Welcome back to SuperCloud 4, where we're exploring the intersection of cloud computing and generative AI. In particular, we're interested in the disruption potential of AI, the importance of quality of data, and ways in which organizations can most effectively and efficiently bring AI to the data. And with me is Thomas Hazel, the founder and CTO of Chaos Search, a firm that brings intelligent search capabilities to cloud object storage, which eliminates the costly efforts to move data. Welcome Thomas, good to see you in studio again. Yeah, great to be here. All right, so it's been almost a year since the chat's GPT heard around the world as I sometimes say. When you first heard about that, what was your initial reaction? You've seen the rapid innovation since then. What was your reaction? Did it change the way that you thought about the opportunity and where do you land now? Well, I think we all logged on to chat GPT and play with it probably around December. But what I saw in January was the world changed. It wasn't so much the innovation, it's been there, it's been around for a while, but the global consciousness of what this could mean to business to really to everything and the focus, the energy really, we changed. And so as a founder, as a technical founder of a analytical company, I said, we got to be part of this. And so early on we adapted the technology to Chaos Search and we offer an AI assistant that you can now have a conversation with your data. And it's pretty cool, it demos really well, but it really solves the problem of I don't understand complex APIs, I don't understand high level tooling, I can just log in and have a conversation just like you do with chat GPT. So let's back up a little bit. How does Chaos Search play in this sort of ever changing field? Where do you fit? Well, so we go after log analytics at scale. We just announced Chaos Lake DB last week where we allow customers to stream their data into their data lake, GCS, S3, and we auto discover, auto index the data such that you can use well-known APIs like Elastic API or SQL via Trino. Now we don't have any elastic or press on the hood. That's the costs and complexing challenges. We re-imagined the whole data architecture and database but we allow you to do observability, security, application insights, but again, at scale ease of use and reduction of costs dramatically. So Thomas, where specifically does AI fit and how do you bring LLMs and you're not building your own LLMs? Paint that picture for us. Yeah, we took our data solution and we integrated well-known API, well-known LLMs like chat GPT, Vertex AI and now we're working with Amazon's LLMs. And what we've done is use it as a reasoning engine. So we don't put data into the LLM. We actually ask the LLM intelligent questions ask on our database. So for instance, I might say I'm a security analyst and I present the LLM with say a basic schema and I'll ask it to craft a search query and or a SQL query to do the query on our database. So it would use the hallucination and obviously directly less expensive to build your fine tune your own LLM and that combination allows our customers to interact in a new way, a conversational way. Okay, so you're sounds like you're testing or actually implementing all kinds of different LLMs. Open AI you mentioned Vertex AI, which is Google, right? The bedrock I think is just now coming into the AI, right? Yeah, so there's a variety of LLMs and to us really whoever provides the best LLM or our customers can choose which LLMs they want to use and then we integrate with that back end. So to us, we see it as a co-pilot and analyzing your data. So a lot of these large language models understand what it means to you know, what type of failures you might have over the last year. How do I query that and say elastic? How do I analyze that data in SQL and using that partnership that if you will chain a thought and agency to bring those two worlds together but not having to share the raw data with the LLM. Okay, so it's bring your own LLM. The cloud vendor presumably is responsible, correct me if I'm wrong, for making sure that the LLM vendor doesn't have access to the data. Is that correct? And we purposely as our phase one approach, we do not share any data with the LLM whether public or private. You know, a lot of times people think that you have to train up the LLM to make it have some type of intellectual answer. We just use it as a reasoning engine. So our database plus this intelligent region engine and bring these two worlds together. No sharing data. Now we do share schema to help facilitate a question but it really could be any LLM private or public. As a technology company, how difficult or simple is it for you to integrate those LLMs into your environment? Is it a selection of, is there like a menu that you can choose from? Can I use, you know, meta or not? Do you have to do any work there to integrate? Well, so as you probably heard prompt engineering is quite popular these days. And so what we've done is connect to a variety of backend LLMs and we do unique prompt engineering. And