And our next speaker that I'm very happy to introduce to you is Giovanni Carrada, who is a biologist by training, but is a science journalist by profession. And Giovanni, beside having been one of the authors of super quark, which is a very popular science program on the Italian public TV, and very successful I'm going to say, is also I think one of the people who have given most thought as to how to deal with the communicating science and innovation in the food system and in the agricultural system. So today he will start our war show by perhaps taking a reverse approach so we start by how to explain what we do and then we'll describe what we do. And his talk is entitled the power of meaning. Thank you Giovanni, the floor is yours. Thank you Michele, for inviting me to speak here today. Why the power of meaning because people in the sciences people trained in the scientists believe in the power of data or facts for good reason of course. But we are entering now in a very different domain, where the power occurs to meaning much more than to facts and numbers and data. Why because we, as I think we'll, I will be developing these about I think the next speakers we will get into it a bit more, we need a turnaround in the way people perceive innovation in the food sector. And if we don't get this turnaround, we're going to have serious problems in using the very technologies that might help make the food system more sustainable. And so, I would go here it is the next one. Okay, so what one thing that's been it has been briefly mentioned before is that people the public doesn't have any perception of the huge impact of food production on the environment. It has always had it it's always been the the by far the biggest single factor affecting the environment, especially through land use the biodiversity crisis is a is a result of huge land use and converting natural highly ecosystems into simplified frail and ecosystem which, for example, do not stop carbon. It is a is we're talking as we can see from the slides about huge amounts of land that have been lost mostly to forests. We have devoted to food production as a, as in all areas of sustainability, real progress will be made. Yes, of course through changes in people's habits in behaviors, but these can go on so far, especially in food. And we need new technologies new ways to do things which can give us the same amount of food with at a lesser cost for the environment as a lower cost so we have to effectively the cop tried to decouple our food production from the natural resources that we have to share with a biosphere and land is by far the the foremost factor, but so we need innovation within a lot of innovation, but the, the great paradox of food is that is yet by a few is our it is arguably the only sector in our economy, where something new is perceived as being inherently bad and not even inherently good. If you think about the all the marketing strategies of the, of the food sector is this it's the, the, the, the arc or more or more progress goes backwards, not forwards. Whatever innovation is being done. It's an in lots of areas it is a lot of innovation. It is being kept hidden carefully hidden that just think of the wine sector. In Italy is a huge economic importance it's the last 30 years is completely innovated in every aspect, but nobody ever speaks about it. So we, the public at large have developed this idea that there is something inherently bad in the new way in everything that has been done in the last 100 years. It's a very widespread notion, which is not very helpful when you have to innovate, because you find resistance at every stage. So, this is a condition in which incumbents, which is people doing all things and have one who have an economic and financial interest in doing things the old way have huge way sway. It has always been like this in all kinds of innovation historically. What happens is that when you have a new technology, the proponents of old ones usually debated publicly in the name of health safety and to do also the environment. What we have seen is a, so it is a condition in which opposition to innovation can have usually have the upper end, and in which innovators find it very, very difficult to have their voices heard. So what we have to do is in a way to make food production normally gay what does it mean. It means that every innovation is judged on a scientific basis on a case by case basis by science based regulatory agencies, instead of being judged and allowed or banned, according to political criteria, which in the food market as it seems to be the normal way to deal with things in this area. The GMO controversy has been mentioned already. It is the only way it's one of the very few cases, maybe the only one in which a whole technology has been labeled in a certain way and effectively banned. Without listening to the advice of the very regulatory agencies, for example, here in Europe. So, the topic of my short talk would be about how to make food production normal again, that it is to be judged on a fact, a factually on a case by case basis. It's very important today, because as you have said the impact of food production is huge. Professor Morgan that mentioned that 38% of our missions were the missions are somehow related to food production and distribution and everything. But we have at least three big technologies that can be real game changers. The first one as we mentioned is genome editing, which can progressively help adapt plants to the environment rather than the other way around, which is what we've been doing until now, which is why we have such a big environmental impact. And if you think about it, genetic technologies, which you have seen as we have seen, being the biggest contribution to the progress of our agriculture are among the only, maybe the only ultra technique that did not have any significant negative impacts, just think of all the others fertilization, lots of energy, tilling erosion and loss of carbon pesticides, etc. So it's a, it's a, it's a field that in the public section is really seen upside down, really an upside down field. So we have genome editing, which is already suffering all the opposition that GMOs met in the past, but which is huge. So it has huge importance for adapting the plants, and also of course for increasing productivity, because another paradox is that the word intensification is seen as something bad in public perception. If you think about it, the higher the productivity of land, the less land we need. And this is not just an hypothesis. It is what has is already happening in advanced countries. In this country in Italy, we have four times the amount of forest we had a century ago, four times as much. And in the last 10 years, forests have been growing at the rate of 60,000 hectares a year, due to increasing intensification of food production. So technological advancement due to innovation, it's a hundred thousand actors a year in Europe. The same is happening in the United States, in Canada and all the other advanced countries so we're talking about very big things. Just think that in Italy, growing new forests is a response for observing about between 15% and 20% of our greenhouse emissions. So it's a huge contribution. Just think that just by extending our innovation, existing innovation to the world, we have huge effects. Then there is cellular agriculture, cultured meat, cultured fish, cultured coffee has already been made, coffee without passing through the plant or a meat without passing through the animal. Just think about the human, the huge suffering of these animals, but from the point of view of sustainability, it's something that is truly disruptive because the amount of land and resources that all inputs needed to make cultured meat or fish is a tiny amount of the overall impact. So we have things, tools that might really revolutionize all this field, but it is increasingly meeting a lot of resistance, resistance which mainly comes from, of course, all the established industries in the agro sector. We see, we've seen, we've been seeing it in Italy very, very clearly and it is being done with very same arguments that were made 30 years ago against GMOs. They have never been proved true, as we have seen after 30 years of experience. And it is effectively preventing us from making the biggest contribution we can make to the sustainability of food systems. The engine dries, I would add, it's still not mature technology, it still has to prove itself fully, but it has the potential to wipe out all the pests in agriculture, especially insects but also rodents, which is a major source of waste in the food system. And with it we could do without the huge amounts of pesticides with all that it entails in terms of impact that are currently used in agriculture. So we are talking about really big things which are meeting this kind of a position to new things. What are these misperceptions made of and here. So partly it's played ignorance. Part is that we are very vulnerable to threats that are being made to the purity, the integrity of our food. We have it is an internet mechanism in our that it was as evolved to protect us from food contamination, which is impossible, a very serious problem. So whenever one raises a specter or something bad in our food, whether found it in facts or not, we are very careful. We have a strong preference for the tried and true what is in our food tradition, good reasons of course because it was foods that have already been tried by someone else in the past. So traditional foods have, we are so it means we are very conservative when it comes to food. In this case about in the case of food technologies several decades now a very negative communication of things being said against innovation in food. Part of these things are founded of course because modern agriculture had very heavy impact, not just on the environment we're also on cultural traditions in many places. So this is, we might think about also it's an interesting thing to think about why food has become such a lightning road for anxieties. If you think about it, food is the only maybe the only part technology, the only product of modern technology that we can actually choose. Whether to eat a particular food or not, but we can't do without the smartphones or other kinds of innovation that some, they are somehow forced on us. So we have concentrated on on food a lot of expectations and anxieties that we don't attach to other technologies. So it's a very sensitive issue. We have powerful cultural trends and the foremost one is a cultural trend that makes us surprised everything that is or seems to be natural. This is also a byproduct of our anxieties about the future. It's characteristic of every affluent age and age in which changes are rapid. And it is very misleading because it has been applied in this debate in food to agriculture that is being branded as something natural, but it isn't at all. Agriculture is a very artificial thing. But it has been framed is a very important word where we are going to talk a lot about it as something natural. And in our culture today, natural means good, fair. And in a way, something divine. We are in a secularized societies, we have been in a way turning nature into a God. And when nature is a God, everything they does are we think it does. It's perfect. We can change it. This plays also on a lot of moral lashing religious intuitions that in a way compel us to feel that meddling with nature is something that should not be done. So we think that all the agriculture we've been doing is natural. So we don't have to do to change what we've been doing. And this makes us blind to the huge environmental impact that for thousands of years has agriculture has had. Especially for example, in countries as densely populated as Italy in the late middle ages, Tuscany was almost three less erosion was huge. And there are lots of data about coast and things that demonstrate this. And of course there are lots of cognitive biases that prevent us from changing our mind. And all the communication of incumbents, all the people who have an interest in opposing innovation in food production and always played on this. And you can't challenge these things directly. There is no way, once we have made up our mind and we have and these are, these are very deeply convictions we all have that I have also you can challenge them directly. So what all these things have in common all these