I started trading about eight years ago, and I lost money in the market when I started, like many people do, you're not born usually knowing how to trade. Then I created my own system. So I created my own system and my own method, and that's all that I do. That's all that I do, and then after a couple of years of trading, my friends encouraged me to teach what I know to people, and I didn't actually start teaching right away, but I started just putting videos on YouTube. For those of you that have never been to my YouTube site, Paul can put the link in the room there. You can go and subscribe to my YouTube page. I have a lot of plays of the day that I put on YouTube, past webinars, and market reviews, and then I decided to teach a class on my method. Actually, my class is the original information that I wrote for myself. I literally wrote everything down and had all the sheets, and then I put it into a PowerPoint. My class is very authentic. It's everything that I designed myself in the market to make money. So when I first started trading like everybody else, I learned a lot of stuff about a lot of things, which was good information. You can get stuff in books and you can get things from webinars. I'm sure those of you that are here have been to other webinars, but you don't always learn how to make money. The problem that I had and the reason that I ended up deciding to do my own thing was everything that I went to. I learned something, but I couldn't translate it to cash. At the time, I wanted to find a full-time job. I had a good job, I was doing mortgages, but the industry was changing and I saw the writing on the wall. So I wanted to replace my current income with another income that was very similar, or at least have the potential to make unlimited income. Because when I was doing mortgages, I was commissioned. So I always had the potential every year. If I did a lot of loans, I made more money. Trading is very similar, which is probably why I enjoy it. The more money you risk, actually, science positions, and we'll talk about position sizing tonight, the more you can make. So for example, if you take 5,000 shares of something, I'm just going to write this in the room, and you short a stock, and you have 5,000 shares short. If you short something at $10, short it, and you're betting that the price is dropping if you short. If it goes to $9, I'm just being very simple here. If it goes to $9, you have a dollar profit. So if you have a dollar profit times 5,000 shares, this is something you're trading in, then you'd make what, $5,000? We'll talk about some of the trades here tonight that we've done in the last week or two weeks in the room, but really it's about the quantity. So the idea of making $250,000 a year may seem like a lot of money for people if you're not making it or if you're trading right now and you're losing, but it's not if you get the direction right in a stock that you take the position in. For example, the one I just showed you, if you had bummed long the stock at $10, and it dropped to $9, and you had 5,000 shares, you would have lost $5,000 instead of making $5,000 if the stock price went up. So how do you make money in the market? You make money in the market, taking the position in the correct direction, and how do you make a lot of money in the market, whether it's 100 grand a year or 200 grand a year or 300 grand a year, it's by the position sign. And as a day trader, I'm very nimble, so I'm in and out of my trades very, very quickly. So I'm not taking 1 million shares of something. Okay, hedge funds, banks, they trade stocks with high liquidity and volume and take big sizes, but what I do, I'm looking for those big stocks to trade. So does anyone have any questions so far before we get going? Let's get started. So here's my email. If you're interested in more information, you can email me at melissa at the stockswush.com or email Paul, Paul's in the room tonight as well. Or you can feel free to give me a call, 929-3200 gas, okay. Anyways, here's all of my social media sites. Like I said, you can go there and you can follow me mostly on the YouTube, I think it's a good site. So as I was saying earlier, it's about thinking about your future, planning for your future. When I was making the transition for myself, knowing that I didn't want to keep doing my mortgage job, I was in the process of planning for the future. How am I going to get to the next level of my life? And I wanted to have a bright future. And if you're doing something right now, whether it's a trading strategy in the market or a job that you don't like, and you feel like your future is not bright, you have to change that, okay. It is about living in the now. So I know this is where the balance comes in. You have to say, I'm here right now, I'm living in the now. I'm living in the now and I want to create the future that I want, okay.  So you take where you are right now and you look ahead and you say, how can I get to this place in six months or one year to make 250 grand a year, to be a successful trader, to quit my job if you want to. And that was always my goal. I just want to do it for retirement, for extra money you want to trade. But for me, I wasn't in that survival mode when I was making the transition. I wanted to thrive. I wanted to have a career where I could make hundreds of thousands of dollars or millions of dollars, which by the way is kind of the market. How, again, same concept, same principle, it's about share sign. It's about share quantity, okay. And that's what it's about. So if you get the directional bias correct, of big money, you can make a lot of money in the market. You can. Not just 250,000 dollars a year. Like I said, you can make much, much more. The potential for profits is unlimited if you can predict the direction of a stock or the market accurately. So think about what are your goals. Are your goals this much money, whatever you're making right now if you're trading? What are you trying to make?  I don't know. What are your income goals for trading? What are your income goals right now in your career and are you making them? Are you achieving them? Are you on pace? We're more than halfway into the calendar year. Between now and the end of the year, are you going to hit your income goals for the year? Are you going to make more money this year than you did last year? Are you going to make same or are you going to make less? Okay, think about that. All of a sudden, time just flies by. But you still have, August, September, October, November, December, five more months left in the year. You could change your income level for the year for the calendar year of 2016 if you decide to learn how to trade and become profitable. You've got plenty of time. You can do it. So how do I do it? I chunk it out. I chunk it out as a day trade. And then, like I said, I am doing options trade. But I am doing only one trade a day when I trade. So this sounds like a lot of money for some of you, especially if you're not making it, but really comes out to be about $1,000 a day. So $1,000 a day is $5,000 a week. And some days, you will make more than $1,000 in a trade. Some days, you may not trade at all. And so, again, it's about chunking it out to get to the goal. Now, going back to the example I used previously, a dollar move in something if you have $1,000 shares is what? $1,000. $0.50 in something with $2,000 shares is what? $1,000. I'm talking about a $0.50 move in a stock. $0.25 move share size is $4,000. I mean, a $4,000 share is $1,000. So you don't even need something to move a dollar necessarily every day. Once you start to take $3,000, $4,000 shares is something. You can get a quick short move in something to make $1,000. So this is how you're doing it. You're chunking it out. And stocks move like this all the time. And sometimes you get things that move way, way more than that. We'll look at one of the ones that the trading room did today. So they happen all the time. You've just got to look for them. Here with DDD. This is, again, a very cheap stock. Even cheap stocks at this price point, this is the ticker symbol of DDD with a short. You see here with a stock, these red bars to pick the drop in price. Again, if you short it and the stock is dropping in price, you're getting the direction right, boom, you make the money. You've got to take the trade to get in it. But this is more than a dollar. Nice, nice move. But again, the price point is very, very cheap. Sometimes I trade stocks that are over $100, but it's very rare. I'd say most of the stocks that I trade, again, I'm an equity trader, between $5 and maybe $65. Okay. Most of the ones that I am doing. So here was the list of the last 10 trading days. This doesn't include today, which we will go over today's trade. But there were 12 trains in the last 10 days called in the room by either me or Paul. Nine were winners and three were losers. Okay. So this is a 75% win ratio. What does that mean? That means that out of 12 trains, nine works, three were losers. So you can see here with the system where you're predicting the directional bias and you take the trade, if you know where it's going to go, you don't have that many losers. Okay. And so obviously, if you're risking a couple hundred dollars a trade or a thousand dollars a trade, and some trades that risk more than a thousand dollars, you can see how you can make the money. So with a $1,000 risk in these 10 days, and again, it doesn't include today, you could have made $11,000. So you see how you can get the goal of making over 200 grand a year. So let's go over the DDD. Now, as I was saying, I have a system that predicts directional bias. How am I doing it? Well, I look at the daily chart. I look at the daily chart of a stock that's doing what that's gapping. Now, what is a gap? For those of you that have no idea what a gap is, I'm going to explain it. And if you're ready to do it, this is just going to be a review for you. Stock closed here, okay? The night before, at 4 o'clock Eastern time, the U.S. market has a close. At 9.30, the stock opens in the morning. Between 4 o'clock and 9.30 a.m. Eastern time, what happens? The stock had a move. The move can happen in the post market at the pre-market hours, but it creates a stock to gas. So what is a gas? It's just the difference between the close and the open. That's all the stock is, okay? It's different between the close and the opening price. Should this close to your rent 14-something? No, for the next day, here rent 13-something. So that's the gap. So in the morning, I'm looking for these, and you can look for them on a scanner, like the DDD or many other things, to see what is gapping. And then I have a method. It's a rating system that I use on the daily chart. It's a 26-point rating system where I will rank the gap on the daily chart to predict whether or not I should short this or go long in, or do nothing with it, okay? Or do nothing with it at all, okay? So this is what it is. Now, how do you make the money? You have to still wait till the stock opens. This is the open, okay? So you wait till it opens. 