what's interesting is that prompt engineering really goes across all the LLMs that we've been using. And through that prompt engineering when a customer asks a question, we say, hey, with this information, with the customers ask, how would you craft a question on our database? And so it really hasn't been terribly hard to integrate with different LLMs. Now, different LLMs are better than others. Chat2D is pretty good. Vertex AI is pretty good like Bard is pretty good. But, you know, as they get better, our reasing engine gets better. So to us, we have a plug and play architecture that brings in the LLM with this chain of thought, with this prompt engineering we do. And again, our backend high scale database. When you say ChatGPT, are you talking specifically about ChatGPT or OpenAI's, you know, GPT-4 tools or both? I'm talking about OpenAI's LLM in the backend there. They have a API backend service that you can integrate with. And I've played with LLAMA as well. These are all toolings that as we bring those to market, it's really any LLM that the customer wants will allow them to support. I mentioned prompt engineering.  We were at this event. And he attends Boston Latin high school. You know, very prominent school in this area. And I was asking if he uses ChatGPT and he said, well, not for school because the teachers don't allow that. But my attention is they should be teaching prompt engineering in school. Do you agree? Well, if you have interesting questions using a tool like ChatGPT, really super powers your ideas. And so you don't want it to do all the work, right? Cause it does have a ability to do some pretty clever writing, but I think we all need to understand how to use it, how to leverage it, whether you're a software developer, a blog writer, now with a multimodal inputs, you can take an image, audio and transcribe it and then ask questions about that. It's really amazing. And we know the future is this. So I think our students, colleagues should know how to use it. You'll lean in. All right, let me get back to the discussion. So Cisco just bought, as you know, Splunk for 28 billion. So there's obviously a significant interest in observability and log analytics and that entire space. How can you take advantage of these shifting market trends? Some of this consolidation, but it's clearly a signal here. Yeah, Splunk was the original log analytics platform. Their credit, it's a Cadillac in sense of its capability, but it was built many, many moons ago. And what we want to do is go after those use cases, but with a modern architecture, a lake architecture and doing a way that dramatically reduces the cost. So very often we replace Splunk or augment Splunk because great product, but at scale, it can cost you millions, tens of millions of dollars. And so we have customers that we're using, say Splunk, maybe doing a terabyte a day, but couldn't afford to go to 50. And they bring chaos in and we go to 50, black Fridays go up to 300 terabytes per day. That's something that you just wouldn't want to do in Splunk, but it's a wonderful product. The key is that we built an architecture, modern architecture, a lake architecture that allows customers to stream all those logs into one central spot and publish tooling that they know and can use. Okay, you're saying you wouldn't want to use Splunk for that, why? Because it's just too costly. I mean, if you'll Google Splunk and cost, I think. Well, it's always the big criticism of Splunk, the oracle of log analytics, but okay. And then explain what one has to do to get data into whatever observability environment. I love Splunk too, I don't mean to pick on Splunk, but how you have to prep the data, move the data, and I'm inferring that you eliminate that heavy lifting. Yeah, I mean, we know these tools are great, but as I mentioned, they're costly. They're also complex and time consuming. So imagine if you have messy data. Log data is quite messy, quite complex. Schema's changing over time. And you have to build a pipeline and ETL process to clean the data, to put it into your database of choice, stack of choice. What we took at Chaos Search was the ability to stream your data to your lake, as is, and we discover it, we auto discover the schema and automatically index the data. And if schema changes, we worry about it. And through what we call data refinery, you publish lenses or views into it that you can do dynamic schema change or transformation that you can do search on or do SQL on. And so it's really problematic. We've seen it time and time again that a lot of these relational systems don't go after that space because of that crazy, messy data. And we really went full in to automate it as well as scale, but again, don't change your tooling, just change your process. Thomas, I want to ask you as a database expert, you're seeing the rise of vector databases. There's like dozens of them. I mean, you've got Pinecone and Milvus, there's Chroma. What are your thoughts on these? Is it a feature that's ultimately going to be sort of embedded? Where's the value in your mind? So vector databases