Good evening. Good evening. Thanks for joining. I'm just updating the attendees list on the document. Hi. Hi, Karthik. Hi. Yeah. Yeah, let's actually put in brackets here. I'm going to just say it must space my data. I can rename. I'm just renaming. Okay. I can rename. I'm just renaming the zoom so that they. Thanks for taking time. It's an important call. It's okay. Hello everyone. Hi, guys. How are you? Hired today, honestly. So I'm afraid that not necessarily a lot of people are going to show up today. Yeah. Yes. But let's still give people a chance to, not if you wanted to present something. So you might not have the biggest of audiences, but anyways, we have to recording and we can share it later on. That's still something we can do. All right. I'm posting as always the link to the document here waiting a couple more minutes then for people to join. Taylor, I really like your profile picture. Hey, thanks. I feel watched a bit when I look at it, but okay, I just posted the note stock in here. I'm adding one more item in here. So obviously we have to operate a working group update today, which is greatly making progress here. Then litmus integration services. Captain. Again, it's not too many people are going to join today. We'll just record it and then post the link to the on the mailing list later on. The recording should be available to tomorrow. And then I briefly also wanted to give you, maybe we do this bit. Then also as one of the first items, a brief update on the potato hat project. Okay. So usually we give people five minutes to join in here, joining today. So I'll be back in two minutes. Okay. So I'll be back to give the update on the operator working group today. Yeah, I will do it. Thanks. So a short update from the operator working group. And so we're meeting. Once a week at a small, very small group. Three more people and myself. And on that meeting, we are talking about the operator trying to get it. Into something we can share. We are sharing the results week by week. So in the definition of an operator, you can see. Sorry. In the operator definition working document. I will edit in the links. You can see. Progress is being made. We are open to comments there. And if you want to join the small group. And I will also share how, or you can just ping me. The last time we talk about capabilities of an operator. And we are trying to define. What are the best phases in the life cycle of an operator and how an operator can say, I'm doing that or I'm not doing that. And so we finished defining that and we will add it into the document. And so keep on watching. And that's it till next time. Sorry, it's been a long day. Thanks. I just wanted to say a broader audience that just started. There is already like, just quickly sharing it here. This is like all the new operator definition. What could you have done already? You wanted to record this meeting. It's getting automatically recorded. All right. Okay. Thanks. I think is something we already discussed. The yellow is something we discussed, but it's not in final words. So we're having a week. And we will refine it. So we talked about it. This is the output of what we talked in the meeting. And now everyone of us took a small chunk and we'll write it in the better. And explain words. Yeah, I think that's great. So the next meeting will be canceled anyway, due to QCon, QCon, obviously. So if, if you would have maybe a bit of a longer update, not the next meeting, but like the actual next one, like in a month from now, that, that, that would be great. Yeah, I believe that also in a month, we will have something more concrete to show. So maybe we'll schedule a longer. Read of the. Yeah, I think that might be that that's going to be great. Yeah, I think that's great. And notice has been in there working for, for quite some while and then we kind of lost track of it. And now we are back on it. So I think that's great. And ideally we can then also like share it with a bigger audience later this year, but that looks very, very promising already. And yeah, thanks for taking this, taking on this work. And if you want to discuss specific topics, also feel free to use the mailing list. I found that there's way more people reading the mailing list than I did you to time zone issues and other reasons. Okay, looking at the agenda. Some people were just adding something in here. Since you have conformance and new CNCF working group who added this one. That was me Taylor. Okay. So I'm just looking at also having the demo and I assume you're here to really give the team here a small update. So I was wondering whether we should just do this or the demo, how much time would you need for, for that topic? Roughly. And it's really just more of a notice and request for people to be aware and join if possible. So just a couple of minutes. Yeah, then I would propose you go first and then we dive into the demo if the demo team is fine, because that gives you more or less time for the demo till the end of the meeting and being sure that Tyler gets some time to present on this one. Okay. So I would then pass over regarding CCF conformance and. All right. I can. I guess share my screen. If that's something that. Y'all like to be. Yeah. All right. So, and they, I don't, I don't know who's aware of. But it's been, it's been going for about a year as far as. The program start. But it's been focused on. A. Test suite project up until recently. So it's. Just. Recently started was a new working group. That's the new thing here. But to give you. A little bit of background. And by the way, this may be a repeat for some people if you're on the TOC meeting yesterday, because this was shown there. So I'm not going to go through all of it. You can go review that. These slides are from the TOC meetings. But the main thing here is. The CNF conformance is, is looking at. How to help telcos and being more cloud native with their applications. The telco industry. And we're looking at the service providers themselves. So AT&T, but if an orange whoever. And the creators of the telco applications. So vendors and everyone else. I'm just going to kind of go through. Very quickly. So. This is just an overview of saying what's been happening. And. And the telco is more and more interested. The, you're starting to see more adoption of Kubernetes. And on a platform side, it seems like everyone wants to do certified Kubernetes. And so. So I'm going to go through some of the recent surveys as. Talking about. Moving the actual. Networking and telco type workloads. Which they're usually referring to like network functions. Moving those applications on the Kubernetes. And so there's. There's issues with the, the telco industry. And I think it's a little bit different from the normal applications. So that the conformance goals as a program is similar to like Kubernetes conformance for interoperability at a. Platform level. Of course, that's really core Kubernetes. Then you look at additional specs. So what it's trying to do beyond that is. What are the things. That can help the telco industry to understand best practices. So there's two parts that the existing part is this. Project for this actual test suite. And that's, you can come check it out. There's existing code. And this is being developed for. A year now. Trying to build. Test and stuff working with. Different telco community. Communities and. Getting feedback. There's a lot of, a lot of different things. Going into this. But the, the new thing here is. This working group. Going around defining like a certification. And definitions. And. This is, you can see an open pull request. If you go to the CNF conformance where it's the. The charter and. All the other things are being added here for. This new working group and what's in scope. So I'm, I'm. I don't want to take up a lot of time in this call to go over it, but the, the main. Point here is there is going to be overlap and many. Communities. CNCF and Kubernetes. And one of the main ones that we've talked about is. And one of the things that we've talked about is SIG app delivery. And. Another one would, I'm just pointing out would be SIG network. Cause there's, there's an overlap on both of those. When you look at telco and the type of applications that are run. As you have a lot that deal with like user data planes. So not using the standard networking. So what do you do for those type of applications? What are the other applications that may go beyond what. The standard. Common applications are. So that's what the focus of this group right now is. To, to work with the telco and cloud native community to, to try to look at what is the scope. What are the things that we need to do to extend and, and look at best practices on how they can be utilized in the. In the telco domain. And I should really just say that the networking. Domain. This telco would be under that. Platform is likely to be part of it in some ways. When you talk about stuff like a user data plan. And you look at other mechanisms within the. The platform or you could say layers and add-ons of Kubernetes. And that's where it ties in with. The network plumbing group and. SIG networking from. Kubernetes and as well as the CNCF SIG networking, which looks at service meshes and stuff. So this is more of. This working group is forming right now. We're going to be having community meetings to talk more at. I will start having regular. Working group meetings. And we would, we expect to. Collaborate more directly with SIG app delivery than most. Most of them and probably SIG networking as well. I'm sure there's going to be a lot of overlap in different ones. But if, if. We'd love to have your participation in that group to help telcos. I think that's probably it. Yeah. The, the, the test suite. Itself maybe just to throw out is this, this is going to be put forward as a. A sandbox project in the next three to six months. The test suite. And maybe a quick overview of the test suite is. You could think of it as. A combination of what. You could think of it as a, you could think of it as a combination of what you are and, and Kubernetes when you think Kubernetes conformance, you have the. E to E tests, which are managed by SIG testing. And then you have sauna boy, which is what people usually use to make it easier. You don't have to use some boy, but many people do. So the. What's currently called the CNF conformance test suite may be renamed. It takes a lot of ideas and concepts of sauna boy. And, and then supports doing many different types of. Test that you can do. We support. Pretty much any layers. And I think that covers it, but happy to answer any questions. Yeah. Thanks for sharing. So I definitely think that there are reasons. I think that there are points of overlap here going forward. So I think having everything makes sense. Interestingly, the first time we had a touch point with. Regarding telcos was really on the air gap work that we kind of. Well, we didn't really stop it, but it didn't get as much momentum as we thought it would. I think it doesn't really fall into CNF category. So I think that there's something always started to look in as well, especially when it comes to. Telco provider appliances and how to actually deploy applications and as well, Kubernetes there. I got something I reached out to the telecom user group, but also look keen on how this plays on the, on the networking side. I don't see the immediate overlap, but I think it might be good to at least have like a quarterly analysis and see