9.30, this is a one-minute chart. So the previous chart was the daily chart. I'm taking the trades on a one-minute chart. So the stock opens right here at 9.30. 9.30, 9.31, 9.32, 9.33, 9.34. Boom, you can short it. And actually, there was a lot of short entries you could have done in this one. But here is the drop. So I'm taking the trade short, and again, when I'm shorting, I'm betting the stock price is going to go down. And if I get it right, I make money, whatever size you take. And in this case, it was a short. But I know, based on the daily chart, even before 9.30, that I can predict it's a short or a long. Does this make sense? Now, if you did this trade, okay, for example, price of the entry was what? 13.48, stop 13.83. Share size is what? 3,000 shares. That's the share size that, if you wanted to risk 1,000 bucks or thereabouts, you would have taken. Now, every trade I do, I put in a stop. So I take the trade short and I put in a stop. Now, why do I put a stop? I put a stop in because what if the trade doesn't work? And again, you saw there were three losers in the room in the last 10 days. So if I take a trade and it doesn't work, I want to have a fixed loss. And again, this is something that helps you be profitable because you keep the money then from the gainers. Otherwise, you have unlimited losses if something goes against you, okay? So I don't trade like that. I use a stop. It's a limit order stop. If you have questions about that, ask me now. Anyway, it's on the exit, 12.76. So this profit with a $1,000 risk is what? 2,160 bucks. So you take the trade, boom, get the drop and you're out. Let's go back and look. Again, the directional bias is down. It's to the downside. But how do I know to predict this because of the gap? Here's a one-minute chart. Here's where it closed the night before. Here's where it opened the next day. It's because of the gap. You don't know. It's easy to say now because this is already after the fact in the chart for now. But in the morning, this didn't happen yet. You don't even know. You're seeing the gap in the pre-market data or the post-market data. Nothing's printed here yet, okay? Does that make sense? Not enough. Anyone has any questions about that? If you're not familiar with gaps, you may not understand what I'm saying. But that's how I'm making the prediction of the directional bias. And what happens is, and we were talking about this in one of the other webinars, many people are trying to understand what makes the gap. Sometimes a stock gap overnight or in the next morning based on earnings. Sometimes the gap's based on bad news or data or something happening. It could gap for many, many reasons, okay? So there's lots of reasons something will gap. And there's tons of stocks that gap every day. But you can't always get one that you can predict in the right direction. And that's why I came up with the rating system. Now let's look here at this one. Netflix actually was last week and it had earnings. So this gap's been the earnings. So the stock closed here the night before and then got down. Here it was. It just collapsed. So the night before, this was like a 99 something or 98 something the night before it closed. Four o'clock Eastern time and in the morning, boom. Guess what? Gap's down here to like 86 something, okay? Netflix. Here's the one-minute chart. So I rated this per my system. I was predicting it was a short, but guess what? It opened. It had a big green bar, huge green bar. You might have thought this was a long, one over the high of the day, a couple of times here and had some big green bars, but it was a short. And how did I know? Because my rating system predicted on the daily chart that it was a short. And so I shorted it and then I put the stop. So the stop was 87.10. So I shorted it in here, waited, rallied back, held, held the stop. You could have shorted it again and then here's the money. If you're short. So do you see here, if you had gone long this, if you had predicted the direction of bias long, you would have been stopped out, broke the low with a day and then kept going. But if you shorted it and put the stop where I did, you would have made money because here's what it did. So every trade I take, I'm looking to get them to move in the morning. The morning, the morning, the morning, I'm in and I'm out quickly. That's what I do. Really, really quickly I try to take the trade. So this entry was short. You shorted it 86.04. Stop is 87.10. Share size is what? If you're risking a thousand bucks, again, your risk should be equal in most of the trades you do or close to it. A thousand shares is what? You risked 1,060 bucks. Exit 85.05. Okay. Total profit, $990. And there you go. So if you're looking to make $1,000 a day, close enough. I always tell people, trade in the morning, don't keep training. If you're up in the morning, stop. That was something that it took me a long time to get into the habit of doing. And so I run a live training room where I call my trades live and I trade live, but I close it down. No later than 11 o'clock every day and some days earlier than that. It's very, very important to keep the money that you make in the market. Okay. Any questions from anyone so far? Everybody's quiet. Does everyone understand what I'm talking about? About a gap. Does anybody here that's never traded in their life before that doesn't know it's chart or understand a chart or candlesticks in the chart? Tell me. I don't know what everybody knows and what people don't know. So don't be afraid to ask. And again, I'm the only one that can see everybody and see the question. Or Paul can write it in the room. Okay. I'm going to keep going. No one has questions. Okay. Let's talk about the next one. So I gave the stats for the last 10 days. Here was the DFS. DFS was an earnings one, two. Okay. Closed here the night before. Four o'clock, boom. Gaps down. Gaps down here to like 56 something. So this tail depicts the drop in price here that happened quickly, quickly into the morning. So you're looking to predict what should I do with this in the morning. All my trades, I'm looking to get in between 9.30 and 10.00 a.m. through time. That's when the moods happen. That's when the mentions happen. That's when I get these good solid entries in the one-minute charts that have the push. Okay. And in this case, it's the push down. And for those of you that don't know me, I prefer to short. You can go long using my system, but I find that shorts just happen very fast. And they happen quickly. So I prefer to short. So anyways, the stock rally here into the open. Boom. You could have shorted it and here. So this is 9.30, 9.31, 9.32, 9.33, 9.34, 9.35, 9.36. And there it is. You could have shorted this. And been done in less than five minutes. And again, here's the time of the day that you're looking to trade between 9.30 and 10. So here is the entry price short. 56.59, stop. Again, only putting a stop because a stop protects you. It's like the insurance. If the trade fails, I will only lose whatever I risked, which is based on the share quantity. So if you took 2,500 shares, you risk what? 1,050 bucks. Again, this should be pretty close for the same each time. If you want to risk $500, fine. $500, $500, $500, $500, $500. You take half the share, fine. In either way, it should be close to equal. Exit here, 55.92. This dropped harder. Okay, this actually dropped almost 50 cents more down into itself. But once you get a sell-off like this, you're like, oops, sorry about that. Once you get a sell-off like this, you're watching it, watching it, watching it in the level two. And you just take it where we get out, okay? So total profit on this is 1,675 bucks. Now, I do teach in the class targets in the back of the deal. I do teach in the class targets. You do have to watch the trade when you're getting close to a first target, okay? Sometimes things will go to the dream target, but you never know.  You know the money's there.  