psychological mechanisms we've been talking about is that they are very impervious to data to arguments made of data and facts. And this is a lesson that not just the agricultural Genesis community has learned the hard way but lots of other just think of a vaccine science. You don't challenge these convictions is they're made of emotions are very deep seated emotions with data and facts you need something else. We assume the famous 18th century Scottish philosophers and understood it three centuries ago, and cognitive scientists are rediscovering this lesson now. We decide with our emotions, and then use rationality to rationalize away what we have already decided. So you have to conquer hearts first, and then you can conquer minds. In my work as a science communicator. This is the number one lesson, unless you open the door of the emotions packs don't just don't get through. Let's say, have an example that we all know of of the power in this case of just three letters the acronym GMO to frame I was thinking without our even perceiving this. If you label plants go some plants genetically modified organ is what we all think is that all other plants have never been genetically modified. And so that is it is must be something new and strange and something if it is related to food that we should be very suspicious about. This has been the foundation of all the controversy. But if you think about it, genetic modification random genetic modification continuous, continual and random genetic modification is at the heart of life mutations. So the recombination of genes is random evolution is genetic check continual genetic change, but it, we never think about it when we hear you here and use the word GMO. So it is very misleading, and we don't even know that we are being misled. So this is a very small example of what we can do to use these rhetoric tools in a more positive ways. This is my very small contribution to the field, but you can use it consider it a proof of concept of the power of what I'm going to say is that when Italy's agriculture genetics is asked me. Well, we have a problem because they are labeling. And they did it plants as the new GMOs, what can we do. So they're trying to take all the prejudice that surrounded GMOs to this new very different technology, and if they manage these, that's the end for public perception. And so how can we call them these new plans and I said, don't call them with any new way, however clever, because he would set them apart from all other plans. So we should have a new name for the technology. Nobody wants in this in this culture climate to have their food engineered. For example, with biotechnology or new breeding techniques. You said, well, we call we need a new name. So I came up this is assisted evolution technologies, which is suggest a gentle nudge to nature, which is actually the truth. You produce a desired mutation that might have come about naturally. And it's, it reminds us of assisted. And you have, when you need to have babies, artificial, the artificial. And which is now be seen as something positive an act of law to have new children and these small trick together with a package of rhetoric new rhetorical package, but how to talk about genome editing in relation to the needs of Italian agriculture It worked. It was a part of an effort that was made by our geneticists and talking to very important stakeholders in the agricultural sector, and they managed to convince the, especially the biggest they had been staunch opposers of GMOs to side with this new technology. So it worked. It was a way to talk about this new technology that in a way was not did not stride against all this psychological perceptions we all have, and it helped convince a lot of stakeholders. That indeed, maybe Italy that does not need GMOs, but these new technologies what we need at this moment. So we had these, we changed these the perception of these technologies and it happened and it happened rather quickly. It was a very small turnaround, but I said it's a proof of concept of something bigger that we should be doing. So, in order to make food innovation normal again, we need what I call a creative program to find a number of framing premiums ways to reframe technology and reframe this issue in a different way. It's one of the people's minds framing premium. What is the term I, I borrowed from Bill Gates book on climate change is that we need new technologies with a green premium which is called the, the, the, the financial reward of using new clean technology, and not all their technologies. We have this, we need a framing premium and a clever new ways to talk about these issues that help overcome all these layers of those layers of prejudice. We all have. I will briefly go through what I think is at the wish list in this creative program that I think should be done. The first is that the ecological transition should be framed as a change in the technology we use. It seems almost obvious, but it is not in, in, in public discourse. The ecological transition is very often seen as a kind of paling Genesis, in which we have to change everything. And it has also some political undertones, which are not helpful in this way, while we, what is actually happening is we already doing our ecological transition has already happened. The countries are lowering their impact and emissions year by year. It's something that's been happening for 15 years now in the 33 biggest economies, it's already like this. So, we need to change technologies. So the focus should be on technology change of technology. And the last step said, okay, which technologies. So it's not, it's not about going back by, it's about going forward. So the second thing is frame and environmental progress of the coupling from nature, not competing for the same resources, especially land. So we can't go back to nature. There is a very widespread notion that by going back to the good old days, everything will be fine again, which is a very reactionary thing to say, if you think about it, and it's not true. In the good old days, we had two little food of very bad quality, and we had huge environmental impact. It was amazing when I went to the, the highlands of Ethiopia, the agriculture is still in a way almost to integrate the, as a stage of neolithic, and there was not a single tree, they had to plant every, every small piece of land to. So we have to go forward. So we have to reframe farming, people don't know that it's that farming food production is the biggest impact on the environment. And if you don't know this, you don't have this piece of information, you'll never feel the need of innovation. So with it, we are, we are, we can convince ourselves that we have to change only if we have something very big is pushing us. And if it until this piece of information is not out there. There will be known, people will say why change, we have food is good, maybe we're eating too much, why change, because I don't know about this impact. So farming is something artificial, we will be doing it only in the last 10,000 years is nothing. And productivity is a concept that is not out there also. We see productivity as something bad, and the use of the term sustainable intensification which is used by FAO to describe something good. The use of people is something bad. We have become accustomed to consider intensification something bad. We think about it is about more input, more water, more fertilizer, more energy. Well, it's actually the reverse. So you have to be very careful at the word you use when you talk about this. Well, the words that are being used are considered normal in the scientific community out there can have can be perceived in a completely different way. And all these innovations together with digital innovation mean that we are at a turning point in agriculture where we come from a century in which we plant and be cultivated in the same way with the same varieties everywhere. Because of the limits of the technologies we had to make, it was very difficult and expensive to have to make a new variety. Now with genome editing is going to change completely. At a time when we can match each plant each new variety not just with specific environment, but also to a culture. So we'll be able to improve lots of other species and varieties that are in the traditions of cultural traditions of every part of the world. So what many of the things we did not like in in industrial farming, finished. And we have to be very clear that this is a possibility that the technology gives us. It's not more of the same, not still more industrial agriculture is a different thing. So frame innovation as solution also for countries, of course it's been mentioned by Professor Morgan to reframe by a technology the tools to restore the natural order, as we've seen the, the semi religious idea that you don't have to touch, touch what has been created. It's not something we're going to change so easily. So we have to emphasize the fact that what we can do with the new technology is just continue to adapt the existing thing we're not changing everything. We are just adopting what we already have. And so it's not, it's a new beginning. What we have seen how many things we already have in our minds all of us about food innovation. So we said we can change this. So we need to find something that creates something of a blank slate in people's mind. That's the opportunity also of the new technologies. So what we have to think is to be, we should be very clear in saying, and it is the truth that's what the technologies we've been talking about can promise. Put an end to what the industrial culture we have been we have known for the last 100 years. There is something new. And people must be very clearly made aware of this. And, of course, these are these new rhetorical tools have much in common, for example with political communication to have, we have to be very careful in using it in a very ethical way. I mean, which is the way I think we managed to do with assisted evolution techniques. So it takes a lot of discussion and scientific community about this new rhetoric that we have to craft. But it's, if you don't have an effective rhetoric, your device will not be heard. So it must be effective, but it must be scientific community and the innovative community must keep its integrity. And the most important lesson I think is I put it as the last slide at this problem with not solve itself. I've been dealing with this issues for the last 25 years. And I've seen the whole innovation communities blind to this problem, effectively blind. There has been a lot of discussion, lamentations, but very little. The most important in less than I think is that today, if we need to, if you want to innovate and radically lower the impact of food production, the communication problem is more important than the technical problem. Thank you. Thank you very much Giovanni for the very interesting presentation. We have time for a couple of questions from the floor or from the people online. Do we have questions online. Yes, we have a question. Do you have any resources or training you could recommend on genome editing for increased production of food and or plants metabolites, which can be developed for treatment of disease affecting humans and other animals. There is very little, very little. This slide week is exactly the point I want to, I just made at the end of my presentation. This is a very neglected problem. And unless an effort is made to study this problem and find creative communication solutions, not going anywhere. Unfortunately that is not. The intellectual property of the technology which is created. Thank you. And so I will explain my question a bit more I totally agree with you that genetically modified organisms does nothing new. It is as old as agriculture is farmers select and by selecting they modify what has maybe changed in the last 100 years is then giving the intellectual property so patent rights these kind of things to who has created that initial investment. It is logical you invest you need to have a return on profit. There are many people who would criticize the fact that a lot of this was basically a common good, the biodiversity, which farmers had shared amongst each other had traded for for millennia, which then becomes private property of a private institution. So how do you think we could solve some of the tensions or avoid some of the tensions that arise from this, without