where, where things overlap if that's, if that's fine for you. Absolutely. And I'd say the, it's more of a compliment than anything else. And potentially it's going to be a specific on the application side when versus when we get down lower, that may be like a platform or the signal working. I would say it's a specific case. And if it wasn't for the, the platform items where it starts getting a little bit gray, this probably would be a working group under SIG app delivery. There's a little bit of discussion on, does it fit more on SIG network or SIG app delivery? And I see Matt Farina, I think you said in the TOC yesterday that it kind of bridges both or agree, agree on that. But he gets for a quarterly update and we'll continue to collaborate and think of SIG app delivery kind of as a base for a lot of the cloud native application pieces. I think obviously we can think more frequently, but I would at least put like a quarter of the update on the agenda to ensure that people are aware, but obviously if you have something that comes up earlier, you can obviously talk. Yeah, I imagine it'll be less common stuff. I think we'll start saying more of that. Okay, then next item on the agenda. I see you wanted 35 minutes, we won't make it like with a full 35 minutes, you might have to speed up your demo, but it's great to see updates from two projects and especially to CSF projects working together on the chaos engineering and delivery integration between captain and litmus. So I would ask you to maybe sort of we stay in time. Can you do it in 24 minutes because that would keep us in time? Yeah, I think that should be possible for us. I'm going ahead and just share my screen. Yeah, thanks Alice. And as you already mentioned, that was really a joint effort between the litmus and the captain team. And together with Kartik from the litmus team, we are going to present our joint effort here how to integrate these two projects and actually why we're doing this and which problems we want to solve. We have a little bit of a presentation prepared and then the demo Kartik. Maybe you just let me know when I should move on with the slides and I will do, I will let you start and then I will handle. Thanks. Hi everyone. Great to see Taylor Watson and Lucina here. We've met recently and should get on soon to contributing to the CNF conference this week. Okay, so we have some items on the agenda. There's a couple of slides we have explained what we're trying to do and then go to the actual demo and try to keep it short on the slides and move to the demo quicker. We could probably go to the next slide here again. Okay, so we are all aware that resiliency on microservices is hard and whenever we are applying applications on Kubernetes, we know that there's a lot of services running there which we have not built ourselves and 90% of our resilience depends upon all the infrastructure components that we are running on top of. So it's really difficult to predict what kind of outage can happen. So it's very important to have a system where we are going ahead and injecting these failures ourselves. So Cures Engineering has been compared to vaccinations. So that's something that we are injecting failures effectively and trying to find where the system is and that information can actually help us for application, business logic or our department practices. So I think that's really important. I think we can go to the next slide. So one of the things that has a project we looked at is Kubernetes with the onset of Kubernetes I think and a little before that the way people spoke to infrastructure change, infrastructure as code came in and then Kubernetes came in and Yamens are beginning the standard. So with Kubernetes all aspects of managing applications and managing resources and policies is all done in a declarative way via Yamens and we wanted to adopt the same UX when people are attempting residency tests or chaos on their clusters. So that's where the custom resources came in. So we were able to provide the same user experience describe the chaos intent in custom resources and have an operator understand that and implement the right functions. So if we go to the next slide and litmus we have each of these activities that you see there in the block diagram. So you can see a traditional flow of the chaos experiment. We have a hypothesis that is describing some steady state for the application of infrastructure which is validated first followed by a fault injection and there's going to be a steady state verification that's done at the end of it or even during the fault as the chaos occurs in parallel and if our hypothesis is right and if the steady state conditions are met or they are regained within the toleration seconds that were specified we're going to qualify that infrastructure or application as being resilient otherwise we see the waveform to be less and the way to describe this in litmus is via custom resources so there are some off the shelf custom resources static ones called chaos experiment CRs which is what is listed on the chaos hub and this will be pulled into a cluster and the chaos engine is user facing CR that maps a custom chaos experiment resource with an application instance that's described by the namespace and labels and other attributes. So when the chaos engine is created the chaos operator launches a set of chaos parts which actually implement the experiment the chaos injection logic is implemented by them and the results are captured in another CR so they are present CRs as each of these has a scope for a lot of information to be placed inside it and acted upon so this is a iterative way of doing chaos that was the seed for litmus and that's something that's been adopted and taken quite well and it lends itself to a lot of paradigms and cloud native space like GitOps which we will talk about in a short while when kept in is being discussed so this is about why we needed it and how we are doing it the next slide is talking about what are the ways in which projects like litmus are being used today initially chaos engineering was viewed very much as an interesting very much in the domain of the SRE but there is been left shift and people are using it as part of the release that is they are using it as part of the delivery pipelines so chaos experiments with the frameworks like litmus are allowing themselves to be used in this model so you could actually do a chaos experiment as part of a CI pipeline with different popular CI frameworks with LAN etc where you can actually store the experiment before hand and run them as part of the pipeline and use the research to determine the CI stage success or the CI job success and application artifacts the two parts of the stage can be placed into other clusters which are doing some kind of scheduled chaos which are typically for longer durations and then we can interface with CD mechanisms with CD solutions which can actually take this and put it into a staging namespace run its own set of validations and promote it to other stages depending upon the success so this is something that we are seeing in fact this is one of the dominant use cases of litmus in the recent times and we are seeing the community and has this part in the interesting integrations which we will talk about that's where I would like to hand it off to you again to talk about what these integrations are about Thank you oh it was a little animation but here we go, yeah thanks so before we will jump into the demo and show you how captain and litmus chaos work together I just want to give you a brief heads up on what captain is maybe you have already heard about it it's also a CNCF Sandbox project and it's really a control plane for cloud native delivery and operations and in this sense it can orchestrate the whole application life cycle for today we will really focus on as an old driven delivery that means that captain has as a central piece coordinates based on SRE principles like SLIs and SLOs it has this as a central piece already baked into the captain platform so we have this part and we can trigger different tools different integrations like litmus and then after they do their job captain can then take its next actions for example evaluating how actually these tools have been executed and how the quality of a microservice that should be delivered to production is actually affected same as in litmus also everything in captain is declarative and captain itself comes with its own github approach so everything that's stored inside captain managed by captain is also versioned and stored in the github approach and that is true also for everything that we are doing now with the litmus experiments so everything really works or it's moved into the github or git repository and can be linked to github git lab or whatever so how captain works in the sense how can you really connect other tools the two main definition files for captain are a shipyard file that describes your environment and a uniform file that the concept of a uniform that describes which tools you want to connect to captain and captain is this control plane then connects all the different tools together so for example captain starts by deploying a new version of an artifact your container image deploying this after the deployment captain will automatically trigger the testing tool you will define which testing tool you want to use we will be using in the demo two testing tools at the same time that are both connected to captain after the tests are finished captain will execute the evaluation of the tests but captain can also be linked or other tools can be linked and connected to captain like the chat box tool you can control captain by a chat bot or you can have everything that's going on inside captain sent as a notification to slag and of course you can use these tools for observability like for me for a dashboarding with profana you can link this to captain and when you're using captain captain can distribute the events from the control plane to these tools and then they can take action so with this a lot of automation is possible and a lot of automation is baked into captain for this use case today we will only focus on the deployment testing and evaluating we will take a look at the automated configuration of dashboards or promotion to production we will only take a look at one piece of captain let's say so what we have prepared for today is really how can we include chaos engineering into a pipeline and we start with deploying the potato hat application I just saw it's also an item for today's agenda of this SIG meeting so we will hear maybe more about this it's a small demo application that we are going to use for this demo we are going to deploy this into a chaos stage so it's a single stage environment in this case because we're just interested in evaluating the resiliency of our microservice we're using help for deployment and captain is executing the deployment with help for us after the deployment is finished captain will trigger chemeter tests but not only chemeter tests but actually at the same time also trigger litmus chaos so with this we can make sure that we have actual traffic on the service under test our potato hat and we are also triggering chaos on the service so we can then later on evaluate the resiliency once the tests are finished the captain quality gate is triggered and this will reach out automatically to Prometheus and gather some data that is defined for this quality gate and will gather this data for