here. That's good We've got Nolan Gorman four out of ten and after we do the randomizer We'll we'll we'll take another look at the autograph and all the hits again We'll do another sort of recap at the end. All right, then we've got Riley green 15 out of 25 And we've got Cabrinha Hayes nine out of ten Pittsburgh pirates Pirates prospect you got Nico Horner Six out of 25 and the frames look really nice this year to nice clean frames clean design Good-looking set and there's our first one-of-one at Sam Huff Rangers prospect Sam Huff and that's sort of sort of rose gold Design so you can let's see if there's a gold example. There's there's your gold example, and that's the sort of rose gold Example there for those of you collecting this at home these 180 top loaders are a little loose But they'll they'll all be taped up blue-taped painters tape before they go out another ranger Josh young ten out of ten 14 out of 25 nice Jared Kalanick big Mariners prospect, of course We've got Nick Lidolo eight out of 25 and we got a Gavin Lux 16 out of 25 Nice Gavin Lux got called back up again today From the alternate site. I feel like they're gonna shuttle shuttle him up back and forth Although Justin Turner did tweak his hammy So he might get an opportunity to shine a little bit this season he was really good Yeah For my Dodgers minor league team there. All right Moving on Daniel Lynch Two out of ten always cool when the blue matches the blue of the team. That's always sharp Yeah, I guess if we're going alphabetical or no, no Kyle Lewis Got Casey Myers made his debut the other week last week 12 earlier this week 12 out of 25 Tigers hoping he'll be their next Justin Verlander He's really good. He's got good stuff All right, we got Christian Pash six out of 25 He's supposed to be I mean all these good guys are supposed to be really good, right? It's a good collection of prospects here We got Nate Pearson 22 out of 25 Heart-throwing pitcher for the bluebirds Buffalo blue jays He's all about and we've got a Louise Robert 18 out of 25 Some great photography there. I love it. We know what he can do We've seen him do it White talks playing some good baseball just in general the last week or so. There's Julio Rodriguez eight out of 25 Mariners putting together a good collection of youngsters Then we've got seven out of 25 Tariq scoobal Also made his debut recently because these kids aren't getting aren't getting a Minor league playing time So we're starting to see a lot of these future stars being called up and given some some playing time for the big club Scoobles pitching today says Oliver there you go. So keep an eye on his performance. We got Alec Thomas five out of ten for the snakes a couple more to go We got Taylor Tremel two out of ten For the Padres With the Reds for yeah, he was with the Reds for a little bit All right, and the last one is gonna be yep. You guys guessed it, right? It's a one-of-one Bobby Whit jr. Wow And that sort of platinum foil I've said this before I'll say it again I saw fan graphs article where where they they compared him to a good comp could be someone like Trevor story So we'll see how he develops All right, there you go So those are your autographs. I'll type those in in just a moment, but let's check out what the other stuff is All right, so this should be you can see on the top camera right here This should be one of the one-of-ones Slide that over to the main screen. So this is gonna be the oh Yeah, the mid-50s Bowman television design one-of-one and it's gonna be oh, it's gonna be Robert Possen one-of-one His name is Robert Possen His name is Robert Possen This is the other one-of-one Sliding over here. Maybe this way. Is it vertical? All right, we've got an RC on here This is the other one-of-one. It's Yordon Alvarez. It's either gonna be Yordon or Vlad, but here's a Yordon. I got Yordon last time I was hoping for a Vlad. That's all good. So nice big prospect Getting surgery on the knees below the frame right here And then hopefully we'll be back strong Nice This should be the graded card See can we can we look at the grade without seeing who it is? 95 no auto grade. It's oh, there's the name Shohei Otani Wow in his world baseball classic gear No H in his last name either Nice from 2017 Bowman Chrome Mega box prospect refractor 9-5's on the centering edges and corners surface a 10. That's pretty cool. All right now But wait, there's more The cut auto Which way you're gonna go? Which way are you gonna go? Okay all are Robinson as in Jackie Rob? Wow Jackie Robinson cut auto Whoo, I'm actually wearing an old Kansas City monarch's hat right here Jackie Robinson weekend as well Right all the players are wearing 42 RIP Chadwick Boseman also played Jackie Robinson Wow, that is amazing. So one person's gonna get that that's gonna be a lot of fun. All right ladies and gentlemen There it is. Let's slide that over there. You know, there it is peeking out in the corner right over there Nothing else. Hard to see against the black, but There's nothing else Anything secret underneath? No, nothing. Nothing secret underneath. All right. So here's what we're gonna do I'm gonna pause the video. We're gonna type in all of these autographs. We're gonna come back We're gonna run the randomizers and see who ends up with what thanks for watching stick around We'll be right back. All right. Welcome back folks. So as you can see on your screen, we've got the autographs all in there All 30 autos So now here it is we're gonna use that one dice roll for all the randomizers I think there's gonna be like four randomizers, but list that we have to randomize It's all gonna be one dice roll whether it's snake eyes or whether it's 12 So first of all, just so I can kind of talk this out a little bit with you guys We're gonna randomize your names and then match you up with that then we'll pause the video again because I have to type in X amount of names Seven eight seven times eight times seven times eight times then I'll put the list of autographs in another blank list and Then we'll do use that use the previous dice roll the same dice roll to randomize the full list of names and the full list of 30 Autographs and then match you up with something there, too Make sense. All right, here we go. So the dice roll for everything 11 times 11 come on around work five and a six 11 times a little bit lower than that would have been nice But almost full suspense five and a six your names first I wanted like four one two three Four and 11 the final time After 11 we've got Aaron Pettit down to number block two 11 times for everything 11 times for these hits right here one two three four five six seven eight nine 10 and 11 the final time after 11 times We've got the cut signature Wow down to the graded card Look at that so Aaron Pettit who won a spot in that sterling mini box break you get The Jackie Robinson 1955 Bowman baseball cut signature Wow All aboard Man, I will take a look at that again in just a second random number block one. You get the Bowman set. What was that set number two? That was 84 out of 100 84 out of 100 so That will go to glow bug. There you go Ross you also want to spot in that sterling mini break you get seven transcendent Autographs we're gonna figure out which ones you're in group C We're gonna have to do that a little bit later Chris you get the party invite Let us know if you end up going Let us know what happens there Danny you get eight autographs John Garrett. You'll get eight autographs Chris You'll get seven autographs Mike you got the it ended up being your dawn Alvarez Through the years So there you go you get the one-of-one All aboard the Big Head Express And John Garrett you get the 1955 Bowman baseball superfractor autograph one-of-one, which was the Robert Possen his name is Robert Possen John Garrett All aboard the Big Head Express And the graded card the Ohtani goes to number block two that was not numbered Yeah, no serial number on there So as we said right here if a number block spots landed there And if there's no serial number then it'll go to spot zero So here in number block two. That's Alex W. There you go. You get the Shohei Ohtani The old world baseball classic uniform Nice, so there you go. So before we randomize the autographs. Let's take a quick look So Alex W. Yeah zeros with the with a nice win right here So Alex You'll get this one So congrats to zero right there And then for the set 84 out of 100 right there. That's another different Alex Glowbug in the chat So you'll get this sealed set right here, which is nice a lot of value in here, too Let's see Mike There's your one-of-one you're on alvarez pretty sweet and John Garrett nice Robert Possen No worries Glowbug. Yeah, you were just talking about how nice that set was too and you ended up getting it All right and the invite of course Chris let us know if you end up going I guess you could tell it too. I don't know where it's gonna be I'm sure Due to due to the pandemic they may they may push whenever that event is that might be TBD But still could be really cool. Sometimes they have it in Vegas. They usually have it in Vegas So it should be pretty nice. All right, so now time to see who gets autographs, right? So we're gonna pause the video again and get all this sorted out So Chris your name will be first. There'll be seven Chris's then They'll be eight Danny's then there'll be seven Ross's and eight John