limiting investment. So it's a kind of balance. The answer is, I don't know, because I'm not in the scene but it's something that can be discussed and solved. I mean it's a kind of it's a problem of regulation. And but so it's not a problem of communication but something that is usually forgotten all this discussion is that it is true that, for example, genetic traits come come from traditional agriculture. But until they are used in a new context that they don't have the same factor. So, every time there is innovation. There are profits from the person who sell this innovation. The very biggest part I remember some figures from economist works is that is what everyone gains by using that innovation. This is always forgotten everybody thinks of the other profits. And sometimes, of course, they must be regulated better. I'm sure about that. We always forget the benefits that we get from better seeds for example, which is my if you see the overall benefit is much higher than any financial gain that a company can make. We all we all benefit. And this, of course, we have to find ways to to give part of this back to the but it's a question of regulation on a public perception. For example, I'm also a farmer I own a farm and one of the reasons I would probably not or I'm sometimes reticent and I come from, you know, Italy so we tend to buy our seeds every year. We don't really conserve seeds, but I've worked a lot of time in Africa and South America, where reusing seeds is a very important part of their economic. So why would a farmer buy GMO so it's which then they cannot replant in many cases. Now the Terminator gene is normally used much. No, but there is an intellectual property. It's sometimes you're going from a kind of self sufficiency to you are very much affected by current economics so by the price of seeds, you then have to use pesticides and fertilizers use a lot of petroleum based inputs, which are very cost. So, sometimes we need to think more about the small older farmer and trying to push them to then use this innovation because they're scared of this innovation, because they tend to lose control. They are very much then dependent on the third parties on who sells them the seeds and fertilizers and so forth. This is a regulation problem. It's not a technical problem. No, but you were talking about the perception and the perception comes from a lack of relation. So that has to be also solved from a communication point of view on the users on the farmers. So there's the consumers who eat the food but also you have to convince the farmers. And so in this also I think we need a bit more communication and in order to create that communication we need a bit more regulation or the regulation has to be a bit geared towards more more vulnerable actors in that food system, which are the ones. Not only innovation is made by big companies in Italy for example a lot of it is made by public institutions and the amazing thing that happened for example with all the general issues is that all the research made by public research was lost. These new regulations crushed all the public research, all the startup research, and the cost became so high that only a few big companies could compete. And that was the result of the position is exactly the opposite that was a foreseen which is makes you think. Okay, thank you sir for the impact what you have made. Like in Africa, the issue of GMO still becomes a big problem, like every place in the world. I specifically will agree with you 100% on the issue of information. Like where I work my institute we invest so much on information to spread the information to the masses. GMO for me is nothing new. Because as long as we exist the world evolves, Eman evolves, plants evolves. So we just modify some few parts to see how we can feed ourselves and make ourselves more sustainable than others. And also another point I looked at is the impact of agriculture in environment. I've worked on environment science for quite many years now. It is huge, especially in industrialized country in Africa is still not a big problem, but in terms of substance farming, but if you turn the way around in a commercial agriculture is a serious issue. And then from solid Russian land degradation environmental pollution contribution to climate change and even pollution of the food we eat, and that has translated into so many cancerous diseases and some new hybrid disease that we are still finding solutions to like scientists. So the output I looked at from what you have done is excellent because number one you have been able to touch the very one thing that is causing problem ignorance of what a modification of plan does it doesn't do anything harm. It makes us more sustainable so that we can move to what I like to talk about a type one civilization because if you still do what the forefather did, like what they say the good old days. Do you want to start drinking? Don't you like aircraft? Don't you like iPhone? So going back to the good old days for me is maybe they are not aware of the impact science has done. They are aware of the iPhone they are with. And number two thing for me is the modalities to cut down this environmental destruction that agriculture has made. That means developing new tools to solve new problems because there is always a new problem. And how can these tools go down to the end users? And for them to understand these tools, for them to use these tools, and for them to apply it in various policies, which is very, very important because for you to do anything you do and succeed in any country, it must be backed by policies. So let me stop at the point of policy. So I will love us to include a kind of a policy strategy so that we, the government of each land can key into it and execute this very important tool that we are talking about here today. Thank you so much. If I correctly decide your question, your point is, I might answer that you don't have to turn citizens into small experts. You'll never be able to do that. You just have to reframe, as I said, some key concepts, which I've been talking about.  So yeah, you should hopefully do it as little as possible of it. So the one you make might be very productive. And also that there is an arc, as in industry, the first way was lots of pollution, lots of problems, lots of unintended consequences, and that things got better. I think in agriculture is going through the same thing. I mean that if you see the first wave of the Green Revolution and the 50s and 60s, the very impactful, but then I see it clearly in Italy, though, you always find ways to improve you learn from mistakes, to lower the impact gradually. So as new technologies are adopted, things improve. The problem is that we don't perceive progressive improvements clearly. But that's why we need to, we need a clean cut in communication. So, okay, let's get rid of industrial agriculture. We have new technologies that can address the things we don't like, we've never liked about it. So we have a new thing. When you have a new thing, you have thrown away, hopefully you get rid of all those prejudices, and you can have a new playing field, a more level playing field. Now, I'm not a techno enthusiast at all costs, but it must be normal again, as I said, so everything will be judged each application. What was wrong with it with the, with the GMO issue that was, they were all lumped together as a single thing, as if we lumped all drugs in the same way. It just goes absurd. We have to regulate each one and evaluate each one carefully on a scientific basis. That's what I advocate. So, also with respect to all the other problems, each thing that we discussed and seen and not lumped together in a way that actually only helps incumbents and prevents innovation and gets and the worst thing that they left all the problems and touched. Last questions, but try to be short because we don't have time. Okay, thank you for the chance. My personal question is, do we worry about nature? The answer is no. We all are not worrying about nature. Why I say this one, as we know that agriculture and biodiversity, they are very important. Agriculture is very important because it generates different types of food crops. Without our culture, we can't even live. So our daily food shortage is covered by the culture. The other thing is biodiversity issue, because I am from ecology part, so there are different plants, different animals from the lower to the higher. So they are very important for nature as well as they are very important for us, every nature. So, for example, if you take in my country, developing countries, for example, Ethiopia, I came from Ethiopia, so that in Ethiopia, there are so many types of degradation, forest degradation, water degradations, different plant species degradations. There are different legs are now currently degraded because of agriculture, because of some different herbicides, for example, herbicides actually they are very important, but at the other side, they destroy the plant species in the crops. So we are not selective currently. So, from your point of view, from your talk, I really appreciate. So how we are going to worry about nature, we have to worry, we have to think about nature. Nature are very important. Water resources are very important. So we have to keep them. So the main problem is the management issues, the management, the regulation of different countries, how they are serious on resources, are they serious? If they are serious, we are also very serious. The farmers, the rural farmers, without training, technologies are in the rural community, they have to know, they have to train. Otherwise, no training, no well management, rather than it is destruction. So we have to keep, we have to worry about nature. Thank you. Yeah, you're talking about the importance of conserving also the biodiversity, the natural biodiversity. I think this is something that is overlooked, the importance of more resilient ecosystems. We are going through a climate change, which will not be able to avoid all together, I'm afraid, and we need to, to be prepared with more resilient ecosystems and more resilience farming systems. We have more resilient farming systems, if we can rapidly have the right agricultural biodiversity, which is different from the natural biodiversity, which we need, if we want to have resilient natural ecosystems, so it's very important. I think we'll have to finish the discussion, thanks a lot Giovanni for the very interesting contribution, and we'll move on into the technologies that can help agriculture to be more sustainable. And the next presentation will be by Pierre Domenico Perrata, I'm very happy.


I'm Jay Fidel in the Honolulu think-tech studio, and I just wanted to bring something to your attention if you didn't know it already. It's all over the wire now, it just started happening. I saw it on the Star Advertiser, I'm pleased that they cover breaking news that's really breaking news rather than sports or congestion on the highway. And what it is is that something over 70 people were killed not too long ago at the Bastille Day celebration, you know, August 14th. Like that July 14th, Javier Kettos in Nice in the celebration after the fireworks there and some fellow drove a truck into a crowd, trying to kill as many people as possible and then when that wasn't enough he got some weapons out, assault rifles and started shooting some more and he hit an all-time record for one of these attacks, 73 I heard most recently, just when you thought, you know, that there was some save. The problem with these attacks in Europe is that you like to kind of ignore them, you like them to, you know, stop already, but the reality is they keep happening and the reality is they all happen by terrorist organizations, you know, they don't know for sure yet but I'm betting it's ISIS. And Nice is a beautiful city and the Bastille Day celebration is such an important part of French culture and it's so sad, so sad that this happened and it is in such great disregard of everything French and France in general. This is worse than the ones before and they keep happening and I'm so sad and I think we all ought to be sad and the problem is we don't know exactly, you know, how to stop this. We've got to focus on how to stop it, okay? We can't keep having these headlines. Anyway, if you haven't seen it, check it out. I'm sure it'll be all over the news. It's today's bad news. Aloha. I'm Jay Fiedel.