the exact testing timeframe and for the service under test in this case our potato hat application and then captain will go ahead and evaluate based on the quality criteria of the quality gate what we have defined in the SLO .yml file we will see this also in the demo captain will go ahead and evaluate this quality gate and will come up with a total score and this score can be either let's say a thumbs up that means that the resiliency is satisfying based on our service level objectives or if we cannot reach the score our resiliency is not satisfying and we can just re-run the whole workflow again for example usually what captain can also do is we can just promote it to the next stage from pre-production to production if everything is fine I will hand over again to Karthik to explain our demo environment and then we will see the demo the demo environment is very simple we have a GKE cluster on which we have the potato hat app with hello service that is running we have a hello page that is a decent point and it is configured with a readiness pro this demo we will basically look at the difference between single replica app and multiple replica one and see how the quality gate actually fails in the first case and goes through the second case so it is highlighting the deployment issue the flow is something like this so there is going to be a deployment that is going to be managed by captain and once deployment of the hello service is complete the readiness experiments are going to be triggered on that and in that process we also have a black box exporter and Prometheus instance running from it managed by captain and we have also a black box exporter that is hitting the probe success and duration metrics which we are specifically querying for and the Prometheus SLI provider of captain is going to that is received and it is going to provide that to captain which is going to carry out an SLO evaluation against this metrics we can see that when we do the demo the rules that we have set as part of this evaluation and if like you can say we are successful in meeting the criteria that is set against the metrics that we go ahead and pass the stage and then we are going to set up what it is so this is what we have in terms of the demo I think we can go ahead with that thank you sure okay so what I have here is the user interface of captain what we call the captain's bridge I will just open this it is actually from earlier today when we did our experiments but what we are going to do is send a new deployment event to captain and I will do this with the captain's CLI but with the option that I just send the cloud event to captain the reason I do this via the cloud event and not via the built-in capabilities of the captain's CLI is that we can take a look a little bit on the details of the cloud event so I will go ahead and deploy my potato identification micro service and I will just do this with one replica and I will instruct here captain to start the deployment and what I have already told you on the slides will happen so captain will start to actually deploy the service and we will see this in the captain's bridge the captain will deploy the service and what we can already see here is that the chaos runner and the pod delete will start it so after deployment it was really fast because actually it was already running in this exact version on my cluster so the deployment was finished and captain is triggering two different kind of tests the first one is the chemeter tests they are running in a different namespace so we are not seeing the pod here we are only seeing the pod in the litmus chaos namespace but what we can see is that captain also triggered the chaos tests we can already see it's killing the pod here so our chaos tests are the pod delete chaos experiment that means a pod that from our hello service replica set the random pod will be killed by the litmus chaos experiment and it takes a couple of seconds for the next pod to come up we have added the readiness probe with a little bit of delay so we can make sure that once the pod will receive some traffic it's already up and running and now after a couple of seconds it's up and running so let us now take a look on the captain user interface what we can see here so we just triggered the chaos and we can see the configuration change was received and the deployment is finished after the deployment is finished captain will start the tests and we have already seen the test execution of the the litmus chaos experiment but actually we will wait here for the full execution of the chemeter tests usually they take about two minutes for them to be finished so we should receive the event just in a second so we can already see the tests are finished the chemeter tests with a couple of thousand requests has been executed against the service triggered by captain captain is now doing the SLI retrieval that's some captain internals let's say to reach out to Prometheus and query all the data and captain is doing an evaluation and we can see the evaluation is done we can see already here the total score so it's red, red of course means not good, we got a result of zero we would need at least 75% to be able to proceed to the next stage or for a green result and we can also see all the SLIs and SLOs that have been evaluated so we can see actually our probe success percentage and the probe duration was not satisfied the reason for this is that the pod was actually not available for a couple of seconds since the chaos experiment killed the pod there was no other pod that was able to receive the traffic and it took a couple of seconds for the pod to come up so we with this experiment we could already evaluate the resiliency of our application and what we can now do is to play a little bit with our replica set or with other settings in our experiment we will just increase the replica account that saved to three I just saved this file and I just send it again so in this case I'm sending the same instructions I want to deploy the same version but I want to have a replica count of three this time so taking a look we can already see Captain was triggering Helm to deploy two other instances of this pod once they are finished then Captain will go ahead and trigger the tests again it's always the same workflow or pipeline whatever you want to call it it's always the same first the deployment test the evaluation and then the result of the evaluation with the promotion or rollback for example of the artifact so we can see the deployment is finished and we can already see the chaos runner is started at the same time also the Chemida tests will be started the chaos runner now starts our pod division experiment and this is also triggered and orchestrated by Captain so actually Captain is starting the chaos runner and the chaos runner is then starting the pod delete pod in this one will kill a random instance of those three instances of our hello service I'm not sure which one will be killed but one of those will be removed and another one of course will come up just in a second a new one is created because this one was killed by the experiment everything is good it will take about 30 seconds for it to come up and to be ready but in this case we would assume that the two other instances they are ready and they are here to receive all the traffic that should go also to the killed instance so we would assume that our quality evaluation regarding our resiliency would be quite good this time because we have a higher replica set than we have some backup pods that will receive the traffic of one of those goes down so let us take a look in the Captain's bridge it's again it's a new instance of our configuration changed it's the same version but this time it's a higher replica set deployment is already finished so right now the tests are being executed in the background and we are not only waiting for the for the litmus experiment to finish but also for the jmeta test to finish we can see jmeta service has finished and we can see the evaluation and this time we got a score of 100 since both of our SLOs are evaluated correctly or satisfying so our success percentage is 100% there was no downtime this time although one of the pods got killed and also the probe duration was quite fast faster than this 100 milliseconds that we had set as a limit so what we can see here is basically we included chaos testing into our pipeline into our CD next two performance tests and we called it the chaos stage that's the reason why you see a chaos here it's indicated right here because it was failing but with this you can easily integrate chaos testing to your CD and make sure to evaluate also the resiliency of your microservices that's the main idea it was a short and easy demo but if there are any questions we are happy to answer the questions otherwise we just have two more slides as an outlook what will come next with this integration but we are happy to answer questions here as we have just settled the demo in one sorry we are almost on top of the timing here so if you have anything else did you want to share with do it and maybe also reach out to people on the mailing list sorry yeah we just want to share this but Uma please go ahead no I'll put the slide I wanted to ask Alois if I can take two to three more minutes to give quick updates and to go through three to four slides so what will be the next for litmus and the captain integration we will also take a look at the litmus chaos results right now we are using Prometheus as the data provider for this evaluation but also litmus is exposing a lot of data for the experiments and we are also taking a look at the litmus data and we also want to improve this work by not only including it into the CD part but actually testing self healing methods and testing auto remediation by having litmus experiments as the tests and then auto remediation scripts and auto remediation instructions orchestrated by captain as the solution for problems or chaos introduced by litmus and if you want to learn more about this in a little bit more detail we have a joint webinar next week on November 11th we will share the slides and if you want to give it a try yourself you will find all the resources here thanks so much and Umar please share your thoughts sure that was great integration and I wanted to thank with the projects they were working on working sessions for three to four weeks so that was great can I share my screen as well again yeah okay so we were this is just an extension of you know the presentation so we were chatting with Harry about you know what are the next steps for the project and he suggested that before going to QoC it would be good if you could present it into the six chairs and that will be safe so primarily we have been seeing a great momentum in the last four to five months just before and right through after the project has become a sandbox so there were some people in the community to maintain us feel that you know we are ready for integration so to state that intent and provide a quick snapshot of why we think so so the real reason where why we feel that we are good for applying for or at least showing the intent to move to incubation is the adoption and which indicates the project maturity we are 1.10 release and also there were some huge deployments in production for some time now and they have become the contributors as well so we feel that we will definitely pass the due diligence very easily and we also are happy to state that this project