Garrett's and we'll put you all together right there and We're gonna see who gets what so stick around we're gonna pause again. Oh, yeah, you want to see the Jackie Robinson again That's right. I forgot to show Aaron Pettit. He's Jackie Robinson again. Man, that's an excellent one Beautiful as a Dodger fan as a fan of Jackie Robinson Jackie Robinson weekend basically Everyone's wearing 42s That's beautiful Alright, so congrats again to Aaron Pettit. Okay, so again, I'm gonna pause the video when we come back We're gonna have all these names typed in and Then we'll see who gets which autographs and then we'll do another autograph recap to stick around again. We'll be right back All right, welcome back. So now we've got this list right here So remember Chris gets seven autographs. So if you look at the count Number in the bottom right hand corner of my spreadsheet right there. It'll say seven. That's group a group B is Danny with eight There's Danny eight times right there group C Ross seven times and Then the final group group D John Garrett eight times right there. So that's a total of 30 right there. So let's copy that list of 30. Let's put it into this blank list here and Then let's go back to the autograph list that I had Right there. So there is 30 already You can see count 30 right there on the bottom. So there's a 30 autographs and We'll put them in right there and Then and we're gonna use that same dice roll again 11 times and then we'll match you up in the final auto list Good luck five into six 11 times your names first one Two and 11 the final time All right after 11 we've got Danny on the pole and John in the 30 spot after 11 times and then the final one 11 times for the autos one two 10 and 11 and final time. Good luck. We got Joey Bart down to Tariq scuba with a Bobby Whit jr. In the second position All right. So there it is Danny you got the Joey Bart seven out 25 Chris with the Bobby Whit jr. All aboard the big hit express We got John with the Brett Beatty to 25 Ross with the Aquino to 25 Danny with the Nate Pearson John Garrett Hunter Bishop All aboard the Big Hit Express Ross you get the Jason Dominguez two out of ten nice Chris with the Joe Adele 12 out of 25 John with a two out of ten Daniel Lynch and the Wander Frankel four out of five all aboard We've got Chris with the other one of one the Sam Huff all aboard the Big Hit Express Ross with the 18 out of 25 Louise Robert, that's awesome. You got the Robert and the Jason Dominguez Danny with the Casey Meyes John with the Riley Green Chris with the Cabrion Hayes Ross with the Taylor Tramel Christian Pash for Danny Josh young ten out of ten for Ross The Kalanek for Chris Danny gets the Alec Thomas and the Yordon Alvarez Nicola Dolo for John Garrett Nolan Gorman four out 25 for Danny John Garrett with the JJ Bladet Ross with the Julio Rodriguez and the Boba Shed Chris you got the Nico Horner and the Gavin Luxe CJ Abrams goes to you Danny and John Garrett with the Tariq Scoob all that is very nice So there it is Here's another quick autograph recap ladies and gentlemen Thanks everyone for watching and for getting into 2020 Bowman transcendent baseball Random hit break number two the 10 spotter We appreciate it Think everyone and hope everyone ended up with some nice stuff if you're able to get some hits We're gonna try we're gonna try to get some more as always, but they don't make very many and Not not too many fall into our laps, but if one does We'll be sure it'll be on jasky's case breaks calm keep your eyes out on our website for that Thanks for watching Party invite the one-of-ones that entire set global. What are you gonna do with that set? Could fetch a lot on the secondary market could be a keeper. Maybe that's all they're all prospects in there And of course my personal favorite as a Dodger fan as a fan of Jackie and all they stood for That's pretty cool, too Nice. Thank you very much everybody. I'm Joe for jaspies case breaks calm. Thanks for watching Thanks for breaking with us and I will see you next time. Bye. Bye


Okay, I think we're going to get started if everyone's okay with that. Hi everyone, thank you for coming to our talk today on operationalizing sustainability in Kubernetes. In other words, how to care about the planet from behind your desk. Just to give a brief intro. So my stunning colleague on the right here is, oh, I see two stunning, is Gabby. Gabby is an amazing platform engineer, incredible system engineer and a very mediocre snowboarder. This is why she's actually interested in saving the planet. She needs all the practice she can get. Well, he's not wrong. I'm better than him. Brendan is a very versatile engineer, front-end, back-end, networking, telecom. You name it, he's probably done it. And well, his passion for knowledge and bettering himself and those around him, which really feeds into his work that we do at Resync and his own personal blog called the GreenCoder.io. So that's a little about us. And we work for a company called Resync. So this is kind of why we wanted to do a talk on this specific topic. We've been doing research into just the impact on the climate that IT has had as part of our work at Resync. And Resync is just a consultancy that's looking to reduce IT-related impact on the environment. And mostly just consulting on best practices. Great. So before we dive in, we're going to get on the same page and provide a bit of context, specifically, why are we bothering to talk about this? What are CO2E or what is CO2E? And how do you measure sustainability and how do you measure that in a full system and in a virtualized system? So just bear with us through the theory. We do have some pretty cool experimentations at the end. So to start off, what is carbon equivalence emissions? So generally, we talk about carbon equivalence, which is an overarching term for any kind of greenhouse gas, so methane, nitrous oxide, carbon dioxide, obviously. And we call it carbon equivalent specifically because we... Yeah? Sorry, first time on stage, just FYI. We call it carbon equivalent because essentially we measure all of these gases in terms of their heating emission compared to carbon dioxide. And obviously everybody's heard of the big bad carbon dioxide. Now, if you see any of these two symbols, these designate any greenhouse gases and not only carbon dioxide. Right. Why should anybody care about this? Well, the IT sector currently uses around 5% to 10% of the world's electricity. We then change that into greenhouse gas emissions. That's 2% to 5% of greenhouse gas emissions. Now, at scale, that's a monstrous amount. When we moved to the cloud, we had this idea of the infinitely scalable cloud. You can pretty much set up any of your applications in minutes. We don't really care about optimization as much. Obviously, it does come into effect at some point. And with this in mind, we've slowly started seeing this huge scope creep of redundancy and waste of server utilization. To us, we're hoping that IT should be part of the solution and definitely not part of the problem. And essentially, we have to face the facts. Paper straws just aren't going to save us. So great. Now we know what carbon emissions are, but how do we measure them in our systems? Because without the ability to measure, it becomes challenging to optimize and reduce since we won't have the means to quantify those changes effectively. So first, to calculate the total carbon emissions, it's composed of two parts. The embodied emissions and the operational emissions. We're aware of the SCI. This is very similar to that, but just without the rate because we didn't require the rate in our calculations. So we just focused on these two components. And so Brennan's going to talk about what embodied emissions are. All right. So we've kind of gotten into this mindset that carbon emissions is a synonym for electricity or power usage. And unfortunately, this misses a huge part of the puzzle. When we're looking at IT hardware, we're essentially missing a huge percentage of the emissions as part of the manufacturing life cycle and supply chain to get the hardware where it needs to be. We look at a general server that's used by a lot of cloud providers, the Dell PowerEdge R740, and shout out to Dell. They have an amazing research on every single hardware component that they push out and the emissions that it produces. And in this case, we're looking at about 15% to 16% of the emissions as part of the manufacturing and end-of-life parts of the server. Now, this is based on a... Oh, obviously the question is how do we reduce it because we don't really have control over these emissions. This percentage is based on a four-year life of a server. Now, four years was the default a couple of years ago for most data centers. And what a lot of the cloud providers have started doing, or data center providers, is they've increased their server lifespan by two years. So Google Cloud, AWS, and Azure all have the same six-year life cycle at the moment. And this has reduced the embodied emissions of those servers by about 4% to 5%. Now, AWS specifically has actually reported that just increasing the lifespan of their servers has given them around a $900 million benefit in profits because they're not spending that money on upgrading servers. Well, in any industry you obviously get over achievers, there's actually a company, there's a booth downstairs