Okay, so that was another good one. INTC was Thursday. INTC was Thursday of last week. Another gap down. Stock closed here. Boom. Gap down. Drops fell. Nice. What? Short move. Again, you look at the daily chart. You predict the movement. You predict the movement based on the gap, based on the reading system method that I've devised. It's 26 points. I'm not looking for a perfect score. I'm looking for 20 or more. 20 or more means that it's a high level probability of working in the direction of the gap. So in the case of a short, if you raise 20 points or more of a high probability that I can short it and make money, it's going to go down. If the stock gaps up, it's a high probability if you raise 20 points or more in a gap up that it's a loss and you can go along the stock. So again, it's about making money in the right direction because it's the only way you're going to profit. If you try to go along the stock, you're lost. So here was this one. Open, rally, drop, broke. Rallyed up again. Set the high of the day at 9.30 here into the open. Boom. You could have taken it here. You could have taken it here. You could have actually done a later trade in this as well. Usually I get in my trades in the morning part of the day very quickly the first set up. But this had a couple entries here too. And do you see here again the time of the day that you're in it? So this was a nice one. Paul did this one. He entered it very aggressively in the first bar and then once it started going, he tightened up the stock. So the profit in this, where he had lowered the stock was 280 bucks. I did not trade or run the room this day. But if you had followed all the way down, it actually went down to $34 and broke 34. But this is how Paul chose to play it to be aggressively entered it here and then he protected this profit. Some people do this. They take the trade and then they immediately protect the profits, a small amount of the profits or get out of some or they put the stuff at break even. I usually let it ride or give out a path and let the whole rest of it ride. You have to decide your own money management. But in this case, if you held it, it went more. But this is how the room played it and how Paul played it. So you can be aggressive, but if you tighten up your stock, you may not get the whole move. Either way, most of my trades don't try to be down by 10. And that's like the time of the day that I try to be out of my day trades. And that's another benefit of actually day trading gas because the moves and then happen so quickly in the morning, you can take the trades and be done quickly. A lot of other setups and strategies are out there. You kind of get away until 10 o'clock or later to take them and you got to have the market with you. And then you have to get the market direction right. If you don't get the market direction right, then sometimes you don't get the setup that day and the strategy that you're trading if you need the market with you. What I do is completely independent of market direction. In fact, I created my system during the time of the market. Has been rallying and my system, I do more shorts than long. I prefer to short. So I focus on this downside more. Panic comes into stocks when their price drops and no one really panics when something's rising. So you don't have that panic emotion. Things tend to slowly rise where when things drop, they tend to drop fast. So that's just common sense. Anyway, here's another one, MCF. Stock closed here, boom, gapped down. Again, this is happening when at night or in the morning. So this is a daily chart of this MCF. Again, every day you'll trade a different symbol if you trade with me. I never know. I never know how to see the gap and you don't see the gap till night or the morning. So closed here, gapped down, you raided. Permai system is a good or not. Is it a short or a long? You got to know. You will not make money on this today if you go along with stocks. Okay. So here's the move and it dropped. This was in a five minute chart. So this is usually a trade in one minute, but you can do five minutes. So you have to be a little bit more patient, slightly more patient. Okay. Anyway, still had a nice move here. Drops broke and you could have held this. You could have held this for a long, long time. But it actually did make a later move. So sometimes they go very, very quickly and sometimes they take a little bit longer. This was a much larger move here that took a little bit long to get going. But it still had a beautiful sell-off. Price of the entry was 10.06, stop 10.21. This is a good share size. Again, it's all about the quantity, but you're allowed to take it from the stop because the stop was a baby stop. Okay. This is a really good stop. The stop is the difference between the, where you're putting the stop at the entry and that's how you're sizing yourself. So with 6,000 shares, you'd be risking what? 900 dollars. So there were two answers on this, close enough to the price, same price. Total profit, 3,150 bucks. Okay. So here you have it. Beautiful, beautiful, beautiful profit. Again, if your goal is $5,000 a week, you just made more than half your goal for the week in one trade. So going back to the concept of what I was saying about chunking it out, chunking and chunking and chunking it. This is why you got to keep your losses low. So if you get up in the morning and somebody doesn't meet your criteria, you don't trade. I don't trade if I don't have a gap that rates 20 points or more. If I get up in the morning and there is a gap that rates 15 points, then I'm not doing it. It's 20 or more per my system. If there's gas, but they don't meet the rating, then I can't see them either. What if you get up in the morning, there's three good gaps and every gap rates 20 points or more. You can do all three. Me personally though, I usually like to do one trade a day. One trade a day limits your losses and it keeps you very focused. And you've got to pay attention because these things move very, very quickly. So does anyone have any questions about that? So that was actually all of last week trading. Do I get typing? I don't know if you have a question there. I missed it. It looks like it's stuck. JA is asking me about the points. I saw you signed in late. My method, my system, looks at the daily chart of a stock. Okay. And I have the rating system. This is what I teach in the class. It's 16 hours long. Okay. I have 26 points on the daily chart. I go for the checklist. It's a checklist of points. And I tally them up. If a total 20 or more, I can trade the gap in the direction of the gap. So if the stock is gapping up and it raised 20 points or more, I can go long. If the stock is gapping down and it raised 20 points or more, I can short it. But I know before 9.30. And that's how you're making the prediction because you've got the rating system and you're seeing the gap live. You see the gap live. You can see gaps live right now tonight. It's six o'clock at night. The stocks are the reporting of stocks that are gapping live. You can get up in the morning at 7 a.m. tomorrow early and see stocks gapping. The market gaps a lot. 7 o'clock is going to look at the gap. It's gapping at night. You can plop it in the room. I didn't have time to look without tonight, but I can look at something. So, good question, though. How much money you make, as I was saying before, has to do with the size? It's the size, the size, the size. It's the size quantity. And in this kind of direction, right? So, this is common sense. My strategy is based on common sense because, number one, you can't make money if you're in the wrong direction. That's common sense. People try to get tricky and say, well, I'm going to take this again. This isn't making any sense. Look at what's happening. Have a reason for doing it. Have a system. So, my system is the rating system, the 26 points, okay? But the 26 points actually predict institutional moves, positions in stocks. Banks, hedge funds, trade stocks. They go long stocks and they sell stocks and they short stocks. So, my rating system predicts what institutional money or big money, I call it, will do in a stock.  It's people that are trading huge math and large positions. So, I created all the point system like Ja had asked about. I created that system based off of what are institutions doing or what are they going to do? Because you've got to know what they're going to do ahead of time, okay? And that's how you know. Now, this was today. This was guild, okay? Stock closed here, boom. So, here's the night before, here's the morning. Stock closed here and then open here. So, you get up in the morning, you don't even know it's just going to sell off like this. But you're trying to predict it. How do you do it? You look at the rating system. If it's total 20 or more, you say, this is a good one. I can short it. And it was a good one.  Again, if you would go along this, you would have lost. Now, why is the common sense around this? Again, because you're predicting with a point, if an institution is going to sell their position in guild. Because you don't get a bar like this. This is a huge monster bar to the downside. Again, the red depicts the drop. This is a short that had a huge move. And you wouldn't get a move like this in a stock at this price point. 