What's up everyone take down here back with another video today. I'm going to be sharing the top games on the PlayStation 4 Let's get right into this Okay, so up first is Detroit become human I've talked about this before on the channel and I did end up streaming it back whenever I was playing it But this is a game back whenever it first came out I really wanted but I ended up passing on it because I was playing a bunch of other games at the time And I did not want to purchase this game and not have time for my other ones But over the summer of 2019 it ended up being free for the PlayStation Plus So I ended up getting it and honestly it was a lot of fun And I really wish I did buy it back whenever it first came out, but I'm glad I ended up getting to play it anyways It is basically a decision-making game where the decisions you make in the game actually matter And that's something that means a lot for me for story and the story in the gameplay and everything else was awesome As well the decisions you make in the game affect the whole game and it affects how not only the public reacts to the androids But also how people feel about you and other androids and humanoids So it is something that is a lot of fun And also you can end up getting a main character killed in the beginning of the game By making the wrong decisions or any of the helping characters they can get killed off as well There's a lot to it the mechanics were fun And there's a lot of trophies for this game You do end up having to play it a couple times to get all the trophies because depending on the The decisions you make different things like the collectibles the magazines are going to be there or the different outcomes of Each event are going to be different and some of the trophies are specific And you have to make specific choices at each part to unlock that trophy So it is a lot of fun. It is something that you would be able to platinum I haven't been able to yet. It's something i'm working towards But it is a lot of fun in one of my favorite games on the playstation 4 Next up is marvel's spider-man. This is the second most sold game on the playstation 4 And honestly I see why it came out in 2018 and it honestly is a lot of fun The mechanics of spider-man and playing as spider-man is awesome. There is a stan lee cameo, which I thought was pretty epic But the mechanics the web slinging slinging abilities Swinging from the webs was so far advanced from previous games That it really was awesome and it really reminded me of the early games but with advanced and current age graphics The web abilities the story The web gadgets everything was amazing the supervillains in the game. I thought they could add a little bit more of basically spider-man's biggest threats and biggest Villains, but I really wish that they had Venom Sandman maybe the goblin a little bit more in depth would have been epic But I do like who they put in the game I think in my opinion there will be a second game because if you watch the end trailer or the end cutscenes of the game If you made it to the end of the game There is a lot of what ifs and I wonder if this will happen or that will happen So it's definitely something that I could see them making a second game and continuing the story with everything and the facts that the morals is Basically turning into spider-man himself means there's going to be two spider-men and it's something that I could see Marvel doing with the success of this game and with the Avengers game in the works and other Projects in the works right now for marvel when it comes to video games I really hope they make a second marvel's spider-man video game But it definitely is a lot of fun and I would recommend it and it's an easy platinum to get also if you're into getting platinums The next one is fire cry 5 which is the first fire cry video game that I've ever played It's a lot of fun. It is in the united states It is in the country area. I would say I think it's colorado if i'm not mistaken But there's a lot of wildlife. There's a lot of hunting and fishing you can do to earn extra money There's a lot of missions in the game the main story mode. The map is huge. There's a lot to do The one thing I wish it had was car customization But I can look past that because the rest of the game was phenomenal The guns the fact that you can customize the guns Somewhat and have amazing guns and the shooting mechanics of the game were pretty epic So everything about the game mechanics and that I did love the story was amazing It followed a cult where you're basically trying to take out the cult's leader And it was also a very fun game. It definitely would recommend it if you're also into getting platinums yet again Um, it is an easy platinum to get you do have to grind out on a couple parts But once you get past the hard trophies, it's something that you can fly by and do I just wish some of the trophies for the story mode You can play in co-op and have somebody try to help you out and do it All of these story mode trophies you have to play the story mode in solo by yourself And that can become difficult at times But it's definitely worth it if you want a platinum and also after that Far Cry new dawn came out, which I also platinumed and it is a fun game But i'm not going to go into depths of far cry new dawn Next up is a sports game and I know not everybody is into sports game But it is ufc 3 Comparing it to ufc 2 which I believe came out on the ps3 originally It is a lot of fun It is far more advanced and you can do more in it than you could in the previous ufc games I really hope they make another one that hopefully will come out later on the playstation 5 whenever it does get released in the holidays 2020 But the fact that you can run and do your moves and you can do basically an actual superman punch You can run and just jump and dodge at your opponents I thought was a lot of fun. These submissions are hard for me to get used to But it is a game that's even though it is now a couple years old I literally still go back and play. It is a lot of fun to do. I love playing it online Playing the career mode. I might actually go back and restart the career mode because there's a couple trophies I missed but it's definitely something that is fun and I honestly just love fighting games like that That's why I love playing the wwe but ufc Ufc 3 was a lot more fun Compared to ufc 2 there's a lot more to do a lot more moves added in the game But they ended up taking out a couple fighters that I wish were in the game But anyways, it is a really fun game next up is fallout 4 The first fallout game that I played was fallout 3 and I honestly loved it so much Whenever fallout 4 was announced on the playstation 4 I really wanted it right away and it ended up getting it I think a year after it came out because it was on sale for only 20 dollars And it was the best 20 dollars that I spent on the ps4 for a video game basically You're going through the wasteland like usual trying to find your uh father No, you're trying to find your son who I don't know the story is crazy I never got to finish the game because there's so much to do the map is incredibly huge So many locations to go and explore and find so much to do And I really like that you can go and actually build your settlement That's something that I thought was so fascinating about the game that you can go and Depending on what you scrap at different locations. You can actually go and take that supplies the wood the metal Everything and you can build up your own houses and bring your own people and build up your settlements And I thought that was really creative. I had a lot of fun with it But I literally tried a couple months ago going back and playing it The game I can't remember exactly when it came out But the gameplay the mechanics of the game are really not like current games now And I know fallout 76 came out not too long ago, but that's an online based game So I really hope they do make another fallout video game Hopefully on the ps5 yet again, but I really hope if they do They don't have just online. I hope they have basically like all the fallout games in the past Except they have a co-op option. I think that would be epic if they were to do that Next up is battlefield 5. I'm a huge battlefield fan ever since battlefield 1 I remember I remember playing battlefield back on the playstation 2 days But I still always played call of duty back whenever battlefield 1 was coming out That's whenever I was just getting out of call of duty because call of duty was going more into a futuristic direction Which I hate futuristic shooter games. So I got out of call of duty. I might not go back. Probably won't but battlefield I'm going to stick with it moving forward battlefield 1 followed the world war one, which I'm a huge War buff. I love war history and they actually put a lot of work into it Battlefield 5 follows world war 2, which I thought was a lot of fun. I really enjoyed it It also includes war stories. So instead of like call of duties campaign mode for world war 2 where it is Basically an original story that they created within the time period of world war 2 and everything that happened during world war 2 battlefield takes the approach of taking real life war stories of actual people that were in the war veterans And bringing those war stories to life in a video game. So their actual war stories, which I thought was awesome The maps are big huge maps for battlefield online. I love playing conquest, which is a combination of team deathmatch and capture the flag, but there's more flags alpha bravo charlie foxtrot golf and hotel, I believe instead of just Alpha bravo charlie, which call of duty has their 30 minutes matches I think 30 people like 30 players versus 30 players you have almost a thousand soldiers each team so if you're if your team basically gets to Zero soldiers the other team wins and by capturing the more locations that means you have more spawn points So I think that's just for me more of a strategic war game And that's why I love battlefield, but I definitely enjoyed playing battlefield 5 It's really far advanced and I can't wait for the next battlefield game. However I think they're gonna go to a modern battlefield game next which i'm really excited if they do Because yes, I love the war history But I want to see a them take an approach of a modern game once again modern warfare Instead of call of duty, which is more futuristic and I don't like call of duty anymore And the last game that I want to mention for my favorite playstation 4 video games Is need for speed 2015 This is my favorite need for speed video game because I ended up getting a platinum fort But besides that there was a lot more Fun stuff to do in this game as to other need for speed games There was drifting which I was a huge fan of drifting when they brought it back in need for speed 2015 I was extremely thankful and happy They brought back drag racing in an update which I thought was fascinating And the fact that you could start off the game with one vehicle and end up finishing the game with that same vehicle If you so wish by doing upgrades and purchasing the upgrades yourself I thought was epic When need for speed payback came out which came after need for speed 2015 You had to purchase different vehicles for different classes, which I absolutely hated because I had for need for speed 2015 the first vehicle I had which was a Ford Mustang Fox body 1990 And I used it throughout the whole game for every style of race drag racing drifting and regular races But to do that in need for speed payback. You had to purchase three separate vehicles for each class So it was a lot harder to do you could not go and use one vehicle for everything And you could not start off with one vehicle and end the game with one vehicle because you had to upgrade it In certain vehicles you start off with you can only upgrade so much to the vehicle level And for the later games or the later races you need a higher level vehicle to be able to complete so Need for speed 2015 not only did you end up getting a platinum if you work towards it Which I did and it was something fun to do But it was a More of a fun racing game than need for speed payback and I actually prefer this over payback I know heat just came out, but at the time I'm recording this video I do not own need for speed heats so I cannot honestly put this into this video Because I don't know how it is yet and how it will compare but need for speed payback But need for speed 2015 was an awesome game It had a lot more customization options than need for speed payback It had a lot more to do in the game and it was a lot of fun I think I ended up making the wish list for need for speed heats And I explained what I really loved for need for speed 2015 and what I really wanted to see that was in 2015 That I wanted to see in heats and I think I also in that video mentioned what I seen in need for speed payback That I don't want need for speed heat. So if you guys want check out my need for speed heat wish list video I know need speed is already out But that will explain what I liked about need for speed 2015 and what I didn't like about need for speed payback A little bit more. So I hope you guys have enjoyed this video Comment down below your favorite playstation 4 video games, and I'm going to leave this video here Please take care peace