that you can go and visit if you have questions. They've figured out how to increase their server lifespan for up to 10 years. And this is Scaleway. Now, Scaleway did a very big research on all their data centers and found out that in most of the instances, the component that failed in their servers was the RAID controller. And this is due to the RAID controllers having batteries in them. And nowadays, a lot of the servers don't actually require hardware cards, right? So by removing the RAID controller and retrofitting these servers, you can actually push the lifespan of these servers up. Now, they did this by around 14,000 of their servers and there's an awesome blog post about it. And this is also one of the strategies that they've incorporated to try and get ahead of the worldwide chip shortage that we're seeing due to our friends in the AI department. Okay, and now that we know what embodied emissions are, we're going to look and talk a bit about operational emissions since these will be what you probably have the power to optimize over the most. So operational emissions can be calculated. They're essentially the workload you're running on your server and they can be calculated by the energy consumption, by a grid carbon intensity coefficient. Yeah, that's a mouthful. We'll get to that later, but first we're going to talk about energy consumption and that's the amount of power being utilized over a period of time. And you can get that for a server in a couple ways. I'll show you two, but there are definitely more like EBPF if you've been to the Kepler talk and other ways. But one of those is smart plugs. They're probably the most precise measurements because they can give you the entire power consumption of whatever is plugged into it and can give you the consumption of the entire system. They have built-in Wi-Fi and API. And a second way to measure is called Running Average Power Limit, or RAPL for short. This was developed by Intel and it's a hardware component on your chip and can expose real-time power metrics of different power domains like CPU, GPU, and RAM. It's exposed by the power cap framework, which is an interface between the kernel and the user space. And so if you ever want to know if your system has access to that, you can just look in the power cap directory. And if it is present on your system, this can be utilized by various tools, including the Prometheus node exporter and another being Linux Perf. And so as you can see, these are some of the domains that you can measure. And we're gonna do a little measurement of my laptop and we'll focus on the energy package domain because that is the energy consumption of the entire socket. So pretty much the cores, the GPU, and the on-core components like the L3 cache and the memory controller. So it's as similar as measuring the whole system as you can get based on that. So I ran this on my laptop the other day and used StressNG to force a load of 85% over 10 seconds of time. And as you can see, it reported 313.49 joules of power was being used. So now that we know how to get energy consumption, let's look at the grid carbon intensity coefficient. So this is a coefficient that represents how many grams of carbon dioxide are being released into the atmosphere per kilowatt hour of electricity being created. So it's pretty much the intensity of your carbon. If it's a higher number, that typically means the electricity created is probably from a coal plant or another not-so-great source. However, if it's a lower value, it probably depicts wind or solar power and that's what you wanna aim for. One way of being able to get that value is a tool called electricity maps. They provide real-time data of the carbon intensity for a specific region at a period of time. So on Monday when I was running that PERF command, the carbon intensity was about 40 grams here in France. And great, so we're gonna put it all together and get the total operational emissions based on the Rappel command and the carbon emissions, or the carbon intensity. We first convert joules to kilowatt hours to go from power to energy and then multiply that by the coefficient and we get 3.4 times 10 to the negative 4 grams of carbon from just that little running. So now we know how to do it for a full system. We need to talk about virtualized systems, right? Because in most instances we're talking cloud providers, data sensors where we don't actually have access to the underlying hardware components. So we have checked cloud providers aren't really keen on us walking into their data centers and starting to plug in some smart plugs. We're still waiting for responses. Even though Rappel is quite accurate, most providers have disabled access to it. There's a CVE where privileged users with local access were able to access unauthorized disclosure of information. So in virtualization we need to start jumping to the different components that actually use power in any system. This is mainly the CPU, the memory, the storage and networking. Storage and networking do play a part in power consumption, but in any system majority of the power usage is memory and CPU. This is depending on what system you're looking at and workloads, of course. We also have to face the fact servers are complicated. There's different compiler flags that you can run with servers. There's different hardware components, different energy profiles, and unfortunately cloud providers aren't really forthcoming with all the different configurations that they have set up underneath. So what the tendency for a lot of tooling in this area has done is they've started using machine learning models based on a database called the spec power database, which is just a huge database of hardware components with their full energy profiles. And using this idea with information that we actually do have around virtual machines, we can then, such as the CPU type, the memory type, different utilization levels, we can then run that information through these machine learning models to get an estimate of the current power consumption of a server. We are seeing different machine learning models being used, so I've seen K-means, SVM regression, not so much linear, like full linear regression models. However, all these models, we are still a bit in the dark due to lack of data from the cloud providers. I'm iterating that in case there's anybody that works for cloud providers come chat to us afterwards. To sum it up, how do we measure power consumption in emissions in our systems? We make a whole bunch of assumptions, a whole bunch of estimations, and whatever data we can glean from cloud providers. Hint, hint. Okay, now that we've chatted at you for a while, let's get to the good part on how you can actually optimize your clusters for carbon emissions reduction, reducing carbon emissions. All right, so we've got this idea of time shifting, which we'll go through first. As an industry, we've run cron jobs for, I don't know how many decades, and the idea behind time shifting is we slowly move our workloads to shift to schedules that coincide with lower carbon intensity. In any given day, so this was a Monday morning, and as you can see, the carbon intensity does fluctuate throughout the day. That nice big orange spot over there is otherwise known as the sun, and happens between 7 a.m. and 7 p.m. Not in Amsterdam in winter. However, with such a green energy source, we do also notice that carbon intensity is much higher during the day between these times due to most of us working. So literally just shifting your workloads to later on in the evening, so between 7 p.m. and 7 a.m., you do get a drop in carbon intensity for your workloads. We also have this idea that not all workloads can shift in time, so if you do need to run your workloads at a very specific time, you can shift your workloads to run in different regions that do produce greener energies. With my little experiment, we had the Netherlands Monday morning when I started working. We had a 404 gram carbon intensity, and if I was to run my workload, that's essentially what my workload would be using. If we shifted it to our northern neighbors, still in Europe, still under GDPR, I do understand that running your workloads in different regions does come with some compliance and security concerns. We have a carbon intensity of 24 gram per kilowatt hour, right? Now, exact same workload, all I'm doing is shifting it from Netherlands to Sweden, and I'm getting a 95% reduction in carbon emissions for my workload. With that idea, or with these two ideas combined, we get this follow-up of the sun methodology, which is very much stolen from our support engineering colleagues where you've got an engineer in every time zone to follow the support and follow the sun in emissions. We're talking about the software following wherever the carbon intensity is lowest, and this is being designated as carbon-aware software. Your carbon is actually aware of the carbon intensity that it runs on. This is one of the four running ideas in the industry to reduce your carbon footprints, and we didn't really feel like going too much into implementation details on this because it is very, very, very, very well documented. Yeah, so then we decided to think, well, how would node utilization come into effect with this? And it was observed that a majority of utilization is only sitting between 10% and 20% on