80 some dollar price point, you would not get a sell off like this unless an institution wanted to dump their stock in it. And it doesn't matter if it's after the fact, you've got to get it beforehand. So here it is. Again, one minute chart. So you raise the gap in the daily chart and you're taking a trade in the one minute chart. Open drop, rally, row, boom. Hit it. You're in. Put the stop. And there it goes. So I mean, you could have actually played this all day. The talk about dream targets. High up here is 84 something. Low down here, 81 something. This is a big move. So we were talking about dollar moves. This was a huge move for this stock. And actually it was a really good entry for this because the sell off happened. One, two, three, four, five. First five minutes of the day. Anyways, price of the entry, 83.69. Stop at 84.81. Share size, 1,200 bucks. Risk what? 984 dollars. Again, has to be the same or similar to the same. Very, very nice. Exit 82.76 and 81.76. You can have these it out. Total profit could have been today on this trade is $2,000. Again, do you see how chunking and chunking at Monday, Tuesday, Wednesday, Thursday, Friday, tomorrow's Wednesday? So this is how you put together a week. This is how you put together a month. This is how you put together a year. And this is how you make upwards of $200,000 a year. But you have to actually take the trade. This is very, very quick. If you aren't watching GILV, you'll never get this. It won't put, you would never get it. You have to know before 9.30 or this has happened and you find it on a scanner, you're like, and you miss it. So you have to have all of this situated and figured out before 9.30. And that's the beauty of what I do because I'm looking at the gap in the pre-market that I'm seeing the daily chart of this. If you went until the open and you're looking on your scanner at even at 9.35, sometimes it's too late, you'll miss it. And this is how many traders do it. They are too late. Then they try to take a late trade in these and 80% of the movie's over. The sell-off has happened and then they lose. Does my system calculate stops and profit targets? I actually teach in the class and I manually write it in the room every morning and put it on the sheet, the targets for the stock. So yes, you learn how to figure out the targets and you will watch the targets to be fit. Does it hit every target every day? No, no, no it doesn't. But if you're up to $1,500, are you going to let it go against you and lose if it's not at the target? No. So this is why some of these examples and some of the trades and you have to make a money-managed decision, you've got to get out at least half of it or more than half of it if you're up money. You can hold a portion of the trade if it starts backing up. Like for example, this here to the target. But it would just, again, common sense. If you're up a lot of money, why would you not get out of some of it? You would, okay? You would. So yes, you have targets, but not every trade goes to the target. And that's just the fact. So you know what you're looking for. You want it to get there. You can hold a piece of the trade. Sometimes things do actually go to the huge targets right away. I mean, that happens. But I got to be honest with you, this hit through the first target here. It did. So you could have just done it and been all out completely. Totally done. All right. So really nice sell-off today. And by the way, looking at this chart, this will continue. This will continue tomorrow. You could choose to stock for again tomorrow. Not as aggressively as today, because the day of the gap was today, but I don't know where this opens tomorrow, but this will follow through. This is lower. All right. Any questions about DeGal? At all. Good questions from J.A. So again, going back to it, you are following the big money. You're looking for each day, what is an institution going to do with this? You've got tons and tons of stocks in the market. Every day I trade a different symbol because I don't know what's going to gap. Again, things gap in night, things gap in the morning. My rating system makes the prediction of the move. And then I know this is a good one. I may rate 10 different stock symbols and one may only rate good. Well, then that's easy. Then I do that one. I still have to wait for the setup. Okay. I don't get in until after 9.30. I don't trade the pre-imposed markets, but I'm looking for the move between 9.30 and 10 and I'm looking for it quickly, quickly, quickly, quickly. Okay. So you want to make the money quickly and that's another nice thing. I'm looking for the momentum. Usually what happens is an institution will know we want to dump this stock and they know it way before the open. So they dump it into the open or go long into the open. Okay. So they know what they want to do with this.  When you're selling 5 million shares of guild or whatever ABC stocks, you're not fat fingering the trade. You're not like we are going to get out of this because of this reason and that reason and their reasons for exiting stocks or taking trades are completely different than ours. We don't need to know that. I don't study fundamentals, but I'm telling you they look at that stuff, but I'm reading the price of what they're doing. I'm reading the price action. That price action is telling me. I don't need to know the other stuff. I know the price and I'm predicting the price movement with my 26-point rating system because of the gap. Okay. But it's the institutions that are making it. So this is all that you need. You can do options, day trains, swing trains. So many mail needs today about 4X. 4X only has one gap a week. It only has one close. But you can use it for anything that has a gap. We have another question here. Are you using any type of line of care to just your rating system? No. Just using my rating system. But on a chart, here I'll go back and show you really, really quickly. I have moving averages. These are just simple moving averages.  I have them on here, but my charts are clean. So this is a 200-peri-moving average. This is a 50-peri-moving average. This is a 20 and this is an 8. Okay. And then you have the moving price and down here is volume. You can't see it.  But, and these are Japanese candlesticks. So these are the things that I have on my chart, but I'm not, you know, the rating system is how I'm making the decision, not the moving average. Okay. And in the case of the moving averages here, look, the 8-peri-moving average was swinging up. The 20-peri-moving average here was swinging up. This was a short. So you can't just look at the moving average and say, oh, this is long because it's rallying. You can't because these wiggle and jiggle. There's not one indicator that you can buy or flop on your chart that's going to tell you what to do. If it was, we'd all have it. And no one would make any money in the market. So, you know what I'm saying? Again, 26 points is a lot of things to look at to predict. If I could come up with 26 hundred, I would. And I'd never lose. I'd never lose. And my risk amount would be $10,000 instead of $1,000. But the point is that the more things that you can use to give you the odds to put the odds in your favor at you, you know, it'd be nice to say we can use this moving average with that one. And that's enough, but it's just not. I mean, you've got to look at a lot of things in order to do it. So, like I said, it's the perfection of one strategy. And then you can do it for many, many different things. John's asking if we do options trades. Yes, yes, I do. Again, I'm usually only in one at a time, very specific. And I will do them over a course of days or maybe weeks. Okay, you can do long-term options trades for months or two. But every day, I'm not day-training options. Every day I'm day-training the actual stock. Okay, but you can day-train options. So many, many traders learn technical analysis. And I trade this in technical analysis, but it's really the gap. The strategy is the gap and then the 26 points which pulled it all together and could make the prediction, which is in the point. So, you know, you can do lots of things in the market. But if you don't have a strategy at all, you'll never make money. If you're doing several different strategies, I suggest still focusing on one. Learn one and get good at that one and make money with that and then learn others. It's very hard if you're not focused on one thing to make any money at all. And every day you get up and you get excited about this thing and that thing. And then you think, well, I'll do this today. And you never have the consistency. The consistency in what I do to make this kind of money is because I'm looking for the same thing every day. And I either get it or I don't. And if the trade doesn't work, then I have the one loss and I'm done. So you won't have losses in anything you do. There's not 100% anything, but your losses should be low. Your losses should be low and you should not over-trade. And you should actually have a fixed risk. So even if you take a trade and it doesn't work, what's the worst thing can happen? You lose one risk unit. And the next day, you can make up the profit for that and still be profitable within two days because the trades work and go multiple risk units as you saw. And today was a very, very good one. You just can't be a jack of all trades and make money as a trader. It's just there's too much going on. There's too many different stocks. There's too many different strategies. There's too much excitement. You have money at risk. There's an emotion surrounding it. You've got to be very strict with what you do. You have to know what you're looking for. You have to be in and out quickly as a day trader. You've got all kinds of things going on around during the day as a drags on economic data and the market moves. You want to just get that momentum, which comes in a gap between 9.30 and 10. And take a quick trade and get out. And again, you're at less risk then. And the nice thing is you don't have to learn anything about fundamentals. I don't read the reports, none of that. So the consistency is reading the direction correctly, focusing on one strategy, focusing on one timeframe, only doing one trade a day, stopping when I'm up and stopping when I'm down if the trade doesn't work. I'm just describing you to a team, meaning doing one trade or trading, trading, trading. I mean, I've tried a lot of different things and I think the reason that I, before I figured this out at the beginning was, I made $5,000 in a gap trade. And that was it for me, that I was so... And at that time, in fact, I didn't have a lot of fives in that trade. I think I had 1,500 shares. I think I had 1,500 shares and I made $5,000. I said, holy cow. I don't remember how much money I risked, but it was nowhere near $1,000. I risked a couple of hundred bucks and I made like five grand. I said, that's it. And then I wanted you to gap. And guess what? It wasn't a short. But I had no idea what I was doing then. It was the very first week I ever traded live money. I'll never forget it. And after that, by the end of the week, by Friday, I said, oh my gosh, I could do one trade a week and I could quit my job. And then the next week, I didn't do it right. And then I was like, wait a minute. And then the next weekend, so it was the back and forth, back and forth process until I came up with a point. But I knew there was something there, into the open, which is between 9.30 and 10. I knew there was momentum. I knew that the way to make the money fast was the shorting. And I knew that I didn't have to risk thousands and thousands and thousands of dollars taking a position to make this kind of money. I mean, if you look at people, we say about the percentages. If you look at the percentages of investment, of money that you have to put out in a trade, of the percentage of investment, return on investment, you get back. It's like over 100%. It's like hundreds of percent. It's something silly because you're making so much. If you risk 1,000 and you make three grand, what's your return? 