now has been you know managed not just by my data but great contributions coming from a lot of others but we have 4-1 maintenance also in terms of incubate and amazone and and autopilot continuous solutions have been contributing recently to the project and we have established the open governance model where we have a lot of six inside platemas and there are about 16 meetings that have happened that have still come or some of some of the contributors or chairs co-chairs inside itmas and we actually integrated with the captain of Tito Spinnaker that's one of the big ones right so for all these reasons I know the project team maintenance team thinks that we will move to the next step as a quick snapshot these are our users some of them are vendors vendor users and some of them are they qualify the end user category and definitely Intuit is one of litmus biggest users and our CNC end user and then IHE, autopilot fn networks that have been using litmus in production so in the last two months since we have presented the project here at the Lurie chairs we have added six experiments and we have done about eight community meetups and I'm very happy to say that there are 39 contributors and 16 members and our Slack has been busy as you can see that in the last two months we almost grew again about 40% in terms of the experiment terms so we do a release of every month so the community can expect that definitely there is a release coming out on the 15th and last six months a lot of development happened and we released the litmus portal which is a platform for running chaos engineering for Kubernetes so as part of that effort we integrated with our Go workflow now you can run super complex chaos workflows that are closer to your real-life scenarios in parallel, in sequence you know so it is possible to take chaos engineering to a production level and as part of that we also introduced probes where we don't tell what should be the end result or a verdict you can define what you think should be a possible failure for a given test in the hypothesis and as part of the requirement for incubation we have updated all over the CACD inside the litmus chaos development they are all open so we have a full-fledged project and also monitoring is a very important aspect so we started the architecture completed it in showing chaos interleaved dashboards for example you might have a soft shop application on any other dashboard for doper now we have chaos interleaving as well so when you run some chaos test there is a red mark that comes right on top of it and also chaos is now possible to be limited to a namescoped and you can have multiple chaos operators in the cluster so developers can run as a small application so that's a very quick update and as you can see this is from DevStats it shows about from my data IntuVate Redat, HSBC, Microsoft DINs, OptiTo, Autopilot these are the top 10 contributors since sandbox so very very happy and thrilled to say that this project has received a great adoption and they are actually contributing back and it is working in the way it is supposed to be in this new project so this is another stat that says how the folks are increasing which shows that people are taking it changing and upstreaming it back so this is a quick update on who is actually contributing these are some of the primary contributors Redat from Israel is adding infrastructure changes to Likmus so that you can run chaos from Kubernetes on a resource that's outside Kubernetes so for example a VM that is orchestrated by Overt and the container solutions which runs Likmus on some of the huge production departments they have been contributing back with the network chaos, chaos stress and open ship support and there are other Microsoft team from Japan has complicated state the AKS certification Likmus in all 33 experiments at that time so Autopilot team has been using Likmus but they are also contributing back in short so these are some of the in the interest of time I just wanted to skip this share the slides and thank you for giving me few more minutes into the agenda but this is really about how Likmus project is running things open governance model meeting 6 and integrating with the ecosystem projects and actually gaining adoption and production and that stuff so with that I really want to stop this presentation and I wanted to thank for being this opportunity Thank you for the update I think it's great to see that project moving forward and obviously that's what we want to see is moving projects from sandbox to incubation I assume you officially created a PR for a TUC because that's the official process how these get routed thanks for the heads up and let's work on this and it's great to see the project working forward and it's great to see you collaborating closely with other CNCF projects as well and I think with this we conclude for today I wish everybody a nice rest of the day, nice evening depending on your time zone and talk to you again in 4 weeks not in 2 weeks so next 2 weeks we have CubeCon and we decided not to have a meeting during CubeCon, thanks everyone bye good job again bye thanks thanks everyone


So, everyone, hi. So, hope yesterday's workshop was great. You enjoyed it. So, do you have any questions? Everyone's okay. So, this time we have another set of great professors going to talk to you today. Yes. Good morning, madam. Can I ask one question, which I have? Sure. Please go ahead. Viscosity in transport properties. So, if I have a computational domain where I have like two fluids mixing and each of the fluids I know the individual viscosity and then there is a viscosity in that like a transport property. So, what is the difference between these two? Like, I'm a little confused. I hope you understood my question. So, see, if you have two fluids, you would typically move into a multi-phase solver in open form and you would have the ability to set the viscosity for each of these fluids. So, that is your question, right? If that's not your question then you'll have to repeat and I have to try and understand. Like, I'm just considering a computational domain where like a fluid is like there is a inlet in which a fluid is entering the computational domain. Let us assume that we only have like maybe one fluid stream just to forget about this multi-phase flows. So, and I know the viscosity of the fluid like the properties. Yes. Like pressure, temperature, velocity. And in the computational domain in like yesterday's tutorial, there was a viscosity which was there. And when I asked that question, someone replied that it is the viscosity of the fluid in the computational domain. So, is that something that we input from our side or like should it be equal to the viscosity of the fluid entering the computational domain? That's what my question is. I'm a little confused. Okay. So, see, you have a computational domain, right? So, let me assume that you have a pipe and you have fluid coming in, correct? So, as far as the CFD is concerned, the initial, so you will have a CFD simulation where you'll have to start from t is equal to 0, time is equal to 0. And to up to a certain time, you will be simulating this flow through a pipe. So, at t is equal to 0, at the inlet, you presume that water is coming in, correct? Now, at t is equal to 0 in a CFD simulation, what you also do is you assume that water is still in the pipe already, okay? So, there is an initial condition that is always set inside the domain and then you set an initial inlet condition, right? So, there are two aspects. So, since you already presume water to be there inside the pipe, okay? Inside the computational domain, it's not like it's an empty pipe, okay? It's having water already. You would set the viscosity same as that of the fluid. What you do through the simulation is you try to run it till the velocity profile inside the domain stabilizes. Does that make sense to you? Yes, ma'am. So, like as a follower, suppose if I want to increase the Reynolds number by decreasing the viscosity of the fluid. So, what should I vary? Should I vary this transport property? Okay. So, typically when you're doing single phase flows, you're operating on a single fluid which has a particular viscosity. It comes with a particular viscosity, right? So, when you want to change the Reynolds number, if you're changing the viscosity, you're changing the fluid or you're changing the temperature at which it's operating. If it's temperature dependent. Do you understand what I'm saying? So, if you're running it for a particular temperature and you have a particular fluid going in and you want to increase the Reynolds number, you typically would increase the flux which is going in. If you want to increase the Reynolds number, you increase the flux. So, what I'll do is at the inlet, I will set a time-based inlet condition where for the first few seconds, it's a lower flux and then I will increase the flux to a higher Reynolds number, flux. You get what I'm saying? So, the fluid remains the same essentially. Okay. Now, if you are operating with a different fluid, say, first few minutes, one particular fluid is going through and then after some 10, one hour and other fluid is going in, then additionally, you will have to accordingly set the boundary condition. Now, internal condition, you will have to deal with the numerics very carefully because after one hour, if the fluid is going to change, then what will happen is there will be a viscosity variation between the two fluids inside. So, that means I will have to do a multi-phase flow at that particular point with two fluids filling in and then a single-phase flow. Does that make sense? I did not understand it fully, ma'am. My objective was to look at the effect of increasing the Reynolds number by increasing the pressure and decreasing the viscosity. Okay. So, I was a little confused. What should I change if I'm looking at... So, you will have to look at... So, there is an option in open form where you can give viscosity not as a constant but as a variable property with respect to temperature or whatever expression you might have. Okay.  So, as your system changes, for example, I have done a simulation with variable viscosity with respect to temperature. Right? So, then in the viscosity situation, what I'll do is I'll give a polynomial in terms of temperature dependence. So, as my fluid is getting hotter, then accordingly, the viscosity will change based on the temperature it sees. So, you can explore options where you can input a table with different parameters, viscosity changing with temperature. Typically, viscosity changing is more stable with temperature. Okay. I've not tried pressure-based variations. I don't know. I think it will make your solver a little bit unstable. Yeah. Another way to ask, we have, suppose in yesterday's example, the viscosity value, if I remember correctly, it was 0.01. If I change it to 0.005... Yeah. And then run the simulation. What exactly is happening? Nothing is going to happen. You're just saying that another fluid is coming in. That's all. So, it's going to presume that the internal domain, it's going to just... So, if you're doing it with water, say, the simulation and then you make it to oil, changing the viscosity, then it will just presume that the entire simulation is oil. You understand what I'm saying? So, you just change viscosity. That means it's a whole new fluid. The domain will think that it's dealing with a whole new fluid. Okay. Okay. Now I understood, man. Thank you.