servers. So this graph may be from 2009, but we found other data that was very comparable, like McKinsey and Company found global utilization falling around 15%, and then in 2022, IBM found that server utilization was only falling in between 12% and 18% of capacity. So not only is that limited utilization a waste of idle resources, but let's look at how it affects the carbon emissions. So when comparing the way of utilization of based on power, it's easy to kind of assume that they would be linear, right? The more workload you have, the more power you would need to power that. But that's actually not correct. The rate of power consumption as utilization increases actually decreases, which means the more utilization, the more value you'll be getting out of your energy consumption. And so we wanted to show how this played into effect with a little experiment. So just to set the stage for the experiment, we tried to keep as many variables consistent as, you know, we wanted to make pretend scientists, and we created a single cluster, single control plane. We used Google's Microsoft versus demo just because we felt like it's a good representation of workloads running nowadays. We set the region of Europe West 1. We turned off any autoscaler. We had two node pools with a single node in each. One of the nodes had four VCPUs. One of the nodes had eight VCPUs. And we removed all the resource limitations on any workloads that we were running. So they could essentially grow and shrink as they needed, as they wanted. We then scaled the load incrementally in tens and we left around 20 minutes between each scale to see the effects that would occur. Yeah. In order to measure, we used a CNCF tool called Kepler. Kepler is an EBPF based power, it measures the power on your Kubernetes cluster. Sorry. We also decided that we needed something that also measured the full underlying system. Cloud Carbon is a terrible name. We're looking for a new name and we appreciate that. But essentially we created our own tool that measures VM utilization and converts that to emissions as well. Great. So as Brendan said, we used the Google Microservices demo and it comes with a load generator and we started with one instance and then we incremented it by 10 instances every 20 minutes. And so as you can see, as time progresses between the first to the 10 instance jump, it's about 400 watts and 0.4, sorry, 0.4 watts. And then as you see between like the 50th to the 60th instance, when we made that 10 instant jump, it dropped to about 0.1 or 0.05 watts of energy. So it's showing this, it's showing that nonlinear rate of growth and rate of change and the arc is very similar to that energy proportionality graph. So in other words, the more utilization, the more value you're getting out of your energy consumption that are for the planet. Now this was where we started bringing in our tools. So Kepler specifically measures like containers and namespaces in Kubernetes, right? So if we look at the previous graph, we notice that the increase was very similar because it was actually measuring the exact same workload. What we wanted to measure was the underlying virtual machines of each. Now the blue line at the bottom is the VM with four VCPUs and you'll notice that it has greatly less emissions than the VM with eight VCPUs. Yeah. Now this is because more VCPUs equals you need more energy to run it, right? As we increase, the increase is again proportional but not linear and we get to a point where the smaller node actually started flatlining at 100% utilization. We don't recommend this for any workloads ever. So this was just an experiment but the bigger node actually had a lot more space to grow. So as the utilization of the bigger node increased, it could still increase in energy consumption. Which, yeah, we don't want you to flatline your nodes to run them over 100% utilization. So we wanted to see how autoscaling would take effect and change this information. So as you know, autoscalers is pretty recommended and well utilized in Kubernetes both in the way of scaling up to handle load and scaling down to not waste resources. And the autoscaler typically makes these decisions based on the resource requests that are specified in the manifests. So what we did with our experiment is we enabled autoscaling but then we also set the resource request for that load generator deployment to be 300M. And this is because this is what Google specified to be and we figured they know their application better than we do. Let's go with the default and, yeah, we ran the exact same experiment. And these were our results. So the bigger nodes are the green and the smaller nodes are blue. And as you can see, the smaller nodes had scaled up to 5 and the larger nodes only scaled up to 3. But the total emissions or the energy being used was about 5 watts for the small nodes and the larger nodes only hit about 3. Whereas before you see it only got to about 1.6 watt hours. So, yeah, utilizing more resources is causes more energy consumption especially because the CPU utilization was only sitting at about 20-30% and that's leaving a lot of room for more growth. Yeah, more commands. Yeah. So then we thought, okay, well let's try to set the resource request to 100 and see then how that will manage with the autoscaler. And as you can see, it performed a lot better. There are a lot fewer energy being consumed and the CPU was being utilized a lot more with 50-60% utilization and only the smaller nodes increased to 2 and they reached about 2.5 watts of energy consumed and the big nodes never even had to scale up. So with that utilize your nodes more. Take the time to analyze your workloads and understand your resource requirements and what you need. Just throw resources at your clusters because you can. Yeah, really take the time to utilize them effectively and get the more value out of your energy that you're using. And I would also just like to point, when you do utilize your nodes more, I mean exact same performance, your applications running nicely, but you're using about half the amount of energy that you would in an optimized environment. And yeah, the fewer loads, less carbon. Let's just hit that home. We are ongoing with our research. If you have any feedback, any comments, any questions, please reach out to us. There's all of our handles LinkedIn. We're a friendly bunch no matter how we look. Resync.com is our company. We have a blog there. We have my personal blog, the GreenCoder.io where we're just trying to get the information out there for everybody to be able to take control of their climate-related impact. Yeah, we would love to collaborate and answer any questions. Yeah, and then just a quick thank you. Obviously, talks are quite intense. So thank you to Anna and Jessica for all their help. And yeah. And our team at Resync. And that's it. Hi. Seems like the number you put it on, the 8 to 10% of the ITC sector consumption sounds very high. So do you know where the source of that data? I actually got that from the EU, the EU's European Parliament websites, I believe. We do have links to any information that we've shared. So we will share the slides. Okay, that would be great. So I think there are multiple sources. Sometimes they're saying nature papers saying different stuff. So it might be really great. Thank you. Hi, you mentioned your tool being cloud carbon. Yes. Could you expand a bit more on the shortcomings of Kepler and why you needed the other tool? And is this tool open source or on GitHub or something? Yeah, so we don't actually see any shortcoming with Kepler just to be frank. Kepler measures Kubernetes clusters. So namespaces, containers, pods. What we found is, especially in the cloud environment, this doesn't necessarily mean that it measures all the underlying Linux processes that are also running there, right? We wanted a more a bigger overview. So any overhead that you need to run your workloads and that's why we created cloud carbon in the cloud environment. So we wanted to make sure that we had the same pending.  