are a great tool to do similar research. It's great. However, it's a little complicated to put all your data streams into a previous scale. And what's interesting about these LMs, these multi-model LMs is maybe the future is to transcribe in high fidelity all these images, all this logging data, all these audio videos into text and index that text as search, semantic search. So, I think there's a place for vector databases and then you integrate them with the LM, but I've seen a lot of problems with hallucination even when you use it, the cost, the complexity. I think with high fidelity descriptions, maybe that's going to transcend some aspect of vector databases. We're taking that approach where we believe that the language of choice is language and the interoperability. So some say a picture's worth a thousand words. Well, what if you convert all that to language, index it and analyze it as such? That way you can now ask questions not just of the moment is summarized, but historical in the past and get some type of analysis on it that maybe the summarization is good enough let alone the hallucination problem. So is the future or maybe it's here now that you can interact with chaos search through natural language? Is that how you do it today? Yeah, so it's funny. If you're, say, a Kibana expert or a Tableau expert, looker, okay, you understand how to analyze data, but a lot of people don't know those tools let alone the search or SQL APIs and they may be a security expert, but not the tool. And so with our service, we have a tab on our console that you just log in, select the data streams you want to analyze and you can say, I'm a security analyst. How would I analyze my cloud shell data over the last five years? And it'll give you a description and you'll say, well, can you detect fail logins over that timeframe? And it will run the query for you. You didn't have to know how to do it. It'll bring up Kibana dashboard to analyze it. And so to me, it really opens up data democratization for everybody. So the idea that you can have a conversation with your data and not know these complicated tools is really powerful. This is changing the world. I mean, it's going to be virtually across every software, even personal productivity software. I sit there and do trial and error just to get my Excel chart right.  How do I do that again? I got to Google it and it takes a half hour to figure out just exactly how to lay it out. So my question is, how do you see the future of data and the role of data in AI and future of data apps? Well, I think AI and data, it's going to rule the world. So more data you have, the more ability to derive insights. And so we've taken a data lake approach that with our data lake DB, we allow you to stream it all in and provide that integration to that AI to ask those questions. And you can imagine, just Google the question, how do I train an LLM? How do I change the LLM with new data? This is really complicated stuff. And we took an approach where use a powerful at scale database integrated with an intelligent reason engine and it really solves problems quickly. And again, it's something that I think we're all feeling when you just play with chat to be for the first time. Explain again how you eliminate or minimize the hallucinations. So we don't ask the LLM for the answer. We ask the LLM for the question. And so we are streaming data into our consistent database that you can do historical search, SQL analytics. And then if you have a question like how many logins failed over the last five years, we prompt engineer with that question to the LLM to craft a intelligent query on our consistent database. And that allows us to remove the hallucination of what it looks correct, but you don't know if it's right. And we see time and time again, people struggling with that hallucination problem, let alone the complexity and cost to build one. So wait, so you're kind of gaming the system to get the right question so that you get the right answer or you get a non hallucinated answer. That's right. And the LLMs are brilliant. They're highly intelligent in what they can do, but this proximity, this vector or LLM proximity is the problem. That's why it hallucinates.  You do a search query a hundred times, it's the same answer. You do the same with an LLM or a vector search database with LLM. You're playing with maybe 50% sometimes. And then as data is changing, that gets more problematic. One event, you could have catastrophic forgetfulness or overfitting. These are all problems that we took an approach where let's just bring these two best of breed technology together in one solution. Are customers asking you, what are they asking you about AI? How are they thinking about it? There's a lot of experimentation going on. There's actually quite a bit of spending going on, which is kind of interesting. It's sort of stealing from some other areas. What are they asking you about their biggest challenges? Well, one is cost and two is the complexity. So I would say what we're bringing to the table is you can immediately drive value with chaos