need you to make sure you can get into the platform. All right, are there any questions? If you do, while I'm gonna introduce one of the learning pathways in a minute, but if you have any questions at any time, please can you put it in the chat? Because then Tony, myself, and is Kirstie still with us? Yes, Kirstie's still here. We'll try and help you immediately. And if it's something worth, if it's something that should be shared, then I will raise it in the course of the presentation. Okay, all right. So make sure you found where the Zoom chat is. Have it fired up. Make sure that you're keeping an eye on it as well as listening to me. So any questions, you can either put your hand up or you can just type it directly into the chat. All right, so I'm in the moodle now and I'm scrolling down and I'm looking at the first one. Find open content. I've already done my baseline studies. I'm ready to start. So you will click on this link here. What happens is another program will open. So something that I might have to change the screen for. I don't think you can see this.  It's this one. Well, I'm hoping you can see this screen now. It should say find open content. Can you see that? According to the Zoom, it is. I'm just gonna make it a little bit less long. My screen is very long. All right, can you still see that? All right, so I'm gonna now introduce find open content and this is a little, this is the learning pathway and our design thinking at the time was we need to be able to get the information across very quickly. People in this day and age don't have time to go on workshops. You can't anyway these days. They don't have lots of time available for new skills. So we're gonna find a new way of doing CPD which is short, sharp, engaging, interesting, maybe sexy even, well, does an old person like me really understand anyway. So the idea then is this is what we're trying to achieve. We're trying to get key information but more importantly skills across quickly and sharply in an engaging way. All right. So here we go. This is a short, sharp tutorial to acquire the skills necessary to search for open content, to decipher creative comments rights and to evaluate the usefulness of open educational resources for new purposes. So that's what we're trying to achieve. So keep that in mind with your evaluation hat on. Does this little learning pathway achieve that? So let's go in. So here's our list of what we're gonna cover. I'm gonna do a little bit of it now but they're gonna ask you to push on on your own in your own time. Okay, so what are we gonna cover in this learning pathway? So this is our little schematic of what we're trying to achieve.  Why even bother? Then we're gonna look at open licensing. What do these creative commons licenses allow and what do they not allow librarians and academics to do with the resources? We're going to look at what different types of open content are there. Now we've mentioned OERs. That's in the name of OER Africa but there's different types of open content. So we'll also have a quick look at open courseware. Very useful. And then MOOCs. Mm, MOOCs are open but they have issues. So we'll talk about the issues as well. Okay, then we're gonna go on and look at open search techniques. So how do you find them? And this is the sad thing about these OERs is that yes, they can be brilliant but they're not always easy to find. So I'm gonna show you a couple of little tricks and then you can encourage your academics to also look for things that are relevant and so on. So we're gonna look at Google advanced search. We're gonna look at the creative commons search. We're going to look at YouTube, for example. YouTube allows us to find openly licensed videos and then there are some very well known famous repositories of open content. So we'll look at MIT, we'll look at Merlo, we'll look at Open UCT and various other repositories and then what happens when you found them? How do you know if they're any good? So we're gonna have a little section on evaluation. How do we evaluate your results when you find them? So we're gonna look at that, okay. So that's the overview of the little learning pathway and we're gonna work our way through that. I'm just checking the questions. No one's asked any questions yet, is that right? Am I that good an educator that you guys are all on board and know exactly what's going on? Yeah, no questions on the chat. Just to remind people, the easiest way is to put a question on the chat and then either Nkem or I will pick it up. This is Tony Leliot speaking. Thanks Andrew. Okay, all right. So therefore there's all the outcomes. You can just click on these little things and find out a little bit more about what the outcomes are. There are apparently five. Do you know how to decipher open licensing? Can you identify different types of open content? What are the search tools that you need to be able to use in order to find the content? How do you work your way through these open repositories? And then when you found something, how do you evaluate whether it's any good? All right, so now we start the first real piece of content in the learning pathway. Why even bother? Okay, is there an advantage in looking for and using open content? And we like to think obviously there is. And in fact, whenever we talk to educators, all right, let me ask you a question. How many of you feel comfortable with OERs? Do you know what they are? Or is this a new concept for you? So I would like everyone to put in the chat whether they think they are familiar with OERs. Do I need to go into a lot of detail or can I just flick past them? Let me know. So now an activity for everyone. I want you to type in the chat, how familiar are you with OER? How familiar are you with OER? Do you know what they mean? Have you used them? So to what extent? Try and rate your or give us an indication of how familiar are you with OERs? And I'm waiting, I'm watching the, I'm watching the chat to see who says what. Yeah, please just write in the chat space.  Just share with us because it will help us to know how to proceed. Okay, so Emmanuel's been very brave and very fast. Emmanuel's already told us. Okay, what about the rest of you? I want to hear what you've got to do. Right, Aloysius. The concept may not be familiar, but probably I know it's in a different methodology and that's quite possible. Okay, maybe you know it as something else. So all right, so it looks like I'm gonna have to unpack these things. What else? That's two of you. Emmanuel's also not so familiar, whereas Edward says he is familiar with him. Please, be down this road before. Olga, she's not so sure. All right, what about the rest? Raheela, Raheela, we've not heard from you. We've not heard from you, Ijeoma. We've not heard from you, Ngazi. Oh, I'm happy that the University of Abuja Library that you're here, so just tell us. Do you know anything about OER, you know, much, little? It doesn't matter. Every answer is correct. We just want to know how to proceed so that we'll be sure that everybody gets on the same page eventually. All right, so we've got Francis. Says, yeah, also needing some clarification. We've got Richard saying that he is familiar with OERs. Doris, where are you? Ngazi, where are you? Hey, come, let me know. You're being watched. You're online. Your every moment is being analysed by artificial intelligence. Don't get carried away, Andrew. Well, my intelligence is artificial, probably. All right, okay, come on, come. Where's these comments? You're not passively listening. You are involved. Let's go, let's go. Type up in the chats. Yeah. Passive learning here. We want you engaged. Come. Okay. Raise your hands. Yeah, find that chat. Come, come, come. All right, Techno Free raised her hand, so we need to unmute her. All right, okay. Hello. Good morning, ma'am.  I'm Raheela, I live already. I'm on the line listening to you people. I'm the one on this one. Three plus. Yeah, we asked a question. Do you have any idea what OERs are? Are you familiar with it at all with the concept? Have you worked with it? But like we said, no answer is wrong. We just want to know what you know so that we can help you know better. So do you know about OERs? No, ma'am, I don't know. At all. All right. Okay, then you're in the right place because we're gonna give you all the data. All right. Exactly. Okay, people, this is, as we go online and start doing more and more of these zooms and these teams and these Skypes and all that type of thing, and it's a good facilitator and it, and when you get a chance to facilitate, you must do the same thing. Be very careful that people don't become too passive. They just sit there pretending to listen, all right, because there are no learnings happening at all and just wasting everyone's time. So that chat was a little experiment to see if you're following, all right?  