300%. I'm just going to make that anywhere. And certainly not in five minutes or 30 minutes or however long the trades go. So it's the idea that it does take learning it. You've got to learn it. You've got to learn the point. You've got to apply it. You've got to apply the information as an application. It's getting the knowledge, understanding gaps, taking the class. There's a cost to do that. But once you know it, you can make the money back very quickly for the class. And then you're running forward. Then you're running ahead. But ultimately, this idea of just doing one thing is so important.  And that's what I do. And that's what I do. And I do it every day. When I trade and run the room, I call the trades live. People do them with me.  When they're risking their own money, I want people to be profitable. I want people to be successful. So then I teach the class that I do once a month and a weekend. And you really, really have to take it seriously. Even though you're only doing this for 30 minutes a day, if you don't take it seriously, you won't do well.  I call out the trade live and live time. But I talk about the gap that I like before the open. So 9 a.m., half an hour before the open, and I'll say guild or cost or whatever. I'll say Netflix. This is what I like. It's 22 points. Here's the resistance. Here's the score. Here's the targets. But I also write in the room. I'll write in the room like 22 points or whatever, like in the typing room here and right now. I'll write it and I'll write the targets and the support resistance. And I'll write all the gaps I rate. But I usually am only doing one. I'm doing the top one. So I just do one. Yes, but I'll rate a couple because I don't know. I don't know which one I'm doing or I rate them all. So the idea is to do the highest one. If I rate five things, one is 21, one's 22, one's 23, one's 24, then I'm doing the highest point in gaps because it has more higher percentage of possibility of working. Makes sense? So we will talk about along here. This, again, a great example of how if you don't get the directional bias right, guess what? You wouldn't make any money. A lot of people want to be reversal. Guess what? If you shorted costs, you lost. This was a gap up. Stop post here, gap up. Low, the first tail here is what? 158.13. Client that first moved here is 153.21. Ran up $5. If you tried to short this here and thought, oh my gosh, it's going up too much. Guess what? You lost. Here, here, here. If you tried to short this anywhere in the day, you lost. It was not a short. It was a long. If you tried to short this to fill the gap, you lost. It was a perfect beautiful fabulous gap that opened and pushed. And that's what makes teaching a class for the bottom line is if you went long this, you made a fortune. This is an expensive stock here, but that's an amazing move. Going back again, the common sense, the common sense is you get the institutional push. You get institutions that are buying them. You would never have a stock that ran up $5 at $160 price point, strike price. A five, 10 minutes into the day like that to run up $5 like that if institutions weren't buying the stock. They loved Costco. I don't know why they bought it and I don't care. But you would know in the morning before 9.30 that, oh my goodness, Costco is a good one. It's a long, let's go long Costco. Let's wait till they open. Then you go along it. You could have gone long another one minute high here. Boo! And it wouldn't even matter where you get out. All you're doing is taking it and just do this. It doesn't even matter. This, by the way, went to the dream target and this is still higher. And the thing is that it's so easy to do it if you know the direction. It's so easy. It's so easy if you know the direction right and it's hard if you don't. So people try to scout these that as, as reversals you lose. And by the time you're in this, you should be done for the day and back to bed. So this is an institution. So the common sense is you're looking for what institutions are doing.  If you're selling the stock or shorting it, you got to be shorted. You can be against institutions in the market.  And if you're in them in the right direction, guess what? You don't even do anything.  It doesn't even matter. Listen, why so many people lose is because they are trying to make it overly complicated. The, the points you have to learn, 26 things is a lot of things. But once you know it, you know it. But the rationale behind why you would go long this is not complicated. The ration of the market, now just listen, this is very, very important. And I know it's getting late and I'm tired too, I'm in a couple more slides. I, listen, the rationale behind why you will be successful as a trader or why you will lose is very critical. It's the, if you've got to be alone, you must learn and that is complex and it's a 15 hour class. But the philosophy behind it is not complex. And it's actually common sense. Does this make sense? What I'm saying? So anyways, I traded the open. We talked about gas, Netflix was a gas that happened the other week. Again, one minute chart in Netflix. Beautiful sell off. This is the direction you want to be in. Okay. Boom. Again, here's the moving averages. You can see them, but, you know, you're not doing anything with these here. You're shorting the stock. So you're looking for to do it in the direction of the gap, but you know before the open, predicting is how you're going to make money, but it's the big money. That's where the edge happened. The edges, what is ABC hedge fund, Goldman Sachs, JPMorgan Chase, what are they going to do today? Are they going to sell guilt? Are they going to buy guilt in that gap down? You need to know. So you know how to take it. And the points come from the daily chart, so that's how you can make the prediction. It's this idea of gambling, taking, sometimes people take something like Netflix is a good example. People probably went along it, then shorted, then went along it, then shorted, then went along it again. People probably just going to trade like three, four times to get stopped out in both directions. I never take anything in any other direction other than the one direction that I'm taking it. I don't go short something, then flip it and go long. That's dangerous too. Now someone asked you about Google. I'm in a Google trade right now. This is from last week. This is moving, moving, moving. I'm in at the earnings of Thursday night. But this again just shows you, you can make your goal for the week in one trade. So if you did the Google and got out of it last week, I'm still in the position. I had 15 contracts, 5,000 shares. Again, the 5,000 shares is a good position size. You could have done one train, got out, not even held it. You could have made $6,000. You didn't have to do one day trade last week. That's real money. So this is the chart off of today. Yes, you can do my system with swing trades to JA. But you're in them and you're looking for bigger targets. Okay. You'd be looking for bigger targets in the swing trade. So I'm reading the institutional footprints of money in the stock, just like the Google. If Google's getting bought, you want to be long. If Google's getting sold off, you want to be short. Google's getting bought. So I'm long, so I own calls in Google. Okay. That's how I'm up the money in that position. So you're looking only for the power of money. You can't make any money in the market unless you're in the right direction. And I've called the market long, even today, even though the market was a teeny, weeny little bit of a rent today. It's still higher, still still higher. And I've called the market long in the spy. This is an ETF. Correctly, because I read the gap. So this was the move in the market, had a big flush down. This was, gosh, weeks ago now.  