and AI with these LLMs, meaning that you don't have to spin up tons of engineering resources to build your pipeline, to train your data, and don't have to find all these GPUs to answer the question. You really only have to worry about at scale data with this intelligence AI. So what I'm finding is people are playing because they can't bring it to market. They can't productize this technology because it's fragile. What we've done, and we came out this summer with our AI assistant, it's available, it's real. It works in perfect synchronization with the LLM because it's not being asked to answer the question. Thomas, thanks so much for coming in. Appreciate your time. Appreciate you participating in SuperCloud 4. Thank you. All right, keep it right there. Dave Vellante, John Furrier and Rob Streche. We're in studio. We're bringing in conversations like this all day, live and on demand. You're watching SuperCloud 4. Be right back.


Our next talk will be about Identity-Based Encryption, Resilient to Continual Auxiliary Leakage, by Chi Hon Yen, Sherman Chao, Ye Zhang and Xu Mingyu, and Chi will give the talk. Resilient to Continual Auxiliary Leakage, and this is a joint work with Sherman Chao, who is also here today, and Ye Zhang and Xu Mingyu. In this presentation, I'll firstly talk about the problem that we tackled. And then, before we present our actual construction, we start with the Identity-Based Encryption with Auxiliary Inputs, using our techniques. And then finally, we will go to the Continual Auxiliary Leakage model. Firstly, the central notion of modern cryptography relies on the secrecy of the secret key. However, in practice, this paradigm is subject to some threats of the side-channel attack like the power analysis. Because this attack will try to leak the secret keys, so it breaks the notion of securities in most critical systems. So leakage-resilient cryptography was introduced to provide some formal guarantees, even when the secret key leaks. And in this talk, we only consider about the memory leakage. So in these models, the adversary is allowed to specify an efficiently computable leakage function F such that the adversary can obtain the output of F applied to the secrets. So it aims to model all the possible leakage in practice, including those side-channel attacks. So a major open problem in this field was discussed in here, Eurocrip, a few years ago, and they state that it wants to allow for continuous unbound leakage and without additionally restricting its type. So there are two different main categories here, and there are lots of words to follow on in these few years. And there is a line of research on the second, which is the second restriction, which is on the types of leakage that are allowed. And firstly, the model will restrict the output of the leakage function with less than L bits, where L is usually strictly smaller than the size of the secret key. So the leakage function cannot directly leak the secret key. And then later on, there is an improvement to the leakage function that only allows to lower the entropy of the secret key by less than L bits. So to sum up, L is considered as a fraction of the key, either in terms of bit size or the entropy. And the next, we have the bind to retrieve model proposed in 2010. They allow the leakage is still in L, but L is now considered as the system parameters. So the size of the secret key will increase with L. However, L does not affect the property size, the communication, and the computation complexity. And in these models, we hope that the attack is detected and stopped before leaking L bits. But this may not be good enough because it's quite difficult to stop those leakage after obtaining L bits. So therefore, the auxiliary input model was proposed in 2010. In this model, we consider any leakage function F that no polynomial time adversary can invert. For example, we can consider a one-way permutation is a kind of leakage function that allowed in these auxiliary inputs. In one-way permutation, no polynomial time adversary can invert the permutation. But this one-way permutation is not allowed in the previous relative leakage model because they will leak the whole secret key information theoretically. And of course, we have the first property key encryption scheme with auxiliary input proposed in 2010. And both this auxiliary input model and as well as the relative leakage model, they are all bound to leakage throughout the entire lifetime of the secret key. So this only answers half of the open cache questions that I've taught before. And the second half of the open question that we taught before is about continual leakage. It means that the user will continually update or refresh his secret key and the adversary is allowed to obtain leakage in each period of time. And leakage between updates are still bounded. But overall, in the long run, the number of leakage is unbounded. So there are quite a lot of different scheme work in this model in signature identification, property encryption, and so on. And in this paper, we will consider the identity base encryption with