Excuse my language. But yeah, it's 100% open source. All of our data is there. We really have tried to make it as transparent as possible in case anybody does want to do the same as we've just done. Thank you very much for great talk. You mentioned time shifting our workloads to a more optimized time like later in the evening maybe. But in our case that would sometimes require maybe not scaling down to zero which could be a bad thing. Do you have any numbers on that or how to calculate that maybe? So I think the key here is measuring your workload like the energy consumption Kepler great project to do that. And just kind of doing that research on how much of VM actually uses to run versus scaling up your system. I mean the optimal solution and I am going to say it is to just turn everything off. Companies don't like that answer so it is something we're actually looking into. We're going to take a lot of this and start posting blog posts on different things that we're researching and this is definitely something that we will be looking into. Thank you. Done? Yeah? Yeah. I would like to know if you started any reflection on the useful workload. What I mean is that most of the time you have, let's say your application workload and then you have the rest meaning metrics stuff like this and is there any indicator that you are reflecting on that talks about percentage of useful workload? Business value versus business support. Yeah. We have started looking into this and I think there's actually quite a big push to actually look at this. I think it's a good idea to look at some of the things that we're doing groups to condense modern tools to be like one or two agents per node that you're not running like five agents for your logging, your telemetry, et cetera, et cetera. This is definitely an indicator that we are looking at but unfortunately business support and business value do go hand in hand with any kind of numbers towards that. I think in general open source tooling needs to maybe come together and try and lessen their footprint because that scale I think that could be the best option for the specific problem. Yeah. For the spagamins that you run, what was the OS you used for the nodes because I was wondering if this big chunk that you saw was used even without workflow had to do maybe with high resources of the operating system and if you did any research like changing operating system if you find one which was more energy less energy hungry. We haven't done any research to that side. That would be really fun. I want to try to do that maybe next. I think we used the container optimized for the Google's container D runtime. It's like the read-only file system with it's cause, right? Yeah, I don't know. Forget the name but it's essentially the one that just runs the containers and nothing else. That's a really good question. I'll get back to you. Hi. Thanks for the great talk. I have a question regarding the shift your workloads approach to maybe Sweden. Have you any take on automating the process because it sounds kind of hard to do this manually like your provision resources in your cloud provider and maybe your saving plans and something? There is actually the Green Software Foundation has a C-Shop library or .NET library I think called CarbonAware SDK to incorporate this and there's a lot of research around it. So I think it's still in its early stages but I do see it's seeing traction, especially with the European Parliament's new laws coming in that companies are going to want software that does kind of go down in carbon intensity. So I hope that answers your question. Okay, thanks. It sounds like the cloud provider is in charge for this but I think we need to integrate those features because if every user who uses Kubernetes in the cloud I think it would be very hard if everyone develops their own solution for this. 100% and again calling on all cloud providers to be more transparent with their data. I think the one step down here. Thanks. Cool. Good. I know it's lunchtime so we can go eat. Thanks for coming and thanks for listening.


Good evening, everyone. Actually, it's evening. Good evening. Good evening, everyone. This is Melissa with TheStuckSwitch.com. And here I am tonight doing a market video for you. Thought I would look at this just for so, even though I didn't trade today. And I'm not going to be trading over the holiday, but let me tell you something. While I'm away, the market will be rallying and rallying and rallying and rallying and playing out exactly the way that I thought that it would. And it's starting up today. Actually, the market is starting up today the way that it held up. And here, beautifully, is starting up to break out. Tomorrow is December 23rd. Then the market is open for only a few hours. And December 24th. Then the market is closed December 25th for Christmas. And the market is going to break out. So, given the timing, it's probably going to be tomorrow. The market is either going to have a slow, slow, slow crawl every day this week, rallying, rallying, rallying, slowly, slowly, slowly, slowly, slowly. Or market is just going to break out higher and shoot up like crazy up to 210 and then to 12 and all the numbers in the next two weeks. So it's really amazing because the fact is that the market is higher. I knew that it was higher and it's going to do this now in a time of the year when it's low volume with this like such low volume. Although I will say that look at the volume we had here today. Wow, look at that. 147883242. And the low, here, listen, this is very important. The low of the day today was 20646. Okay. High today was 20747. Open 20675. And the close is 20733. That means from the open, listen, from the open today and to the close of this evening's market time. The market traded in basically a 60 cent range with that kind of volume. That's incredible. This is what I'm talking about, people. This is what I've been talking about. This part doesn't look like much and yet it is something. It's a gap up. Market gapped up today. Market gapped up in the open. Held in the air, mind you. Held here and rallying and had tons of volume in it today actually and barely had a 60 cent move. This is setting up to break out higher. Crawl, crawl, crawl up to the numbers until the end of the year. Either way, this is, I just saw that there, that volume. Look at that. Wow. So what could happen is the shorts could get, people are short this market, which, you know, it's crazy. But people really think the market's going to crash. It's not. The market's higher, higher the rest of the year and higher into 2015. And the tone the market sets here in the next two weeks is setting the tone, setting it up into 2015. It didn't have to play out that way. The market could have rally earlier in the month, the numbers continue rallying. The market couldn't come in before the end of the year. It's not going to change the way this is setting up. The way the market is setting up for 2015 is extremely bullish and it's going to carry through. And also the market's not extended. I've been talking about this for months, for so long now. I mean, March, I just started talking about this, I think. March. March, I did a webinar. You can trial back on the YouTube videos when the market did this. And people said the market did a double top and a triple top and a quadruple top. And then we had two red bars and everybody pined and said the market was extending and it crashed here. No, it's not. And I have it in the webinars and all the YouTube videos. And look what we did after that. And we're still going to continue. And we still are going to continue. And 2015 is going to be one, if not the most bullish years that the market has ever had. I'm saying this. I mean, I'm saying this because it's going to happen. And that's amazing. When most people are thinking the exact opposite of the market's going to crash. So how is it I'm able to see this and reading the gaps? And once again, I'm reading the gaps in the market. Which ones are the good ones? Which ones are bullish? Which ones are bearish? Which ones are strong? Which ones are weak? This is the sign of strength for the market today. Market closed yesterday. Gapped up. Gapped up this morning. On a Monday morning, on a week of the holiday, you had that kind of volume with a 60 cent range. And the market is setting up to break out and go higher. 100% conviction. What a beautiful chart. I mean, I could not draw this better. And you could actually be on the market overnight here because of the ways to setting up.  The market will be a snail all week, really. Lesson has a big, big day, but I wouldn't put it past it. So it could snail its way up to the target or just basically climb up there in one big green bar. I mean, I guess you could have done this here with a tiny, tiny stop, but I probably wouldn't give it a cushion. So the risk to reward here in the market actually is for the longer term. Longer term meaning weeks and months in the next year. I mean, the risk to reward, if you're a person that day trades, okay, the risk to reward for the market, the marketing ETFs, the spies of marketing ETFs and the QQQs, I look at this too, the risk to reward here is really for the longer term. Not so much for the day trades. Stocks, companies, I day trade, they're going to have a lot of them. That's on volatility in 2015. But as far as the market itself is concerned, you may have days like today, but if you hold something, enter something, carry it through, you're going to see a lot of profit. But people are, people, people get out. I mean, people, people were long the market and got out. People got out here, people got out here, people got out here, here, here, here, here, here, people got out here. And I talked about this is a trading room. I said, this is an institutional selling. Why is everyone panicking? I can tell the difference. I can tell the difference when I read the gaps. I can tell the difference when I see how it trades live, which I did not see the market trade today live, but I can go back and look at this, but I can, I know the way this was, it was a slow and steady grind here today. The market grinded, grinded higher here today. That's how, that's how it acted if I'd seen it. I know it. Anyways, I was watching the market in here while I was active. And you know, this was not institutional selling. It was people getting scared. And then the shorts coming in. I talked about the shorts were coming in. We're coming in, trying to push the market down. You can have shorted every 15 minutes. So I've entered a day there for the couple of days. Same exact thing playing out in here. And then once again, the retracement happens fast and furious. And