Recall the flow control, the purpose of these protocols is to make sure that we don't send too fast for the receiver. Both stop and wait and sliding window, they both have the same aim. Sender has to send data, but the receiver in some cases may be too slow to process that data. So these protocols, so the rules that they follow, which is called a protocol, defines or controls how fast the sender can send. With stop and wait, the sender can send one frame before it must stop and wait for an act to come back. And the act only comes back when the receiver is ready to receive more. So that's how the receiver controls how fast the sender sends by sending back acknowledgments. But in some cases stop and wait is very inefficient because we send data, then we wait a long time for the act to come back, maybe there's a long propagation delay, and all that time spent waiting is time spent not using our link, not sending data across our link. So stop and wait can be inefficient, especially when the propagation delay is long. Sliding window tries to improve upon that by still having flow control. So it still means that the receiver will send back acts when it's ready to receive more. So there's still control of the sending rate by the receiver, but in this case the source is allowed to send, we call a window size of frames, more than one frame at a time. Then multiple frames before it has to wait for an act. The difference in terms of flow control with sliding window, the receiver must be able to store in its buffer multiple frames. With stop and wait, the receiver needs a buffer for one frame. It receives a frame, puts it into memory, into a buffer, and then processes. When it's finished it sends back an act, that's in stop and wait. With sliding window, the source sends multiple frames, they'll be received, put in memory at the receiver, maybe 10 frames, and they are processed one at a time, and then acts may come back. So with sliding window, the idea is to be more efficient than stop and wait, but the trade-off is that you need more memory space at the receiver. So sliding window requires the receiver to have more memory to store more frames at a time. The other problem with sliding window, and you've noticed, is it's complex, that is the protocol, the rules that you need to follow, is much more complex to understand and also to implement compared to stop and wait. So let's recap on what we know about sliding window and then we'll analyze the performance and see how it does compare with stop and wait in efficiency. We have sequence numbers to keep track of frames. Every frame has a number. We wrap around when we get to the maximum number, because we have a limited number of bits. So we come back to zero. That's one concept. And both the sender and the receiver keep track of what they've sent, what they've received, and what's been act. For example, the sender keeps track of the frames which it has received an acknowledgement for. I've sent some data frames. I've received acts back. Think of those frames, those data frames as done, are finished. If I send data frame one and I get an act back, then I don't have to worry about that data frame one again. That's been processed. So that's the last frame act. Last frame transmitted from the sender's perspective, I've sent some frames to the receiver, but I haven't yet received an act back. Sometimes I'll say they're outstanding. We're still waiting. So at some point in time, some pieces of data are finished. Some have been sent, but not yet act. And other pieces of data we can potentially send in the future. And the window size indicates at this point in time, how many more data frames am I allowed to send? So on the slide, we visualize that as to the left of this vertical bar are frames which are finished, done, they've been acknowledged. To the right of the vertical bar and before the window are frames which have been sent, but not yet act. I've sent frame six, I've sent frame seven, but I'm waiting for the act to come back. And in the blue rectangle here, this is the window. This says, I haven't sent these frames yet, but I'm allowed to send another five. There's a right five in this case. Frames beyond that, I'm not allowed to send at this point in time. All these frames out here, that's what it tries to visualize. And these parameters in an implementation, they change over time. When I receive an act, say I receive an act for frame six, then you can think this vertical bar moves in that six becomes a frame that has been acknowledged. It's no longer outstanding. We're no longer waiting for the act for six. So you can think this bar moves to here when I receive an act. And also, when I receive an act, I'm allowed to send one more frame. So the blue box extends to the right because the total number of frames which I'm allowed to send, that is those that have been sent and not yet act, plus those that I'm allowed to send. So between the vertical bar and the end of the blue box, this is always in this example seven, two plus five, seven. And over time, as this bar moves and this blue box extends, then it's always seven. That's our maximum window size. And that's a parameter of the protocol. In this example, it's seven. As I transmit frames, if I transmit frame number zero, you can think, okay, zero becomes into this set of frames transmitted, six, seven, and zero. And the blue box gets smaller on the left-hand side. I'm allowed to send another four so that this space is always seven frames. And the blue rectangle is our window, and that's where the name comes from. As we transmit frames, the window closes on the left. As we receive acts, the window opens on the right. And over time, you can imagine that window slides along. It's similar with the receiver, except the receiver, of course, keeps track of what it has received, six and seven, for example, it has received. What it has buffer space available to receive in the future, we have a maximum buffer size of seven frames. I've received and stored two frames in the buffer. I can store another five, and those frames which the receiver has received end act. Similar concepts between send and receiver. Any questions on sliding window before we calculate the performance? It's hard to remember all those steps, but try, for example, I think one quiz question asks about these numbers. So try and understand how those numbers are updated as packets are transmitted and acts are received. We went through an example last week based on this one. What we want to do now is say, okay, let's consider the best case scenario when we use sliding window and compare the performance of using sliding window to, say, stop and wait and see how they compare. In this example, I come up with the example we use as a case where we send three frames and then a little bit later we send four frames. Let's consider a different example and let's assume the sender has an unlimited supply of frames to send. The sender has an infinite number of frames to send. There's no need to wait for more frames. It always has something to send. And for simplicity, let's assume the destination, whenever it receives a frame, a data frame, it sends back an act immediately. The processing is so fast that I receive a data frame, process it in zero time and send back an act. That's the best case in terms of performance. Always sending data according to the rules and process as fast as possible. We'll do that in a moment. This example was different where we sometimes have data to send, like three frames, but then we had nothing to send for some time. This example also considered the case where we sent one act, this receive ready, one act to acknowledge three frames. That's also possible. But we're going to consider the simpler case, one act for every data frame received. We want to consider that case and calculate the efficiency. If you flick forward a few slides, you'll see the example that I have rather than try and draw it again. This one. We'll scroll down in a moment, but make sure you have this one. So I think at the end of your handouts, there's two pictures. Go to the end of your handouts. And this is the first picture. There's one on the back page which is slightly different. We'll go through both. It doesn't come out perfectly in black and white, but I think you'll see the patterns in a moment. The green color may not come out when printed. Let's explain the scenario, and then we'll go through the timing diagram and see what happens and then calculate the efficiency. I've set up some numbers just to make it a little bit easier to add them up rather than using, rather than calculating all these values in advance, I said, the propagation delay is 200 time units, 200 milliseconds, 200 seconds. I don't care in this example what the actual units are, but 200 time units is the propagation delay. So to get a message from A to B takes 200 units. Maybe an example will say milliseconds. The data transmission time, which is the time it to transmit the entire data frame is 100 time units in this example. And I'll say that that data frame is made up as original data plus header. And in this case, the header is 10% of the entire frame size. So if it takes 100 time units to transmit the entire frame, if the header is 10% of that, then we can think it takes 10 time units to transmit the header and 90 to transmit the data. We'll use that later, though, that split of header and data. The act transmission is just 10 time units. And in this first example, we'll use a two-bit sequence number. So the sequence numbers go from zero in decimal, zero through to three, and then wrap back around to zero through to three and so on. And that's what we'll draw here. The maximum window size is three. W is three in this. And in this diagram, as we draw what happens at the source A and the destination B, we'll keep track of the window at A. It'll be similar at B. But just for simplicity, we'll just look at A. And we'll look at if A's always got data to send, what efficiency can we achieve in this scenario? So and I've put the numbers on here. So the first thing, if we look at A, I've numbered, okay, so the data frames it has to send. It keeps going, but we start at zero, one, two, three, wrap around to zero and so on. Initially, with a window size of three, if we've sent nothing, we're allowed to send three frames. And we send them in order. So I've highlighted in green those frames which were allowed to transmit, zero, one and two. So at the start, A is allowed