All right, so I'm picking up from what most of the people have written here, that we need to explain these things in some detail. Okay, there's three or four of you familiar, but the rest of you is pretty new, okay? So what are these OERs? And the little tutorial, the little learning pathway tries to answer that question. So many educators appreciate the value of openly licensed content, that is to find and share quality, free educational resources where you don't need to ask for permission and that you can adapt to better suit your local context and needs. All right, so, okay, these OERs then, free. So there's no copyright costs involved, all right? That you don't have to go and ask the author or the institution if you can take a copy that's kind of given. You can take an OER and make as many copies as you like. That's no problem. And that some of them allow you to change them. All right, so the thinking then is that a resource, it might be for a Psychology One course, but if you can get it and it's got an open license, then you can adapt it. So if it doesn't quite fit the curriculum or the syllabus, you can adapt it. If you think there are some local examples which better illustrate something, you can insert them into the content. If you feel the language is pitched incorrectly, then you can change the language to suit different English users. Or if it's in the wrong language, you can translate it into another language without asking for permission. Okay, so they can, most of them can be adapted. Let me play you a little prop again, the movie, okay? This is quite cute. A few years ago, a professor taught a climate change course reaching about 100 students per semester. One day he thought, if I could upload this course online, then not only would my 100 students have access to it, but others as well. So he did. And this is what happened. Anna sent the course's content across the country to Alex, who was studying climate change. Alex found it so interesting that he forwarded a copy to his friend Lulu in Africa. Lulu was developing peer-to-peer courses with Philip. So they remixed the content with other resources and created a new course about the impacts of climate change in Africa. Alex, a participant in the course, shared the content with Gabby, who was studying environmental policy in Latin America. Gabby brought the content to her class. Together, they translated it into Spanish. After that, Gabby's professor shared it with his other classes. Myra, another student, shared the content with her father. He passed it on to his colleagues. Gabby's professor also forwarded the content to David, a colleague in the UK who was researching climate change. He updated some of the data, adapted it to his study, and published an article in an open journal. Researchers from all over the world were able to read the article. David sent the updated content back to the original professor. By then, his course had reached so many more people than his one hybrid students. Years later, many schools have begun to follow the example and open access to their content. Governments began promoting the use of open textbooks and students began saving money on books. Other innovative universities began to open access to entire courses, making them available to participants from all around the world. These are open educational resources, teaching, learning, research resources that can be reused, redistributed, remixed, and revised. Open educational resources are accessible to everyone, learners, teachers, researchers, parents, workers, citizens, to you. This is open education, knowledge as a public good. Everyone has a right to be educated, yet only a few have access to school. Open educational resources increase access to improved quality of and reduced costs of education. Sharing knowledge is important. How you know, open educational resources give everyone the opportunity to learn. Okay, so there you go. A little bit of propaganda. Obviously, those are principles are correct. It doesn't always work as beautifully as that. There are often, you've got to work a little bit to really get the sharing to happen. But as librarians, you guys are very important in that chain whereby people are sharing and people are adapting a resource to work in different contexts, for Africa, for Latin America, and so on. So it's very important that you guys understand what these things are and then begin to get to grips with the skills required to actually do what seemed quite easy in the video. In reality, it takes a little bit of practice. All right, so that's what OERs are. You guys are reasonably happy that you're beginning to become familiar, all right? The problem, though, is in the video, it was all very easy for people to share stuff. And it is out there that they are out there waiting to be found, but not everyone knows how to find them and how to share. So that's what these little learning pathways are for. We're trying to unravel that little knot. So the issue is how to find these resources as there is no one stop shop for open resources. There's not one repository. There's not one door to back. So we need you guys to be able to find them, okay? Do you know how to find OER? Well, that's why you're here. Okay, the idea is we will show you some tricks and how to quickly find relevant OERs. Okay, so each of these little learning pathways, there's a think section and there's a reflect section. And we would like you to spend some moments having a ponder on each of these questions before you plow on into the next part of the learning pathway. So please have a little reflection. And then if you need some generic feedback, we have given you a little box. You can click on it to try and answer your question or at least to provide you with some background. All right, so let's get going. How do we know when a resource is open? And how is it different from any other educational resources? The question we would like you to consider at this point. Okay, and the trick is we know if something is open because of its license. Okay, each of these openly licensed materials has a little license plate. And it tells us what are the various things we can do with the OER. So in order to find good OER, you need to understand also what is open and what is not. And if it is open, how open is it? All right, so we're going to have a little look at Creative Commons licenses. Now, most OERs, I would say 99.9% of OERs use the Creative Commons system, whereby it identifies rights and permissions. So keep that in mind then. We know an OER is open if it has a license. And it's most likely the license will be a Creative Commons license. So how do these licenses work? So I'm going to show you another video to get things going. Educational resources are automatically copyrighted when you create them under U.S. copyright law. So let's say that I wrote a textbook, for example. I own that and nobody can use it without my permission. When we openly license that textbook, what we're doing is we're giving a license, we're giving permissions to the public to use that book under the terms of the license. Licenses are a part of the copyright system all around the world. They're used by big companies, like when a clothing manufacturer wants to sell a superhero t-shirt. And so licenses are part of the fabric of copyright law. But all of those licenses are negotiated one-to-one. So when a company wants to use a text or a song or a character or part of a movie that belongs to a different rights holder, they have to have their lawyers sit down with the other lawyers and it takes time and money. So adding a Creative Commons license to a work is like saying, hey, this resource is free for you to use however you want. The basic Creative Commons license is the CC by license or the Creative Commons attribution license. And that's the most basic Creative Commons license. And all it says is you can use this work, this copyrighted thing, this photograph, this book, this song. If you attribute it to me, the creator. The reason a lot of educators and governments more and more are choosing that license is that the only requirement is to give proper attribution to the author, to the original creator. That's really easy to do. It also allows you to remix those works with other openly licensed works in a very easy way. And teachers have always given each other materials and if you know the person you're sharing the materials with, you can give them permission. But when you want to share materials with teachers across the country and potentially teachers across the world, you need a standardized way to do that. And that's what the Creative Commons licenses are. Okay, all right. That was it. Oh, we got someone giving us feedback. It's in Gauzy again. I don't know if that worked, but all right. So all right, so then there are these, there are these different types of licenses. And if you look on the diagram on the screen, here they are. So you can see there's six very well-known popular Creative Commons licenses. There they are, one under the other in the first column. But you'll notice that each of them are made up of a combination of rights. Okay, so there's the four rights in the right-hand column. Attribution, no derivatives, share alike and non-commercial. All right, so that's the trick is whereas full copyright is all rights reserved, it means you can do nothing with the resource without asking for permission, okay, from the publisher or the author. And public domain, I don't know if you've heard of public domain, but that's the other extreme, it's no rights reserved. That means that you basically have surrendered the resource and it's out there and people can do with it what they like and you've kind of lost control of it. Whereas Creative Commons lets you play in the middle, some rights reserved. So these are the four rights that you can reserve. And the idea then is you don't lose your resource, you just permit people to do various open things with it. So for example, attribution, as it said in the video, you can do whatever you like with my resource, but you must attribute me as the creator. All right, so that's no skin off our nose as librarians and as academics, we've always had to cite our sources. So cool, it's the same, all right. Indeed is no derivative works. So it basically means there that you can take the resource, you can make copies, you can hand it out to your colleagues, you can hand it out to your students. There's no restriction on making copies, but you may not change the resource. All right, so the idea then is you have to use it as is, okay? So that's no derivatives. Others can only copy, distribute or display verbatim copies of your work says the little label. Okay, I'm going to jump to the fourth one, it's a bit easier, non-commercial simply means that you can do whatever you like with the resource, but you may not sell it, okay? You may not sell the IP, the IP has to stay free. The intellectual property does not for sale, it is free. All right, so the idea then is you can't make a profit from using the resource. So say for example, you loaded a whole load of OERs which have these NC as part of their license onto a USB stick and we're handing it out to your students, but you charge them and you charge them more than the cost of the USB stick. You're making a profit from someone's IP, so that is not allowed if the license has NC written on it. Okay, non-commercial. And then the fourth one is called share alike. This one's a little bit more complicated. It simply means that should you take an OER and adapt it, make a derivative, make a new version of the resource, then your new version needs to have exactly the same license. So the license carries over. All right, so you can use the resource, you can do what you