one, because institutional money, when it comes in and steps into the market and steps in, whether it's selling or buying or shorting, whatever the position is doing, will step in at any given moment in time without an announcement, because it doesn't need to do it, and it won't do it, and it never will do it. And that's why you have to learn how to re-gaps, which is what I do. And I do better than anyone, better than anyone on the planet actually. I wouldn't be able to call the market as well as I did for the last year and call for what is going to happen in 2015. The fact that I can see that the spy is going to go to 300 in the year of 2015 is amazing. It's amazing. It's all because I want to know how to trade, I know how to trade and gaps. And the fact is I'm aligned with the proper things here to read the right things to know what to do because I have the knowledge. I have the knowledge, and that's how I know how to do this, that's how I can say something like this. So the market will close this year into a rally for all intents and purposes. It looks like that's going to play out in the next two weeks. A slow grind up or one big green day, shot up and see where we go. Even if we pull in before the end of the year, it doesn't change anything here. The tone has been set to carry us into 2015. The market's higher. There are people short this market. They're not going to give it up right away here permanently. They'll be back in again. Even if they take the position out, they'll come back in again into 2015 because the shorts keep trying to push the market down. But they're not the ones in control here. The bulls are in control of this market and they're going to keep lifting it up. And the money's pouring in. It's just a lovely flow of money. You cannot have something like this. If you didn't have money coming in and power, power, power money is something that's amazing. It's beautiful. It's fantastic. I see it. I can smell it. I can read it. I can feel it. It just flows through me. And that's how I know it's going to happen. And you want to do with that. You absolutely 100% want to be with that. And in the case of the QQQs in the spot here, which I'm reviewing tonight, the market is higher. Have a fantastic happy holidays, everyone. Happy holidays. Have a wonderful Christmas. Have a fantastic happy Hanukkah. Have a great week. I'll take a look at the market maybe the end of this week before the new year to see how we all flush out or maybe early next week. But the spy is higher. It's a beautiful chart. I've never seen any more beautiful in my life. Everything's playing out here just perfectly. Rally into 2015, rally all year 2015. One of the most bullish market markets going to ever have in its life in 2015 is setting up. And the spy is going to hit 300. And I'm calling this to happen 93 points from here. So that's pretty amazing. But remember my name, Melissa Armo with the Stock Swish LLC. Email me at Melissa at thestockswish.com if you'd like more information on upcoming classes of the live trading room or the swing trade letter. Have a great holiday, everyone.


Hi, I'm Zor. Welcome to Unizor Education. We continue talking about distributions of random variables. This lecture is part of the Advanced Mathematics course for teenagers, for high school students primarily, which is presented on Unizor.com, and that's where I suggest you to watch this particular lecture because the site contains notes to this lecture, which basically is like a textbook. You can read it in parallel or just independently from listening to this lecture. And then there are certain exercises and exams actually on this website. So this is the source of all this entire course. Alright, so let's get back to distributions. We are talking about binary distributions in this lecture. And I will just devote some time defining what exactly we mean when we are talking about binary distributions. So this lecture is like an introduction, primarily the definitions of binary distributions. Now, as the name implies, binary distributions are related to certain things which have only two different possible values. Imagine the experiment which has only two results, like you toss the coin for instance, and it can be either heads or tails. So you have some experiment where the result is success or failure, or yes or no, or any other binary kind of an experiment. So all these binary distributions are related to experiments with just two different possible results. Now, the experiment can be a single experiment, or we can consider multiple experiments, each of them having these binary characteristics. So the single ones, the single experiment is usually called Bernoulli experiment, or Bernoulli trial. Bernoulli was a Swiss mathematician of 17th century, and he was the first actually who researched this type of probabilities and random distributions. So, we are talking about single experiment with two results, which we can conditionally call success or failure, and the results have certain probabilities. For instance, the results of success is p, and the probability of failure is equal to q, which is 1 minus p. So there are only two possibilities, and that's why this particular probability should be 1 minus p, so the sum of them is equal to 1, where p obviously is greater or equal to 0. Now, if it's let's say a coin tossing, then usually if it's an ideal coin, the probability p is equal to 1 half, and the probability q is 1 half. So 1 half is the probability of tails, and 1 half is the probability of heads. Now, what does it mean actually that the probability of success is equal to p of that particular experiment? Well, it means that if you will repeat this experiment again and again under exactly the same condition, and all our sequential experiments are exactly identical to each other and independent to each other, then if you have let's say n experiments conducted, then approximately the number of successes will be p times n, approximately. And the more experiments we conduct, so the larger the number n is, the closer let's say the number of successes divided by the number of experiments will be closer to p. And as the number of experiments goes to infinity, this ratio will be closer and closer to p. So the limit of this as the number of experiments goes to infinity should be equal to p. So that's what it means basically, that the probability is equal to p. Okay, so we are talking about Bernoulli experiment. Well, at the same time we can define a Bernoulli random variable, which basically is probably the simplest random variable, let's call it c. Now, the random variable you know is a numerical function defined on elementary events. We have two elementary events, success and failure, and we define the value of this random variable on one elementary event to be equal to one, and the probability is equal to p. And obviously the value of random variable on the failure, we define as being equal to zero. And the probability of our random variable to be equal to zero is q, which is equal to one minus p. So we can talk about Bernoulli trials or experiments, or we can talk about Bernoulli random variables, which has two different values with probabilities of p and q. Okay, now, as an example I was saying you can consider for instance coin tossing. What else? Well, if you will consider that the result of Bernoulli trial is trial number k is event k, which is either success or failure, right? Then we can have that c of this ek is equal to either one or zero, depending on what actually this elementary event is. If it's success it's one, if it's failure it's zero. But then if we conduct n experiments one after another under exactly the same conditions, what can we actually say about the probability? Well, let's do it this way. We will have the result of the first is one or zero. If I will add the result of the second, et cetera, and result of the ends is n. So these are either zeros or ones, right? So number of ones is number of successes. Now, that's why if you will have this sum and divide it by n, this would be a ratio of successes. And that's supposed to go to p as n goes to infinity, because that's the definition of the probability, right? So the sum of these values of our Bernoulli random variable on the elementary events, which are results of our experiments, is actually number of successes, because in success it's equal to one, in failure it's equal to zero. So forget about zeros, we don't really count them, and number of ones is exactly number of successes. So, and we know that number of successes divided by the number of experiments should go to p as the number of experiments goes to infinity. So that's another way to view, basically, the Bernoulli experiments and Bernoulli trials and this probability, which is actually the probability of success. Well, we will devote some time in the future to basically have some characteristics of this distribution, like its expectation, its standard deviation, et cetera. But that's not part of this lecture. In this lecture, I just wanted to define certain concepts. And the first concept which I wanted to define was a Bernoulli trial, Bernoulli random experiment and random variable, which has a Bernoulli distribution, which means it's equal to one or zero with probabilities of p and q correspondingly. OK, that's all about Bernoulli distribution as far as I wanted, basically, to define something