to send zero, one and two. We cannot send three just yet, because the window is just three frames. It's allowed to send zero, one and two, so let's do that and send them in order, of course. We transmit frame zero, starting at time zero. Transmission time is 100 time units, so we'd finish at time 100. After transmitting frame zero, we'd mark frame zero as one that's been transmitted, but not yet act, and that's what I've marked in red here. One and two are allowed to be transmitted. Zero has been sent, but we haven't yet got an act back for zero. So I'm just visualizing similar to what we've done in the previous examples. Any questions so far? Sliding window. Now's the time before we get into the harder parts. This is here, but not so good. This picture is in the last slide of your handouts, the second to last page. Go right to the end. There are two, one with two bits and one with three bit sequence number. We'll do two bits first. So we transmit frame zero. It's going to propagate across the B eventually, we'll see a little bit later. But after transmitting frame zero, since we're allowed to send two more, let's transmit frame one. And after transmitting frame one, we're allowed to send one more. So we transmit frame two. And after transmitting frame two, from A's perspective, there are three frames which we've sent, but not yet act. Our window is, what's our window size here? Zero. The window starts as three frames, two, one, zero. So the window is closed, meaning we're not allowed to send any more. After sending two, frame two. Three frames have been sent, no act yet. And that will finish at time 300, three by 100, which is our data transmission time. Frame zero slowly propagates across the B. If it finished transmitting at time 100 and the propagation delay is 200, then frame zero arrives at B at time 300. So that's when it arrives. And in this case, B immediately processes. So we're not showing any processing delay. It's immediate, receives the data done, send back an act. So this small rectangle represents the acknowledgement saying, thank you, please send me more. And the act takes 10 time units to transmit. So this act of frame zero, as that propagates back, it's going to arrive at time 510. Just for that first frame, just confirm that calculation of 100 for data transmission, plus 200 propagation, plus 10 for act transmission, plus 200 propagation back, 510 in total for that one frame transfer. Any questions? In fact, that's for frame zero, that's the same as how we'd calculate for stop and wait. Data transmission, propagation, act transmission, propagation. We've set the processing to zero. In the meantime, what's also happening is that frame one is propagating across. It finished transmission at time 200. It would arrive at time 400 and be processed and an act for that frame would come back. In this example, we send an act for every data frame we get. And the frame two will arrive here at, what, I think, time 500 plus 10 to transmit the act back. And eventually we'll see later the act will come back. So let's look at A again as the act comes back. What's happened? We transmit three frames and now we're waiting for the act to come back. The act arrives back at time 510. Actually, we'll stay up so we can see the whole scenario. So at time 510, the act comes back. Before, from A's perspective, there were three frames outstanding, transmitted, not yet act. When we receive the act, what will the act number be in this frame? It's not shown here. The act number. This one comes back. Inside an acknowledgment, we include a number in a similar way. In a data frame, we include the sequence number. The number we include inside the acknowledgment, I call the act number, the acknowledgment number. What's the value? I hear zero, two, they're both wrong. Wrong. One. Why'd we skip? We remember the act number indicates the next frame expected. So I receive from B's perspective, it receives frame zero. Thank you for frame zero. I now expect frame one. That's what the act number indicates. So the act number of the first act will be one. Of course, B sometime later receives frame one and sends an act. That act will have an act number of two, saying I've received one, now send me two. And when we receive frame two, we'll send back an act number saying please send me three, frame number three. It's the next number expected. Coming back, frame zero sent, act with act number one comes back. Question, the act number indicates the next number that the receiver expects to receive. So at time 300, B has only received one frame. That frame has sequence number zero, so the next one in order is one. Therefore I send back act with act number one. Of course, a little bit later I receive frame number one, therefore the next act will be two and so on. So at time 510, frame zero has been acknowledged. How do we know that? A has sent zero, one and two. The act number comes back one, which means to A, okay, I've sent zero, one and two. If B is expecting frame number one, that means B must receive frame number zero. So upon reception of this act, A knows frame zero has been successful. So I've marked this in this grey box. Zero becomes one that's been act already. Zero's done. It's been successfully transferred. One and two are still outstanding. Our window, because zero's done, allows us to send one more frame. Remember the total number of frames that can be outstanding plus we're allowed to send is three in this case. That is the red plus green is always three frames. You see that here? So therefore we receive an act for frame zero. Two are outstanding. We're allowed to send frame three. So let's do that immediately. Transmit frame three. And at that point, one, two and three become outstanding. Because we've sent one, two and three. It takes one hundred to transmit. And then, you can't quite see here, but at time 610, the second act comes back. Transmitted at time, or finished transmitting at time 410. 200 to propagate at time 610, the second act comes back. Acknowledging the second frame. Frame with sequence number one. So we'll see that, okay, frame one's done. This second act acknowledges frame with sequence number one. Frame one is finished. I've got two outstanding, frames two and three. Let's send another one. We're allowed to send frame zero now. And in this example, that act comes back at 610. And in fact, we finished transmitting frame three at exactly 610. So instantaneously, we can start transmitting the next frame, which is zero. We've wrapped around. Send frame zero. Okay, two, three and zero are outstanding. We're not allowed to send any more yet. But the third act comes back at time 710. Meaning zero, one and two are finished. Three and zero are outstanding. We're allowed to send one more. So let's transmit frame number one. We transmit that. And now we have three, zero and one outstanding. Our window is closed again. We're not allowed to send any more. And you can follow the timing. Okay, if we transmit frame three, finish at 610. The data arrives at 810 plus 200, plus 10 for the act, plus another 200 to come back. And that acknowledgement for frame with sequence number three will get back at time 1020. Receiving an act allows us to send one more. We send frame two. When we finish that, we'd receive the second act. You can see the dashed line here, allowing us to send another one, and so on. And that's about as far as I draw. It would keep going, two, three, zero. And then stop for a while. And then one, two, three. And then stop, and so on. You see in general what's happening is with a window of three in this scenario, A can send three frames, and then it must wait for the act for the first one. Send zero, one and two. And then when the act for the first one comes back, I can send more. And the timing works out that when this one is finished, I get the act for the second frame. Then the third, allowing A to send a batch of three again, three more frames, and then wait for the first act of those three frames to come back, and then send another three frames. And it will keep going like that as long as we have data frames to send. Any questions about why that happens? Some of those numbers, how they were calculated. It's in fact, sometimes people find this easier to understand than what we covered in the previous lecture of what happens when we have pauses and processing time. This is considering the best case. No processing or zero processing. Always send an act back. Always have data to send. And we see the general pattern is that, okay, from zero to 510, we got to send three frames. And then from 510 through to 1020, we get to send another three frames. And if you keep going, you'll see every 510 time units, we send three frames. And from that, you can calculate your efficiency and or throughput. Three data frames per 510 time units. How much data? This is a slightly different step. Here we don't know how many bits. I just gave you the transmission time. So we can't calculate throughput, but we can calculate efficiency. And you see it at the bottom of the picture. A sends... So in this case, A sends three frames of data to B, then waits for the act for the first frame. So that's the pattern. And then it sends another three. So over a long period of time, A is delivering three frames of data to B every 510 time units. If you keep drawing that picture, that's what you'll see. 510, 1020, and then another 510 and so on. In our setup, we said, or I said, each data frame consisted of 90% of real data and 10% of header. Just in this example. So the question is, what percentage of the time do we spend sending real data? And it's calculated there as efficiency, but sometimes you can think about it as, okay, in that period of... from zero to 510. That's the total time. What fraction of time did I spend sending real data? Well, I spent 300 time units sending data, but only 90% of that was real data. 10% was overhead. So what's 90% of 300? 270. So in that period from zero to 510, 300 time units spent sending frames, 270 time units spent sending the real data. So we can say, every 510 time units, I spend 270 time units sending real data. And you can look at it the same from B, every 510 time units, it spends 270 time units receiving real data. Because in this case, everything that is transmitted is received. There's no loss. And that gives us our efficiency, 52.94%, which is just 270 divided by 510, but expressed as a percentage. This is slightly different from what we've seen in the previous example. Here we don't know about throughput because we don't know about the actual data size. We just know about a ratio of the timing. Questions? Before we move on. Should be able to calculate such efficiency given a particular scenario. How do we get higher efficiency? More window. Good. I should have said how do we get higher efficiency given the same conditions here? That is, we can't change the propagation delay. Let's say we can't change the data size. Well, to get higher efficiency, spend more time transmitting. And you see the problem in this case is that transmit for 300 time units, then wait for 210. Transmit for 300, wait for 210. That time spent waiting is wasteful, and that leads to a lower efficiency because we're not sending anything. So how do we get to send more if our window was larger? We could send not three frames, maybe four or five or six, a larger window. And the next example that you have in front of you shows that. Same setup, all the same conditions except it's now a three bit sequence number, zero through to seven, and we set the maximum window size to seven. You may see some pattern between the sequence number and the window size. And when we come to back to the slides and back to error control, we'll see some more formal relationship but generally it's the case that the window is based upon the sequence or the length of the sequence number, the maximum window. If we had a window of eight, that is a three bit