like, you can make derivatives, but your new version must be shared alike. It must be shared in the same way. So therefore we call that one share alike. Cool, so those are the four rights that you can reserve under Creative Commons. And if you look on the left again, the first column, you can see the six most popular combinations of those rights. The lady in the video said the top one is the most open. You can basically do what you like with that one, but somewhere you must attribute the creator. The third one, for example, is similar. It says you can do whatever you like with the resource, but you must attribute the creator, the original creator, and you may not sell that piece of work for profit. The fourth one is CC by ND. You can do whatever you like with the resource, but you must attribute where the original came from, and you may not change it. You have to keep it the same, you have to use it as is. And then the very last one is quite restrictive. So if we're looking at it from the terms of what is open and what is quite restricted, then as we got down the list, it became more restrictive. You could say this last one, CC by NCND, is not that different from full copyright. Yes, you're allowed to make copies and distribute them, but after that it's pretty fixed, all right? You must attribute where it came from. You may not make a profit from it. You may not put it in the textbook, which you're going to sell or anything like that, and you may not change it. It has to stay exactly as it is. So that one is quite restrictive, and that's why it's at the bottom of the column, okay? The two arms skipped out. The second one and the fifth one have SA in it, share alike. That simply means you can do whatever you like with the resource, but please attribute, please don't make a profit from it, and any derivatives that you create, any version, new versions, must have the same license. Wow, that was fast. Did you get that? Maybe not, all right. So I would like you to, I'm up to, it's now 12 o'clock, so I'm going to start winding down this little demonstration. We're going to look at adapting open content in a moment, but for those of you who have signed up to do this particular learning pathway, I want you to go through it. I want you to have another look at these licenses, and you'll notice that there's actually a quiz. So we want to see to what extent all of that yada, yada, yada, yada, that I've just done is in there. So for example, let me just do a question. Okay, not that one. All right, this one, this one. Okay, so in the chat, I want you to answer this question. The question is, which statement best describes the ND right? There's the symbol, the ND right? There's the four options. Is it A, B, C, or D? So in the chat now, I want you to type. Everyone has to tell me if they think it is A, B, C, or D. All right, so let's have a look at it again. The question is? Yeah, scrolling down a little, yeah. Which statement best describes the ND right? Let me get it a little small. You didn't know what ND means? Ah, well, let's see if they can remember from my blub. Can you remember ND? It's got the symbol of these two horizontal lines. ND, what does that mean? That will help. In that, let's get it so it's all on the screen. There we go. That's which one best describes it. Is it the first? We're using letters. So is it A, B, C, or D? Which? It's a little bit smaller, Andrew. All right, hang on. Yeah, so that you can feel the screen. How do I do that? Hang on. Give me a second. Let me find how I make it. Is it better? It's okay. No, we can't do this. Yeah, well, yeah, yeah, that's okay. All right, so I'll read it again in case it doesn't appear on your screen. Maybe you're on a phone or something. So basically, the question is, which statement best describes the ND, right? And there's that image, the Creative Commons icon, which describes this ND, right? And the options are, A, the users cannot make any copies. B, users cannot change the resource. C, users can change only the images in the resource. Or D, users can make only one copy of the original. So based on what I said a moment ago, can you now in the chat answer the question? Is it A, B, C, or D? Go. Are we here? No, we're waiting. We are watching. We've eagle eyes the chat. No, no, no answers yet. Are you guys even there? All right. There we are. Yeah, all right. That's one. Here we go. We've got two so far. He's gone for B. What the rest? Can you remember what I said? Or were you snoozing? Were you sleeping? Third one. There's another one. Of course, it's gone for B. Emmanuel's gone for B. Richard's gone for B. Yeah, they're coming up now. Okay, come in the chat. Which one? Idioma. Or idioma has gone for B. Aloysius. B. Okay, I think we're beginning to see a trend, guys. Okay, is it B? Is it really B? Raheela and the University of Abuja, we've not heard from you. Please type in the chat box so that we can feel you. And two of you are using one device. I can see two people from the University of Abuja. All right, Raheela has raised her hand. Okay, Raheela, maybe she can type. All right. Yeah, let's unmute her so that she can say her answer. She can give us her answer. Raheela. Raheela, you can talk. You're muted. I can talk now. I've typed it already and I've sent it. I don't know why he's not reflecting that. All right. All right, I'm already full with you people. Did you go with your answer? Which one did you go with? What answer did you give me? What is cracking? I said I'm online already. I said, is it A or B or C or D? Which answer did you provide to the question? A. A, you said. All right, let's try. Let's see what happens. So most of you have gone for B, all right. So I'm going to go for B because it looks like it's a serious trend. So let's click on B and you'll see it says submit. So let's find out what it says. Correct. Okay, yeah. No derivatives is what MD stands for. No derivatives. It says it lets you copy, distribute, display and perform only original copies of the work. Okay, so it can't be changed. If you want to modify the work, you must get permission first. Okay, so cool. Obviously, I'm a brilliant educator because it looks like it was like 90% of my class got that one right. Okay, and there are some many other questions. So we would like you to now please, for those people who have signed up for finding open content, can you please work your way through the learning pathway? And there will be quizzes and there's videos and there's drag and drop experiments and so on. So we want you to get stuck in and see if you are experts on finding OVRs at a later stage once you've worked through it. Okay, are there any questions? I'll put the link. You can get the link for this. I'm doing the baseline as well, Andrew, before they go to the pathway. Yes. Before you start, remember this is a scientific experiment. All right. So we need the baseline data. So please, can you do the baseline quiz first? Okay, and it's in there. Let me put the link to the Moodle in the chat now so that you guys can start seeing if you can get in. We just come here and here it is. Okay, so the Moodle, I'm putting it in the chat, is there. Right, it's going to ask you to log in. And if you've forgotten your login or it doesn't work, you need to contact me. And we've got another man, Elias, who will help us if there's really big issues. Okay, all right. If you want to go straight on and do the baseline first, let me give you that link. But again, if you want the certificate, you've got to go through the Moodle. All right. But if you just want to go and do the baseline, I'll give you that link to, sorry, give me a second. I'll get it, find it. Finding, finding, finding, finding. Where have you gone? Oh, let me just use the Google link, the Moodle link. Finding, finding, finding. Here it is. Okay, and I wouldn't, I wouldn't try and type it. It's pretty long. But here it is coming up again in the Moodle, sorry, in the chat. So the baseline is here. Right, did you get that? So if you want the certificate, I want you to go through the Moodle first. If you don't care about the certificate, you can go straight to the baseline and just do it on Google Forms. It's there. And again, if you want the certificate, you've got to access the learning pathway from inside the Moodle. Otherwise, you can just click this link. I hope this works. Control C. All right. And then the LP, we call them LPs, can be got, I think that works. All right. So I'm going to wrap up from a learning, from a finding open content section. If you want the certificate, do everything from within the Moodle. There's the first link. If you, just let me get on with it. Can you do the baseline, please? There's a link to the baseline. And then there's the LP, the learning pathway. If you just want to go to the learning pathway and just get on, find out more about Creative Commons and OERs and finding them. Then you can click that link. All right. I see Bernard Egeoma says he does understand. Understand what? Give me, where are you struggling? Andrew, I'm just looking at time. We are after 12 now. All right. I'll get going on the next one. Just, all right. I'll just give people a chance and then. All right. So there you are, people. We want you to go, if you want to access all the goodies in one place and get a certificate at the end, you need to make sure you've logged into the Moodle platform that I was showing on the screen. If you want to, if you're not interested in that, we still want you to do the baseline. So can you click on the second link, which will take you to Google Forms and fill in the baseline. And again, if you just want to access the learning pathway and get on with it and find out more from what we're doing, then there's the third link. All right. However, in the Moodle is all those links. So you don't have to remember them. They're all in one place. Oh, I see. Bernard's interested about the certificate. Certificate, we're only giving to people who are using the links in the Moodle because Moodle will track you.  And therefore, if you want the certificate, it's not me who's going to give you the certificate, it's Moodle who's going to give you the certificate so you have to do it from within that place. All right. Any more questions about finding open content, please talk to me and I'll try and help.