This is the Brexit reaction in the market. Then we gapped up. Then we gapped up. Then we gapped up. How many gaps up happen actually in there yet? That you could have gone long, quite a few. So this is institutional money buying the spy. So you can use this for ETFs as well. Very, very important, okay? You've got to have a system to be successful. So for me, it's the 26 points. And again, I like to short. It's a checklist. It's the checklist works. That's how guild you would have known to short guild today. Instead of going long at a support, you would have known this was a short. It rated 22 points. So my checklist measures the high probability of directional bias for the entire day. That the stock will have a big move in the day. Drop to $3. That's all profit, no matter where you get out. Early confirmation of the bias and the move between 9.30 and 10. Precise entry to follow through in a good risk to reward target potential. So it's about being consistent. It's about being profitable. It doesn't mean you'll never lose in any trades that you take, but you should lose very few times. And if you're not ever trading, then you shouldn't be down, okay? Having profits helps you get confidence and conviction to do this thing as a career. You've got to see the results. If you're green every day for five days or five months in a row, you will get the confidence to do it. And having a mentor or someone like me to teach you a system and call the trades live really helps people. And like I said earlier today, I developed this for myself. I never thought I'd be teaching people and I think it's one of the reasons my system is so good. The other benefit is your output. Sometimes I've been traced for minutes, but I'm usually done every day by 10 o'clock a.m. Eastern time. I'm in New York like I said. Any questions from anyone? So I teach a class. If you are interested in this weekend, Saturday and Sunday, July 30th and 31st, it teaches the 26 points. It teaches what the stock switch is. I teach six different entries. I teach targets, the exit. It's technical analysis on a very advanced level. And it teaches you how to read the institutional positioning in chart. It teaches you how to read gas. Many people out there that trade gas don't do them right. And so then people shy away from gas. But there's so much money to make in them. There's so much money to make in them. And so quickly, so quickly, but you have to get the direction right. So the class is this weekend, Saturday and Sunday, 9 a.m. to 5 p.m. Eastern time. Tuition is $49.99. And you can be anywhere in the world and take it. It's online. And you can redo it as many times as you want. So it's Eastern time zone. And if you take the class now and we're going to retake it in October, you can certainly do that. If you want to sign up, Deadlines Friday, email me for more information if you want to sign up. And if you want to try out of a training room, you can email me at info at the stock squish.com. And you can trial the room before the class, Wednesday, Thursday, Friday.  Just hang on. I'm going to put up, I'm just going to put up my charts here. He's telling me the gaffers tonight. And let's look at them and write any questions if you have any. Is the market up tonight? The market must be up tonight. The market must be up tonight because Google's up tonight after hours here. Yay. What's gaffing up? Just quickly, quickly. Gala had told me that because Google's up tonight after hours. Pull it away. Hold on. Let's look at that. Let me just look at the spy. Apples up. Did Apple rank tonight? Are you sure? Apple had earnings tonight. I didn't even know that when I took that. So my land pass. This is taking forever to load. Look at this. I'm going to fall asleep before this loads. No, Twitter's down. You know what?  Apples up tonight in the earnings. Hold on. I just want to look at the gap and see if it's really along. Like along in the day. Tomorrow.   Hold on. This is another webinar, but I asked you questions. I'm looking at this tonight because this is going to, everything's going to gap, gaping up tomorrow. And I just got done telling you the market's high. Let's just buy. Because I didn't even look or be closed. When I knew we were hanging, hanging like a thread. And I knew we didn't drop today. We didn't really drop today. We looked like we were dropping, but we didn't. All right. Let's look at Apple. Yeah. The spine is up. Quickly, quickly, quickly. Is there any big names out in the morning pre-market? Tomorrow? Quickly. Gala had a look at that. I didn't know Apple's tonight. Honestly, I didn't even know. We should have done an option in this. I've just been too busy. That's amazing. Yeah. I hope this looks better in the morning. The better this looks, the better it is. Listen. This is good. This looks like a nice one. And I'm not rating this now because I'm tired and it's 637. And I've had a long day, but look at this. My Lanta. As long as this holds 104, this is a long. There it is. Just let me look at this. You're in the Google with me. Are you still in the whole thing? I'm going to put the big name out tomorrow. Just a big name. But like Apple is a big name. Twitter has a problem with its life. Let's look at this. I'm not going to be asleep tonight. Well, I'm going to have to go to bed early. There it is. So this is a live gap. It's a live gap. It's being affected by gappos. So Google is gapping up tonight. The price is higher. I'm going to take this off. These show pre-post market activities. See now? I'm going to show you this right now. So this is not close today at 738.46. Boom. Tonight it's gapping up. Live. This is live right now. Here it is. 638. This is still moving. This far went off 10 minutes ago. 9 minutes. 740.25. It's gapping up. So we actually were right on the day. End of close. Because we opened. We opened today. Hold on. Where did we open? We opened at 739.04. And we closed at 738.46. So we had a 50 cent teeny weeny red. Now we're green in the gap. And tomorrow we also have a. We have an FOMC minute too, don't we? We do. Or something tomorrow. All right. I'll just quickly look at the short. Does anyone have any questions for me about anything? Anything about the class? Anything about what I do as we now? We're over the time, but we started late. Very exciting here. Trills that I'm still in that trade. FOMC tomorrow. Facebook is tomorrow night. Or tomorrow morning. Red hat is. I don't see red hat doing anything. Is this the one you want me to look at? No, I can't. I'm sorry. I got your red hat. I don't know. I can't think anymore. I've been up since 5 a.m. You can't just join the trade room job. The trade room is only for students. Yeah, you might have missed that too. I said it earlier. Let me look at this thing. Okay. So this is gapping down. Clearly. Jeez. Where did this close? 5804. This is the situation here. This is a good gap. And it's lucky it is because it's going to be hard to show tomorrow with the market going higher. It's going to be a quick one. I'm not rating this now. Like I said, I'm tired. I'll wait for the morning, but you could rate it at night if you need a system. But the only way I'll show tomorrow is a good one anyways. This looks good, but the market's higher. I mean, it could be a quicker, quick, quick trade for me tomorrow.  I don't even know.  That's taking a beating and it's all situated now. I don't know what the earnings said. Like I said, I don't, I don't, oh, this looks good too. Wait a minute. Well, I don't know. I'm going to rate these both. I don't know which is better. I'll wait for the morning. RHI is good. ACAM is good. These are short. So I don't know what they rate yet. I have to rate them, but those are two ones I will rate in the morning. And apple is a long, I know apple is long without even rating it, but I still will rate it. You can, John, email Paul and email me and I'll send you a trial. You can be in tomorrow morning. This one I'm not going to be doing it with. No data, one tech, it is capping down. I'm sure that it's real. This will trade tomorrow with volume. Not enough information here. CHRW. I'm more convinced. The days to Tuesday, Wednesday, Thursday, Tuesday, Wednesday, Thursday are the best days to trade. Tuesday, Wednesday, Thursday. Let me not care. It's almost like you'll always get something. This is very hard to trade. I don't know what this will rate. I like the other two better. I still will rate this though. This is a hard, hard doc to trade. What do I mean, whippy? Let me just look at the chart. This is like a whip monster. Spreading, whipping, hard to trade. Probably not doing this, but I'll rate it. All right. Have a good night, everyone. Have a great night. Thanks for coming. You are more than welcome to email me if you want to travel to the room, or if you're interested in the class this week and want to learn my method email me. Go to YouTube, subscribe. Sign up to get the updates on there of the market and stuff.  I mean, tomorrow. There's a lot of stuff tonight. Very excited about Google. And maybe I'll do a video on it right now and post it on YouTube. Didn't even know that Apple had earnings tonight. Didn't even know. I've been too busy to even look. Can you believe that? Look, I considered getting out of that last week. Half the position, at least, and I didn't do it. I didn't do that. I had a whole position. No, I didn't. I didn't, but I did look at the econ data. So I knew that Wednesday would be a pivotal day. So you see how everything's coming together. So everything's coming together here. Market's in a rush period here. It's resting. It's taking a nap. It's taking nap. Here, let's just count. Here, if you want to stay, I'm going to do a video right now in the market. So those of you who want to stay, I'll leave the room open. If you want to go, have a good night. I'm going to tape a video on the market right now and post it on YouTube because I want to remember what I'm going to say. Because I haven't done a market video in like more than a week. So just let me put the recorder on.  I'll be right here. I'm going to tell you exactly what I see right now. And I want to tape it for myself or I'll never...