sequence number and a window of eight, it wouldn't work. There would be problems and we'll see those problems later in error control. If we have a three bit sequence number and a window of six, it works okay. But typically we set the window size to be one less than, well, one less than the number of sequence number. So in this case we have eight possible values. We set the window size to seven. Everything else is the same. You see here A can send seven frames at the start, zero through to six, follow through what happens. I recall there's a mistake somewhere because these blue numbers, we'll see who identifies it somewhere. Unless I fixed it last year. Transmit zero, one, two, three, four, five, six because we're allowed to send six, sorry, seven frames, zero through to six. And you see that from A's perspective the summation of the red blocks plus the green blocks is always seven. Five have been sent here, zero through to four, two are allowed to be sent at this point in time. When does the first act come back? Same time as in the previous example. Frame zero finishes transmitting at 100 plus 200 to propagate, 10 for the act, plus 200 to propagate back. At this time, 510, the first act comes back. So let's look what happens there. Let's say we had just started transmitting frame five. This is at time 500. So zero, one, two, three, four, and five have been transmitted. Six is allowed. But then we receive the act at time 510. Frame zero is done. That's finished. When we receive an act, we're allowed to send one more. So zero is done. We're now allowed to send both six and seven. So that's at this point in time. Think at time 500, we transmit or start transmitting frame five. So five is in the red box here. But then a little bit later, at time 510, we receive an act, meaning frame zero is done, the first frame, allowing us to send one more. So we're allowed to send six and seven. We transmit six, and at time what, 610, the second act would come back. 300, 400, 410 plus 200 at 610, the second act comes back. And you see at time 600, zero, one, two, three, four, and five, and six have been transmitted. Allowed to send seven. A little bit later, we receive the act and allowed to send one more. And it keeps going like that. Transmit one, but then a little bit later, receive the act for one of the earlier ones, allowing us to send one more. The window never closes. You see the green blocks, it never gets down to zero. The result, A is always transmitting data frames, never waiting for an act. And that's a good thing. That's the best efficiency we can achieve. Where's the error in that picture? At 700, good. This should be 800 here. The time in which we finish transmitting frame number seven, it's the eighth frame. It takes 800 time units to transmit, so this is a mistake. This 700 should be 800. Change it on your slide, on your picture. It's at the same location, but just change 700 to 800. So just by changing the window size, making it larger, we've improved our efficiency in that we're always sending. No time waiting. What's the efficiency? Well, again, what fraction of time do we spend sending real data? We're always transmitting data frames. 90% of the time is spent sending real data, 10% spent sending overhead. We get 90% efficiency in this case. That's just due to the split between the real data and the header. The only overhead is the header. There's no waiting. Questions on sliding window. How to calculate efficiency under different conditions. This one confuses people a little bit, the efficiency here. We don't look at some total time. You could, but you see that we're always sending. Every 100 time units that we transmit, 90% of that time is sending real data. We do that continuously, meaning we're 90% efficient. The only way to improve is to do what? How do I make this efficiency go higher? Anyone? I want more than 90%. I'm not satisfied. What if we increase the window size? What happens? There's no data rate in this case. I haven't specified the data rate, but we could change it. A larger window will not help in this case. If instead of 7, we could send 15 or a thousand frames, we cannot do any better because how do we do better than always sending? Even if we have a window size of 15, we've always got something to send. We never spend any time waiting, which gives us the same scenario. We always have to wait for the previous frame to be transmitted. A larger window doesn't help in this case. So if we don't increase the window, how can we increase the efficiency? What can we change? Propagation delay won't change anything, unless if I increase the propagation delay, the worst I can do is make the efficiency go down. It won't go up. Data size on the right track compared to what? Why is it 90% efficient? Well, if we're always transmitting of the 100 time units transmitting I said at the start, 90% was data, 10% header. The only way to increase the efficiency in this case is to make the header smaller relative to the data. Maybe it was 95.5, then it would be 95% efficient. 99.1, 99% efficient. So yes, increase the data relative to the header. That's the only way. Now in practice, we usually have a fixed size header. We cannot change that. The only thing we can do is make the data larger, the real data. So in this case, it depends upon the ratio between data and header. Any questions on sliding window? Almost finished it. What about, so in this scenario, we got a efficiency of 90% when we had a window of 7. With a window of 3 frames, we got an efficiency of about 53%. What about a window of one frame? W equals 1. Efficiency go up or down? If the window is 1, the efficiency goes down. When the window is 1, what do we get? Look at this picture. Here the window is 3. With a window of 1, we'd send frame 1 and then wait for the actor to come back. Then send the next and wait for the actor to come back. When the window is 1, we get stop and wait. Stop and wait is just a special case of sliding window when the window size is 1. Window size of 1 means you're allowed to send one frame before you have to wait for an act. And that will give us a lower efficiency. You could calculate 90 time units transmitting divided by 510. 15% or something. So under a, usually with a specific link, when we know the propagation delay, we know the data rate. For a given data size and header size, we can find the optimal window size. The optimal window size will be large enough such that we're always transmitting frames. In this case, 3 is not optimal. It's suboptimal because we spend some time waiting. What do you think the optimal window size in this case is? In our scenario. Do you think 4 would be okay? 4 would not be okay because the fourth frame would finish at time 400 and then we'd have to wait till time 510 before the act comes back. So we'd spend 110 time units waiting. So 4 would not be good. It'd be better than 3 but not as good as 7. 5 would finish transmitting the fifth frame at 510 and wait just for 10 time units before the act comes back. Almost optimal. But still a little bit of time waiting with 5 frames. So 6 would be optimal window size in this case because 6 will allow us to transmit frames and the first act comes back when we're transmitting that window of frames. If you drew it with 6 it would be 3, 4, 5 while we're transmitting that sixth frame the first act comes back allowing us to keep going continuously. In practice we often set the window size the maximum window size to be based upon the sequence number. Why not just set the window size to be 1000 to be very big? What's the problem? Why not set the window size to 1000? We see with 7 it gives us the best efficiency with 1000 we'd get the same efficiency but what's the problem with the large window? Sorry? Check? Check what? This protocol would be the same if we have a large window. If the window is 1000 what would the problem be? It's not shown here but remember we're doing flow control the window indicates how many frames the receiver must be able to buffer so a window of 1000 means the receiver must have memory to store 1000 frames a window of 7 it means it just needs memory to store 7 frames so it uses less memory at the receiver with a smaller window we'd like to really in practice it depends on how much memory the receiver has but having a window of 1000 doesn't increase the efficiency and increases the memory requirements of the receiver so that's the problem the window indicates how much memory is needed at the receiver the more memory needed the worst that case is you go calculate this example tonight this example is the same as the one you have in front of you the answer for this is the exact numbers in the pictures that we just went through on those two slides 90% payload what does payload mean? payload means the original data 90% real data 10% header and you do the calculations you get the exact numbers as the pictures that we drew so in fact you've got the answer to that example already ask now if the window size decreases the efficiency decreases if the window size increases what happens? what happens? there's some upper limit in this case if the window size was 1 it would be very low efficiency we didn't calculate it but a low window size of 1 the efficiency would be quite low it's just stop and wait a window size of 3 we calculated to be 53% a window size of 7 we got up to 90% so yes increasing the window size increases the efficiency but up to some point we got to a window size of 7 the efficiency would still be 90% so the efficiency at some point we reach the maximum so it goes up and then it hits the peak so under certain conditions there's an optimal window size one that is large enough such that we get the highest efficiency the peak efficiency so if the window size increases up to that maximum the problem of going further for the window size 15, 30, a thousand efficiency stays the same but the memory requirements increase so that's the problem of keep increasing the window size we need more memory at the receiver yes if the window is 5 in this case you would give the sub-optimal correct? it's not quite 88% is being calculated if the window is 5 if the window is 5 we could transmit 0, 1, 2, 3, 4 that is 5 frames we'd finish transmitting the 5th one at time 500 5 times 100 finish at time 500 spend 10 time units waiting small amount to wait not quite the best we can achieve so 6 is the best case any questions at the back I'm just in this case this line indicates when we receive the ACK in its entirety at time 510 I've just got it now I'm allowed to send frame 3 so this rectangle shows us the time it takes to transmit 3 I start transmitting at time 3 sorry, at time 510 the frames coming out of my computer it takes 100 time units to transmit so I start transmitting at time 510 I finish transmitting at time 610 that's what it visualizes there so it frame 3 in full arrives at time 810 the last part any other questions on sliding window efficiency remember stop and wait is just sliding window when the window is 1 so in fact if you know sliding window you can forget about stop and wait just remember the window size of 1 any numbers that you can tell us about the efficiency what's the answer 500 times 90 divided by 510 right, so in this case efficiency is the fraction of time spent sending real data divided by total time 88% someone calculated for the frames so we know sliding window we know how to determine the efficiency and we also know the operation of how we keep track of the frame sent, received and act before we move on to the next topic any last questions on sliding window you'll calculate efficiency in the quiz you can try will you get it correct are there any problems easy good no, not okay what's not okay ask me and then others we'll hear your question and get the understanding 510, everyone can calculate where 510 comes from 510 510 is just the summation of focus just on the first frame 100 to transmit get your calculator 100 to