Hello all, welcome to Physiology Open. Before we start, just want to let you know that I have put up a question in the comment section below. Try to think about it and answer it after watching the video and I will give an instant feedback to your responses. Okay, now let's discuss lung compliance. Compliance in simple terms means stretchability. If an external force tries to pull or stretch any elastic substance, it stretches or expands. If we ask the question that how much compliant that elastic substance is, we should know how much force caused how much change in length and then we express it as change in length per unit applied force. Now let's take an example of two different springs. If we apply same force to pull both of them, suppose that blue spring pulls this much length, while the red one stretches more, maybe this much length. Can you tell which of these is more compliant spring? Well, obviously this red one isn't it? Since change in length per unit change in force is more for this red one. So we call that this red spring is more compliant than the blue spring. Similar kind of concept applies to our thoracic cage and lungs. Both are elastic structures and can expand. But here compliance is expressed as change in their volume per unit change in pressure across their walls. So basically force created by contraction of inspiratory muscles pulls the thoracic cage and this at the same time causes change in pressure across the thoracic cage. Similarly, the change in plural pressure due to thoracic cage expansion creates pressure difference across lung causing its expansion and hence increase in its volume. Now whose compliance are we talking about here? Is it lung compliance, thoracic cage compliance or is it compliance of both thoracic cage and lung combined? Think about this, that since lungs are housed inside the thoracic cage, shouldn't we determine the compliance of both together as a unit? Well, both thoracic cage and lung function are important for normal functioning of the respiratory system. For example, say Y amount of change in pressure is required to cause X amount of change in volume. Now see thoracic cage is a stiff, our inspiratory muscles need to work more to cause same expansion of thoracic cage and hence cause change in Y pressure across the lungs. So even if lungs are normal, work will increase. Similarly, thoracic cage is normal and lungs are stiff. Then the Y pressure across lung wall will cause lesser volume change than X in lungs. They may be X minus 10. So to bring X amount of air, more pressure change is required. So again inspiratory muscles will have to work more and cause more expansion of thoracic cage so as to generate more pressure across the lung walls. So in first case for the generation of the same pressure more force is required while in the second case the same pressure is not enough for lung expansion and hence thoracic cage should expand more to cause more pressure change. So basically individual compliance will tell us about where the problem is and combined compliance of lungs and thoracic cage tells us about how lungs and thoracic cage are operating as a unit. From now on we will focus on lung compliance only. Okay till now we have discussed that to determine how much the lung is compliant we need to know the pressure difference across its walls and how much lung volume change is occurring with that much pressure difference. But here by pressure difference across its walls means which pressure? There is atmospheric pressure outside thoracic cage then plural pressure between the plural membranes and within the alveoli there is alveolar pressure. So which pressure should we take? See for any unit we should take the difference between the pressure in the space immediately adjacent to it. So for lung compliance we will take difference between alveolar and plural pressure. They are the ones which are immediately adjacent to it. This difference is also known as transpulmonary pressure. Can you guess difference between which pressure should we take for determining compliance of thoracic cage? Yes for thoracic cage we will take difference between plural pressure and atmospheric pressure. Okay so how much is the change in normal transpulmonary pressure during tidal inspiration and tidal expiration? At equilibrium that is at the end of tidal expiration when no air is flowing from atmosphere to the alveoli alveolar pressure is 0 that is equal to the atmospheric pressure while plural pressure is minus 5 centimeters water. So transpulmonary pressure at this point is 0 minus minus 5 so this is equal to plus 5 centimeters water. Now plural pressure during inspiration is minus 8 centimeters water while alveolar pressure is minus 1 centimeters of water. So transpulmonary pressure will be minus 1 minus minus 8 so it will be plus 7 centimeters water. Okay now let's calculate it for expiration. Plural pressure during expiration is minus 5 centimeters water while alveolar pressure is plus 1 centimeters of water. So transpulmonary pressure will be plus 1 minus minus 5 so it will be plus 6 centimeters of water. Considering tidal volume as 500 ml for a volume change of 500 ml during inspiration the change in transpulmonary pressure is from plus 5 to plus 7 that means 2 centimeters water while during expiration this change is from plus 6 to plus 5 only that is 1 centimeters water. So for similar change in volume during inspiration and expiration pressure changes are different. That means lung compliance is different during inspiration and expiration. This pressure volume relationship of lung during inspiration and expiration is expressed using pressure volume curves. So now let's look at the pressure volume curve of the lungs. In this graph X represents transpulmonary pressure in centimeters water while Y represents volume change in ml. Notice two things here. First one that the relationship between pressure and volume change is not linear rather slope is less or it's kind of flat at the base and at the top. Second thing the relationship is different during inspiration and expiration. See the graph lines are different. Okay first let's see why it's flat at base and top. Flatter path means that for a similar change in pressure volume change is less. See I will take similar change in X axis for the transpulmonary pressure. Mark it on the graph maybe on inspiration line and then see how much volume is at base pressure point. See at the top volume change is only this much while it is more in the central part of the graph for the similar pressure difference. Similar is the case with low lung volumes here at the base. So that means lungs are stiffer at very high and very low lung volumes. You can think of it as a balloon at the beginning of its inflation it's difficult to inflate isn't it? Then everything goes on smoothly in the middle part of balloon inflation and when it's completely filled then also more force is required for causing minor change in its volume. Okay now let's come to other phenomena that why the graphs are different during inspiration and expiration. See in this graph we will take two points of volume say from here to here and now we will extend it to the transpulmonary pressure for both inspiration and expiration lines. See the pressure values during inspiration and expiration graphs? It is much lesser for expiration graph isn't it? That simply means that pressure required to inflate the lungs is much greater compared to the pressure required to deflate the lung. This phenomena of lung behaving differently during inflation and deflation is known as hysteresis. But why is this suffering? Why more pressures are required for inflating the lungs than deflating? Well see again and again we are seeing that lungs are elastic structures that is they can expand but remember that they also have a tendency to recoil back to their neutral position when it's stretched and as soon as that external force is removed which is causing the stretch they come back to their resting position because of this recoil. This is because major component of lung connective tissue is elastin and there is collagen also. Elastin provides elasticity and collagen gives tensile strength to lungs. That means due to inherent makeup of the lung tissue that is due to elastin this elastic tissue is resisting expansion but is promoting recoil. So during inspiration there is an opposing force that recoil is there and during expiration there is promoting force. So obviously pressure change required will be different during inspiration and expiration since one more factor is coming inside right? So can you think what will happen to lung compliance if this elastic tissue is destroyed? This occurs in emphysema or in case of aging lungs or what will happen if there is too much collagen as occurs in fibrosis. See in emphysema recoil is decreased causing increase in lung compliance. Too much fibrosis on the other hand makes it a step affecting its expansion. There is another factor which affects lung compliance during inspiration and expiration. It's the surface tension. See all alveoli are lined by a thin layer of fluid which is secreted by their lining epikellium only. As fluid has a natural cohesive property fluid molecules pull together tending to collapse the alveoli thus opposing the expansion of the lungs. That means left to itself there will be two forces opposing lung expansion one is elasticity and second the surface tension of fluid lining the alveoli. It will be too difficult for the lungs to expand isn't it? So this surface tension is counteracted by surfactant secreted by a type 2 alveoli cells which decrease the quiesce pose between the molecules and hence decrease the surface tension. So can you tell what will happen to lung compliance if surfactant is not produced? Yes, lung expansion will be affected too much and there will be extreme tendency to recoil due to surface tension. Alright now try to answer the question given in the comment section below to check whether you have understood the concept or not. And thanks for watching the video if you liked it do share the video and yes don't forget to press the subscribe and like button thank you.