So, hello everyone. Thanks for joining the talk. My name is Andrei. I am a PAD student at the University of Vietnam Alps, also in a partnership with RIAX Technologies. And today, I'm going to talk about scheduling policies for serverless-based Edge Cloud Continuum. More specifically, I will start with a container layer, our scheduling policy, and then some new progress that we have made with a few other objectives. So, this project is also funded by the MIAE project and the physics project, European project. And talking about physics, the goal is to provide a visual programming environment to create serverless workflows. And we had other two talks here at DevConf. One was yesterday, we had this workshop. And tomorrow, Yanis is going to present this other one. So, if you are interested, please join it. And so, to start, I will define a few concepts and then the Edge Cloud Continuum. For us, we understand the Edge Cloud Continuum as this infrastructure composite of several different layers. Called the Cloud Clusters, Edge Clusters, or Edge Resource. On Cloud Clusters, we have the Global Continuum layer, where we have several different clusters. And in the local level, we have the Edge Clusters, our Fog, and the Edge Resource. So, the idea here is that we come from this big view of the clusters and go to the local ones. And of course, on the clusters, we have the big machines that have more power resource. And then, as far as we go to the Edge, we go to less resource and more mobile machines. When we talk about serverless, to define serverless, I would like to do the transition from the cloud to the serverless. And so, when we have cloud platforms in general, we have this generic scenario here, where we have developers that need to develop big applications and deal with all the settings of the platform, and then put their applications to run there.  And the user will pay by the machine hosting time. So as far as it's there, it will pay for that. When we go to the serverless, we have two main points. The first one is that we will split this view of the scenario. And now, the user, the final user, which can be a data scientist, a developer, and so on, we will just see this part of the scenario. So we split in half. And now, the developer just needs to deal with functions, which are much smaller pieces of code instead of whole applications, just small functions, and just a few settings, let's say, amount of CPU, amount of memory. And the second point, the main point of serverless, sorry, OK. And the second main point is that the other part will be full managed by the platform providers. So the platform provider needs to offer and to deliver everything about the platform. So scalability, provisioning of machines, and so on. So the user will just deploy their functions. And the platform will provide everything that is needed to run those functions. And then in the end, the final user will just be targeted by function execution time. And these functions are not staying there as before. Now we are talking about stateless functions and functions that will be triggered by events. So these functions are going to be executed just when needed. So one function is triggered. It's deployed on the platform, and then it's executed. And then with the scheduling policies here, we want to work on a few objectives which can be cost, can be energy, or time. So today I'm going to talk a bit about these three objectives. The first part of this presentation I'm going to show a few results of this paper that we just published at CC Grid on May of this year. So if you are interested, here are the QR codes for our repository with all the reproducible artifacts. So the paper is completely reproducible. So if you're interested, please check. And I'm going to start with that. And then in the end, to present a new step, our recent progress. So as I was talking about serverless computing, here are a few main points that we focus it when working on this first step. First, when we talk about serverless, for us, we talk a lot about containers. So here we are talking about functions that are deployed inside and executed inside containers. These containers deployment are not negligible. So through our studies and a few other papers, we understood that the time to deploy containers sometimes take longer than the function execution by itself. Because on serverless, we are talking about fast execution functions. So we are talking about minutes. And sometimes containers can take longer than that if we are downloaded the container from afar, let's say, cloud and et cetera. But we can share the layers of the containers. So in this first project, the idea is to share the container layers, even if you are not talking about the same containers. So here we have the infrastructure of the Cloud Continuum that I presented and our motivation. So we want to reduce the amount of data downloaded to deploy the containers and also the amount of data downloaded to upload and download functions, input, and output. So in this scenario, we can say that functions are triggered from the edge, are deployed here on the local level, but the containers come from the cloud. So we want to reduce the amount of data managed on both directions. And when we talk about the containers, we want to reduce the total amount of download. How we do that? We proposed a function orchestration algorithm, so we called it FOA, that it's based on a linear program that will optimize the amount of data downloaded, respecting a constraint of makespan. So the idea of this linear program is that we optimize the entire replacement of the functions on the platforms, respecting a constraint of makespan. And if we are not happy with the output of this linear program in terms of makespan, then we are going to run this looping here, going from the output of the second step and coming back to the linear program, reducing our restriction of makespan. So let's say we optimize it, the whole containers placement, but still it outputs a solution that will take hours to be executed. So then we take this output, we say, no, I want this in half, this makespan, cut it in half, and try to compute a new solution. And then we go on and on as far as we are satisfied with the solution. Then we will have here, we arrive at step three, when we will optimize the download of the layers.  A few reference for the linear program and the minimal cost integration, it's based on a dual approximation algorithm of Moisan Tardosh, he's the paper here. And our experimental protocol. We are doing it through simulations. And to do that, first, we adapted functions on a benchmark called a function bench. We deployed a serverless platform called OpenWisk on top of an academic cluster called Grid5000 in France. And with that, we measured and calibrated the results. So we run this benchmark for a while, calibrated how long each function takes, and then we could build our workloads for our simulations. Why? Because we are running a linear program, so it's an offline approach. So we need to know every information in advance. To evaluate our scheduling policies, we have this simulated environment on top of Batsin and Syngrid, with our combination of a batch scheduling simulator. And as I mentioned, everything is open source and available. Our design of experiments, we varied sizes of workload, size of platforms, heterogeneity level. Here, we also investigate the heterogeneity of the platforms by changing the CPU speed. Right now, we are talking about light levels of heterogeneity by changing the CPU speed of the machines. So when I say that we have different levels of heterogeneity, it means different clusters, the number of different clusters that have the same CPU machine. We varied our two scheduling policies, FOA, and Kubernetes image locality. We tried to reproduce Kubernetes image locality policy, which is basically a first come, first serve approach. Taking into account the image, the container image. So Kubernetes, we will check if on the platform we already have the container deployed in some machine. And if it is there, it will use the same node. But it just looks for the entire containers. Here is our novelty that we want to check also the layers of the containers, not on entire containers. Here is the table of all the functions that we adapted from function bench and the different input values that we're using. So we have functions like mathematical functions like a million float operation, link pack, metrics, multiplication, and also another functions like image processing, video processing, and so on. Here is the simulated infrastructure of Batsyn and Syngrid. So the idea is that Batsyn will run the simulation on the first big square. And then Syngrid will perform the simulation on the real infrastructure. So they communicate to that. And we needed to add a few layers on top of these tools to perform like a serverless platform. So we added an extra layer here on the profiles of the workloads of Batsyn to use container layers. And so here to show a few open source contributions that we did on the project here are the function bench. Here is the function bench repository with our functions already merged there.  The figures that I'm going to show now will follow this structure. By the small facets here, the small squares, we combine workload size, platform size, and with the X here, we are showing the makespan. And on the IX, we are showing the amount of data downloaded. So what we have on this figure are the Pareto curves where we investigated the trade-off between reducing the amount of data download and the makespan. What does it mean? It means that we cannot reduce both at the same time in an optimal way. So if you want to reduce more makespan, OK, we need to relax a bit on the amount of data downloaded. If you want to optimize the amount of data downloaded, we need to relax a bit the makespan. And here with the colors, we are showing the repetitions of our algorithm that I illustrated in the first step. So the idea is that all the first solutions are here on the extreme right with the best cost with the best cost. So with the minimum cost, but with big makespan. So as I mentioned, if you are not satisfied with the makespan, we constraint it in half. And then we go to step two and we perform the algorithm. So we can see that we reduce the makespan, but increase a bit the amount of data downloaded.  