about it. Now let's consider another one, which is very much related to this. Also binary, which means it's related to experiments with only two results. But in this case, instead of making one experiment, we are making certain number of experiments simultaneously. Or sequential, it doesn't really matter. Now, and what we are interested is how many, if we conduct, let's say, n experiments and Bernoulli experiments and Bernoulli trials. Now, some of the results are successes, some of them are failures. And let's say we have k successes. What are possible values of k? Well, it can be zero, for instance, if all n trials resulted in failure. Or it can be one or two or three. And the maximum is obviously n, when all n experiments ended up in success. So k can be considered as a random variable. So whenever we throw, let's say, n coins and we count how many heads are as a result of this experiment. It's one experiment with n coins simultaneously. So the number of heads is a random variable because it depends on the result of this experiment. Now, there are different elementary events which are the result of our n coins throwing. For instance, all of them are success or heads or all of them are tails or half of them tails and half of them are heads, etc. So these are all different elementary events. And on each elementary event, each result of the n Bernoulli trials, I can have a numerical function which is number of successes. This is exactly something which we called binomial distribution. So k is a random variable with binomial distribution. So that's the definition. So we have defined a combined experiment which consists of n different Bernoulli experiments, Bernoulli trials. And the number of successes among them is our new random variable which we defined in this particular way and its distribution we call binomial. Now what's interesting is what exactly is this distribution. So what is the probability of this random variable, which again I'm using the letter c, to have the value of k. So what is the probability of random variable which is equal to the number of successes to have a concrete value k which can be 0 or 1 or 2 or n. There are no more different values. So that's our task right now because to define the distribution of the random variable means that we have to know which value it takes and what's the probabilities of these values, right? In case of Bernoulli experiment and the random variable being basically the result of this experiment, success or failure and its value 1 or 0, we know actually from the definition of the Bernoulli experiment that the probability of it's equal to 1 is p and the probability of it's equal to 0 q which is 1 minus p. In this case if we have exactly the same Bernoulli trials but we simultaneously arrange n different trials which each of them is exactly the same as anything else as all others. They all are independent and they all have exactly the same probability of success but we conduct instead of one experiment n experiments simultaneously. And now we define our random variable as number of successes, right? So what is the distribution of this particular random variable? So for each k we have to calculate the probability. Now let's think about it. The easiest way is to calculate for instance the value of our random variable to be equal to 0. Now what does it mean? It means that we have result of all these n Bernoulli trials, failure, failure, failure, failure, right? Now they're all independent and we know that the probability of combination of the probabilities of n different events is the product of their corresponding probabilities. So the probability of our random variable which is the number of successes to be equal to 0 is the probability of the first experiment out of these n to be equal to, sorry, failure which is q and then the second one etc. and the nth one. So we have n different q's multiplied by each other and this is q to the n degree. Now what is the probability of our random variable to be equal to n? That means n successes and this is p and p etc. and p which is p to the n degree, right? So we have already calculated in simple cases. Now how about general case? Well this is actually a very easy combinatorial problem which we have already solved before when we were addressing all these combinatorics problems. Okay, so here is what it is. So we are trying to calculate the probability of number of successes to be equal to k. Well we have n experiments, right? Now some of them are successes and some of them are failures. So let's say these are successes, these are failures. Now if I know that the number of successes is equal to k, first of all I have to find out which experiments out of these n are successes and which are failures. Well obviously you have to have the number of combinations from n by k and this is basically the number of different ways our k successes are positioned within n experiments. It can be the first k or it can be the last k or it can be the first and then the third and the fourth and etc. So all the different combinations, how many times, how many different combinations of k elements we can extract from the group of n, this how many, which is actually n factorial divided by k factorial and a minus k factorial, right? This is the formula. So I hope you do remember the combinatorics because that was actually immediately before we started probabilities. Okay, so we know that this is the number of different choices of k successes to be chosen from n experiments. Now once any particular choice is made, so we marked k successes out of n experiments, concrete k successes. Now what's the probability of this one? Well, if there are k successes on the particular places, their probability is p to the power of k, right? Because they have to success, success, success. And all others, all other n minus k supposed to be failures, right? And again it's a combination so we have to multiply it. This is the probability of a concrete k successes and n minus k failures to be somehow, you know, after it's already positioned in this particular thing. So what's the probability of our random variable to be equal to exactly k number of successes? Well, that's this number, which shows how many times we can pick a concrete k successes out of n experiments, times this. So once we have chosen, that's what it is. So first, number of combinations of k successes out of n. And then for each particular combination, this is the probability of k successes and this is the probability of n minus k failures, which is exactly the same as 1 minus p and minus k, since q is 1 minus p. So this is the formula and as you understand, it depends on two parameters. So our distribution depends on two parameters. Number one, how many trials participate in the whole thing? This is n. And what's the probability p of a success in one particular trial? So when we are talking about binomial distribution, and that's exactly what is the definition of the binomial distribution, we are talking about the distribution which depends on the number of trials and the probability of success in each individual trial. So I just wanted to define these two different probabilistic distributions. Bernoulli for a single experiment with two results and binomial distribution, which is basically the combination. Now, what's interesting is that if you will define, now we have n Bernoulli trials, right? With each Bernoulli trial, we can associate a Bernoulli random variable, which is equal to 1 or 0. This probability is p or q is equal to 1 minus p. And i is an index of my experiment, i is 1, 2, etc., n. Now, we are talking about binomial distribution. Now, what is binomial distribution in this case? Let me use a different letter, see for binomial distribution. It's actually sum of all these Bernoulli distribution. So, binomial distribution with n different trials is the sum of Bernoulli distribution of each individual trial added together. Because this is actually the number of successes, right? Since xi is equal to either 1 for success or 0 for failure, then the total number of successes, which is actually how binomial distribution defined, right? This is this sum. So, this is a very important relationship between binomial distribution and Bernoulli distribution. So, binomial is basically a sum of Bernoulli distributions. And that's kind of obvious, because Bernoulli distribution is one trial. And binomial distribution is the number of successes in n trials. And obviously, the sum of 1s and 0s when success is 1 and failure is 0 represents exactly what we wanted. Alright, so this is something which I would consider to be a nice definition of these two binary distributions, Bernoulli and binomial. Again, obvious example is the coin tossing for Bernoulli distribution and n coins tossing simultaneously or sequentially. It doesn't matter tossing n coins and then count how many heads or how many tails we have. This is an example of binomial distribution. Now, next lectures will be devoted to basically some numerical characteristics like expectations, deviations, etc. Alright, so that's it for today. Thanks very much. Don't forget that Unisor.com contains all the comments, notes for this and all other lectures. And well, try to read it and I think it would be very beneficial actually if you would read it after you watched the lecture. That's it for today and thanks very much. Good luck!