transmit plus 200 to propagate so we've got 300 10 to transmit the act, 310 200 to propagate back the first act arrives in it's full in it's entirety at time 510 and the reception of the first act is the time when A can send more the only thing that allows A is receiving an act so that's why we care about this time because at that point we can send more and we can see if you follow it through we'll see the pattern every 510 we send a bunch of frames that bunch is based upon the window size I'm not sure if that answered the question anything else in the second example it's a bit harder to see the timing I didn't draw them all in this case for example this is 800 not 700 that was an error I think if you calculate those times yourself you see that we finish transmitting each frame at the 100 interval at 700, 800, 900 and the act arrives 10 units later because there's a 10 for transmission of the act so you can work out the exact times there but it's always the case in this one in that the act the first act arrives while we're still transmitting that first window of frames the window is 7 it takes 700 transmit the first act arrives before 700 allowing us to just keep transmitting because the act arriving means we're allowed to send one more and you see the green boxes are always greater than one or two in this case it's never closed but the exact timing maybe go through and determine those times of course if we have a different propagation delay different numbers the window size we'd have to find the optimum one based upon those conditions back to our slides let's keep going few other things that we maybe haven't mentioned some we may have not so important at this stage in some cases you can have a receive not ready frame receive ready I've called an act a receive not ready frame is an act that says thanks for the data you've sent me but you're not allowed to send anymore send anymore yet an act says thanks for the data you sent me you can send me more a receive not ready is a special case you can indicate yes I've received your data but you're not I'm not ready to receive more yet this becomes useful when we in practice when you look at real protocols we can reduce them overhead by using these type of frames but I think we will not see any examples of that piggybacking everything up until now we've looked at one direction A sending data to B but of course in real life A may want to send data to B and B wants to send data back to A then in that case we just do the sliding window independent in both directions so if A sending data to B it maintains its own parameters for its window for the data coming from B to A it's completely separate there's no connection between the different directions but in practice if B is sending data to A instead of sending an AC from B to A and then sending data from B to A piggybacking is the concept of you've got two different frames going in the same direction join them together send one frame and include the information of both of them in one frame it cuts down on transmissions I think we will not cover that yet we may see it maybe towards the end of the course we will see some specific examples of protocols that use sliding window but if you ever come across piggybacking it means having combining a data and the AC into a single frame I think that's all to say about sliding window which leaves us with error control up until now everything's been perfect we send data it gets to the destination the next issue we send data and something goes wrong how do we deal with that and that's what error control is about let's get started on it turns out error control protocols will go through three basic mechanisms the first one is based upon stop and wait it's just an extension of stop and wait second to are extensions of sliding window so once you know stop and wait and sliding window, error control is much easier we'll just start on stop and wait error control we've got two things that may go wrong in terms of sending frames I send a frame from A to B but it doesn't get to B A transmits it something goes wrong with the link or the connection such that the frame never gets to B we call that a lost frame frame was sent but not received the other thing that can go wrong is I send a frame from A to B B receives it but there are some errors inside that frame the data is wrong maybe we'll use error detection to detect that we call that a damaged frame in the error control mechanisms we usually treat them the same if I receive a frame that's damaged I just discard it, throw it away as if I'd never received it that's the common approach that a damaged frame one that's received but has errors is usually just ignored by the receiver how do we deal with errors we'll go through mechanisms retransmit so we'll see these through examples and we'll go through three techniques of retransmissions automatic repeat request ARQ protocols stop and wait, ARQ go back and select and reject ARQ let's today just start on stop and wait it's based on stop and wait flow control source transmits a single data frame after transmitting it starts a timer so we have a stop watch we start it after transmitting that frame and we store a copy of that frame that we just sent if we receive the ARQ before the timer expires then that ARQ indicates the frame was successfully delivered and we move on to the next frame if no ARQ is received before the timer expires then we'll retransmit so the timer tells us when to try again, render, retransmit there are a few other cases but it's straight to two or three examples here's stop and wait ARQ unless there are any questions let's concentrate on this one so that you can do this just as easy as you can do flow control any last questions on sliding window? stop and wait ARQ it looks the same as stop and wait flow control send data data arrives at B B when it's ready for more sends back an ARQ A sends data arrives at B maybe B processes it for some time sends back an ARQ data, ARQ, data, ARQ nothing new there this is the case where there's no errors the normal case I've attached some numbers in here some sequence numbers we'll explain why they are needed later what happens if same scenario but this data frame, the second data frame doesn't get to B A transmits data one there's an error in the link such that B doesn't receive it draw what happens that is one lost data frames try and work out what will happen if we use stop and wait ARQ try and draw what happens if the second frame there's data with one in the brackets here it doesn't get to B so the first frame is normal and the second one is lost so the first data frame is successful the ARQ comes back this is our data this is our ARQ then we transmit the second data frame we say that data frame is lost that is something goes wrong in our link such that B doesn't receive it what happens next that's what we care about what's going to happen time when the timer expires where you're correct at A so what I haven't drawn here is that after A transmits the data it starts its timer think it's got a clock and the idea is that let's say we started at zero and it goes up it reaches some predefined value we say that timer expires sometimes we say it times out that predefined value would be set we don't know the value at this stage but once it reaches some maximum value the timer expires we get a time out that triggers a retransmission in this case B is not going to receive the data so B is of course never going to send an ARQ back we're never going to get an ARQ so what A does is starts its timer we can think that A starts the timer here and that timer is some value such that so we start here and at this time the timer expires that is we reach some predefined maximum and when the timer expires or sometimes say a time out occurs that triggers A to think something's gone wrong ah I've sent data I'm waiting for the ARQ I'm waiting, I'm waiting I've waited too long let's assume that the data didn't get there and let's try again so we retransmit and in these boxes I don't know if I did it on the slide in the slide I said data one data two, data three the first one of course was data one the next one was data two what's this box this is a retransmission of the data the second piece of data it's another frame of course but the data inside it is the same as the previous frame it's a retransmission why did we retransmit because we waited long enough for the ARQ but an ARQ didn't arrive let's say in this case the error doesn't occur so it arrives at B everything goes fine the ARQ comes back and from B's perspective at this time data one was received and processed and from B's perspective at this time data two is received and processed and of course let's say everything continues normally for any subsequent data so this is the case when there's just one error such that one frame doesn't get to the destination we time out and retransmit that previous frame any problems with stop and wait ARQ we've still got five minutes left on our timer so we can go through the next example next case consider that the data does arrive but the ARQ gets lost try that so we've just done lost data now consider a case with a lost ARQ data gets there but the ARQ doesn't come back let's say the first frame is successful the second frame is sent but the ARQ for the second frame doesn't get back same as before second frame OK arrives at B ARQ comes back arrives at A A transmits the next data frame arrives at B B sends the data back sorry B sends an ARQ back there's an error what happens next and I'll just note A sent data to B B received the first piece of data OK data one successful sends back the ARQ B receives the second piece of data data two successful but sends back the ARQ what happens next what does A do what happens at A does A receive the ARQ no so what A does it sits there forever waiting it will time out it will wait for a certain period of time and then say give up I waited too long for the ARQ I never received it same as before retransmit so we'll have a time out after some time I'll just write this time it's a time out and that triggers retransmission and let's say that arrives this time and let's go back I forgot our numbers this was the first piece of data this was the second what is this piece of data it's a retransmission of the second piece of data what happens at B what does B do in this case B receives the data I just received the data frame what do we do at B you're the receiver let's say what was the contents of the data let's say data frame 2 was sending money from some computer or from an ATM to the bank or some money exchange and the message was send Steve 10,000 baht that was message number 2 and computer B is the bank that processes it and transfers 10,000 baht into my account what happens at B in this point I receive another 10,000 baht that is data 2 the same data is received at B what does B do if B accepts and uses that data we have an error because the same data arrives at B and B receives it twice so that's wrong only one copy of that data should be used by B so we need some way for B to detect ah this frame is the same as the previous one I got let's ignore it that's what we need so we need B to say discard that ignore that frame because if it didn't ignore it it would process that same piece of data 2 times it can ignore and then send back an act indicating that data has been successfully received the problem is how do we know that this one is a copy of the previous one it's not just intended to be the same and the way to know whether to ignore it or not we need sequence numbers inside these data frames so that we can know this is the same as the previous one but we have timed out in the lecture so you'll go and study that and we'll continue with that and introduce sequence numbers into these frames so that we can detect this case it's a strange case that if we don't ignore this we'll get an error


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