And then the last iterations show the best makespan, but the worst amount of data downloaded. So what we conclude here is in all scenarios, about three or four repetitions are enough for us. We don't go to the last one, because we will lose in terms of amount of data downloaded. And then now I'm going to show the specific objectives that we had. So here I'm comparing, again, with the same structure of combining workload and platform size. But now in the x-axe, I'm showing the heterogenic level of the platforms. And in the I-axe, I'm showing the amount of data downloaded. So here we are seeing the difference between our approach for and our baseline in terms of amount of data downloaded. And what we can see is that for outperforms image locality in almost two levels of magnitude in terms of this objective. So yes, FOA can do much better, because Kubernetes image locality actually has this grid approach. And we are focused on reduce this amount of data downloaded. What we can see also with the different combinations of size is that when we have very loaded platforms, we don't have too much choice. Because if we have, for example, here, we have 10 functions per machine. So we have a lot of functions, small platforms. We don't have too much options to say, OK, I want these functions to go here or there. But when we have a lot of options, FOA can behave much better. And the next result is the number of machines used. Here, it was not one of the objectives. Again, the objective was to reduce makespan and amount of data downloaded. And in the end, analyzing the results, we just checked that we did desoptimization at the same time that we used less machines. So in terms of serverless computing, where you pay for as far as you use, it's a much very good result. So we use less machines and optimize both parameters. So the conclusions at this part is that grid algorithms may not profit from the heterogeneous. Yeah, sorry, I forgot to mention that. We can see that as far as we change the heterogeneity of the platforms, the grid algorithm does not change the behavior, while we can see that FOA can adapt a bit better. So they may not profit from heterogeneity. FOA outperformed the baseline in terms of data transfers makespan in addition to system optimization, which means the number of machine used by up to two orders of magnitude. And FOA then minimizes code start delays. So if we minimize the amount of data downloaded, it means that we are reusing more and more container layers. So we optimize code start delays and it speedups function execution. So if we don't spend too much time deploying containers, we start the functions as soon as possible. However, at this step FOA is still very time consuming because it's based on a linear program which has many variables and it performs in order of minutes while our baseline perform in order of seconds. So this is the biggest trade-off or our approach. And so this is one of the main points that we work on this next step that I'm going to present now. So the point now is this was the motivation and let's say the objective of the first part. We want to continue with the good results. We want to improve it FOA in terms of performance. And we added one more objective to this multi-objective approach. We want also to reduce the energy consumption of the platform. So now we do that by splitting our scheduling policy in two phases. So now we are talking about the two levels of scheduling policies. One that will run on the global continuum. So at this step we are going to decide in which cluster each function are going to run. And then at the local level, we will decide in which machine inside such a cluster this function will run. So we do that by reducing at the global level the energy consumption and the function execution time. And at the local level, we are going to use our layers, our scheduling policy to optimize it. At first, since we are going to work with energy consumption, we have started to try to use the Kepler. So we are in touch with the community and the work is in progress to be able to use it. And I'm going to show a bit how we, these investigations in terms of energy consumption. Since we do not have yet something to run on our Kubernetes platforms yet, we are using power wattmeters on direct on machines to analyze the energy consumption at the first step. Again, to model our workloads, to prepare the inputs for our new linear program. So the first step is to analyze the energy consumption. Here we have an instance with three nodes with open-wisk running on it. And we have the first node is the master and we have two workers, but just the third work was used. And so we can see that the two nodes that are, let's say idle are still very energy consuming. But here we can see that the functions are running and then if we cut this and zoom it, we have this figure on the right. So here I'm analyzing the energy consumption, not the energy consumption yet, sorry, the instant power per time for different kind of functions. And then with that we computed the energy consumption by computing the integral of this area. Before that, even if we zoom a little bit more on a few functions, we can see the behaviors of the functions. So we can see here, our first investigations point that, for example, we have a few spikes at the beginning of each execution. And these spikes may become from the containers deployment. So the preparation of the environment to execute the function may consume a few more of energy and then we execute the functions. Here I'm showing different input sizes for the same function. So link back on left and Camille on the right. Okay, so once we have it, we computed the energy consumption, then we remodeled our workloads and we came back to the same steps that are the same environment setup that I showed previously. So we executed everything on top of OpenWis can read 5,000 model workloads and then we performed the simulations with the new algorithm. So it's still a linear program. We still do the repetitions to optimize the makespan as far as we are not satisfied with the final makespan of the solution. And now I'm going to show a few results. So the same structure for the plots with the combinations of workload size and platform size. We can see that first, we still do much better in terms of amount of data downloaded. Here, now our baseline is not anymore like Kubernetes image locality, but because now we are talking about two levels of scheduling policy. So our baseline also has two levels of scheduling policy. So now we implemented a first come first sever at the global level and then cache image locality on local level. So the same, we are optimizing a lot in terms of amount of data downloaded. In scenarios that are not that loaded, we don't have a big difference, but when we can save resources and improve these objectives, we can do that. Number of machine users, the same. We also reduce the number of machine users. As a grid algorithm, our baselines tends to use all the machines available, but we not. And the most important part right now is the energy consumption. So with our preliminary experiments in this direction, we already have good results showing that we can reduce the energy consumption in the median, like half in the median in all the scenarios. And we are going to continue on that. So the conclusions for the second part is still, the grid algorithm may not follow the difference of the heterogeneity of our platform, while FOA does. The new FOA, FOA E outperforms the baseline for data transfer and energy consumption in addition to yet reduce the system utilization, but now we lost in terms of makespan. So now we are not doing better than the baseline in terms of makespan yet, so this is the one of the directions that we are going to work on. Again, we minimize code start delays by minimizing the amount of data downloaded. And now the very good new is that FOA performs in terms of seconds and not anymore in terms of minutes. So it was one of the goals between our two steps, because now we can say that FOA is reasonable to run on made products on Kubernetes. So future work. Sorry, to continue improve FOA's model and try also other linear solvers. To study applications that can be modeled as workflows, because right now we are talking about stateless functions. We are talking about batch of stateless functions. We want to include Kepler for a continuous measurement of the energy consumption. So if we can continuously measure the energy consumption of the platform, maybe we can turn from an offline approach to online approach. And to continue this investigation towards the reduction of the energy consumption. As I showed, the platform by itself, it's also energy consuming and we did not take that into account yet in our model. We are just optimizing the functions. So optimizing also the platform, maybe one of the directions. And we are working on the real implementation on Kubernetes for both of our scheduling policies. Imagine locality is already in progress to already schedule talks on the community of Kubernetes to put it on the main branch of Kubernetes. And FOA is in on development. So thank you very much. Again, this is the reference for the paper that we published for the first part. Please scan the QR code if you are interested. And then on my GitHub repository, you can also found the results for these new steps that I presented today. Thank you very much and I will be happy to answer if you have any questions.


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