As teachers, we strive to assist our students by providing timely, focused and goal-directed feedback. But have you ever stopped to think that focusing on feedback might be counterproductive? This is because most feedback is about telling the students. Anyone who has kids know that there's a limit to how much they learn from being told. They don't learn by being told to stay out of the way of the swing. They get hit once and then they learn. The issue is that students very often do not understand what feedback implies for their own work. What happens is that feedback gets more and more elaborate and this actually doesn't help students become independent. We have to ask ourselves, what skills do we want our students to master? I say that we need them to be able to make good judgments. We need them to be able to analyse. We need them to be able to state a position and defend it. And we need to do this in an environment where their proposition and the defence of it can be analysed, putting the students in the role of making judgments. To some degree, giving feedback all the time removes that. The person giving the feedback is the one making the judgment, not the student. When it comes to making absolute judgment, it is easier for human beings to compare two things and make a decision about which one is better. We're not good at carrying around absolute values in our heads, but we can compare reasonably well. That's where you have the opportunity to give students some practice at judgmental thinking, coming up with reasons in defending their judgments. And maybe in this process they modify or revise their judgments. The process of expressing what we think we are thinking often changes our thinking. We've got to put students in the role of being the observer or the sensor of what's there, the evaluator of it, and then knowing what to do about it. It's like a detective looking for information to use. Nearly always it's also the teacher who tells learners what to do to make their work better. Students are better at evaluating work that is not their own because it's not their personal property and not an extension of themselves. With someone else's work they are detached from it and that's the best type of thing we need to build into our teaching practice rather than having someone constantly telling students the answers. Take for example a student musician I know, let's call him Alex. He practices trombone every evening. Alex's parents were friends of mine and they listened to every practice session. They thought they were being helpful by telling Alex when he played the wrong note or was out of tune. I told him to try listening without giving any advice or correcting him just for one day, so they listened. Alex played a wrong note and stopped and said that's not right and he went about working out what the right note was. He started to learn that he was in control. He has to figure out that something's not right and then what to do about it. And that's why feedback is often way over-emphasized. It's a simple idea. You put the students in situations where they have to make judgments about works of the same kind that they are being asked to produce. And then you say, is it any good? And then how good is it? And then why? Why did you judge it like that? Finally you say, what would you say to the person who wrote this? What advice would you give to make it even better? Deeper's connection with the task is achieved by the person making judgment about it. When the students have to provide advice, that's when the really potent learning takes place. Ultimately I want students to be able to monitor the quality of what they were doing while they are doing it. That's the critical time. That's when it matters. And even the youngest of our learners can achieve this. In Australia there are two TV programs specially geared for preschoolers. One is called Sesame Street, which lots of people know about. The other is called PlaySchool, which is very different and produced by Australia's ABC, our national broadcaster. I asked this three-year-old, which do you like best, PlaySchool or Sesame Street? He thought for a little while and said, I don't know. After about a minute I said, tomorrow if Mummy said you could watch only one of those, which one would it be? He thought and then said, PlaySchool. Okay, so why do you like PlaySchool better? I don't know. I just do. So I pressed further. Is there something you like that happens in PlaySchool that doesn't happen in Sesame Street? Or maybe something that happens in Sesame Street that you don't like? He thought for a bit longer and then said, the stories are longer in PlaySchool. I said, but there are plenty of stories in Sesame Street? Yes, but they're all in bits.  He liked the longer story and Sesame Street doesn't have a segment like that. So here's a three-year-old making a holistic judgment and then identifying criteria for judging between two programs. Everything in the education system has conditioned students to believe that the teacher knows and the teacher is the judge. Being discerning, perceptive, seeing what's there, not what's not there, responding to a phenomenon or object, and then tuning your response to what's generally accepted as high quality is critically important. We should want our students to be able to judge the quality of their own work in the moment of production. Like a surgeon I knew, I asked him, how do you make decisions when you have a tricky operation? He said, well you don't really have much time to deliberate and make decisions. Unexpected things happen all the time. You just respond automatically. You have to make decisions in the moment. You can't write a rubric for it. But this type of tacit knowledge can only be picked up through repeated exposure and in environments where you are making judgments and defending them. We should be part of the students' acceleration of their tacit knowledge. We don't want to feed forward to the point where we are holding learners' hands. We should be helping the learners become intelligent deciders. And they should develop knowledge across two dimensions. One is what the scale of woeful to brilliant looks like, varying qualities. And this means seeing multiple responses to the same task. The second is to compare two works of similar quality but are similar for different reasons. As teachers we see hundreds of pieces of work and we are constantly calibrating. But the students don't regularly get this experience and we need to provide this for them.


Hi, I've got the women's S-Max 10 here from Salomon. This is a pure-piece ski So if you're looking for grip stability and fun, which is frankly why a lot of us do this sport Then this is the ski that I would suggest you have a look at. In terms of the standout tech We could be here all day, but I'm going to pick out the Edge Amp So the Edge Amp is this new section in the ski here, a raised platform and the binding sits on top And as soon as you put the ski on edge, it gives you more direct edge grip So 30% better edge grip If I play a bit of tennis, if somebody said to me this tennis racket is going to give you a 30% better percentage on your serves I'm in so absolutely look out for this try it out. Don't take my word for it. Ski it, try it, test it I can say that the quotes that we've had is skis like a dream Carve grips like a train on a track is one of them that came out and that is without a doubt coming from the Edge Amp Just been out on the S-Max 10 W in the hard snow in the morning. It's a bit frozen But it works really nicely anyway. It just holds your edge and gives you confidence all the way through Just really nice and fun to do Even when it gets a bit steeper, it gives you loads of confidence and it feels like you're going to keep the edge in it When you get down to the bottom as well, it's a bit flatter a bit softer You still just power through and you have loads of fun all the way down Overall, I think this is a great ski for someone to progress go a lot faster a lot harder And it will help you out to get the confidence for it