auxiliary inputs. This is because IBE has found many applications like anonymous IBE, chosen cyber test security of property key encryption from IBE, and so on. And IBE with auxiliary input model provide the leakage resilient with the composition of those ID base systems. As long as those systems are secure in the standard no leakage model, this is because we can always model those other ID base scheme as some sort of leakage of the secret key. So another advantage of auxiliary input model in IBE is that it can give a clean definition, which means that we are free from numeric bonds, such as the number of bits that can be leaked from the master secret key. And compared with the current papers with continued leakage resilient IBE, the current papers only model the IBE that consider leakage of the current secret key for a given time only. So in other words, after a user has compute a new secret key for the next time period, the leakage from the key update query is the last chance for the adversary to get it. So it implies that all secret key should be securely erased. However, with those frequent secure evasion, it is less disastrous to have the memory leakage. And this greatly diminishes the benefit offered from the formal leakage resilient guarantees. And therefore, in this paper, we want to tackle the problem of allowing for continuous unbound leakage without additionally restricting the type of leakage we have. And there are quite a few papers that work in either PKE or IBE, but they cannot tackle this problem completely. And our contribution is to, firstly, is proposed such a continued auxiliary leakage model. And we think we provide a minimal restriction is that no polynomial time algorithm can use the leaked information to output a valid ID base secret key. And in this model, the adversary can leak from all refreshed master secret keys as well as the ID base secret keys. So this model is a cleanup security model because there is no version numbers of the secret key compared with the previous models. And it may be the ultimate model for IBE. Before we are going to this continue auxiliary leakage model, we first give the first IBE that is secure in the presence of auxiliary input only. So we provide the adaptive security in the standard model. Our scheme is based on static assumptions with a moderate cost in terms of surface size and computation complexity. And these nice features are inherited from some previous schemes. And like those previous schemes in auxiliary input model, we are based on this GL theorem. And firstly, this original GL theorem is defined over the GF2 field. Is that given some uninvertible function h and the hash of e and a uniform random number y, the inner product in GF2 is a pseudo random number. And the modified GL theorem is that if one can distinguish the inner product from the uniform distribution, then we can find a PPT algorithm that can inverse the function h. So we want to build a auxiliary input secure IBE. So firstly, we start with an auxiliary input secure PKE. So in the PKE, we have a lambda bit number used as the secret key of this PKE. And in this model, we allow leaking an invertible function of the secret keys. And in this scheme, the inner product of the secret key and the randomness of the cipher test will hide the message based on the modified GL theorem. If there is a distinguish that can distinguish between two messages, then it means that there exists an inverter that can invert the function in time polynomial time. However, it is a contradiction to the definition because the leakage function should be uninvertible. However, we cannot directly apply this PKE and turn this into IBE because ID base secret key has certain structure. And firstly, it is not a resources random number bit number. And secondly, the secret random factors hidden in the ID base secret key are not chosen in the domain. If it's just chosen from this model domain, then it will lead to a brute force attack. And next, we try to add the leakage resilient IBE with the auxiliary input secure PKE. But this is not straightforward as well because in the leakage resilient IBE, we will leak the semi-functional keys in the simulation. So semi-functional key is produced from a real key by M binding factors from set P, where P is of size 2 to the lambda. So since P is quite large, so by the modified GL theorem, the inverter is inefficient. And therefore, we cannot give the correct security proof. And in our paper, we give some countermeasures for those problems. And look, since these countermeasures only appears in the security proof, but not in the real scheme. And then we will talk about the security model. So the security model is quite similar to the normal adaptive ID secure model for chosen plain test attack for IBE. The difference is that we have an additional leakage model, which takes an input of a function F in the family capital F. And it will return the function applied on the master secret key as well as the ID-based secret key. And we do not allow any leakage oracle after the challenge phase. And we define the family of leakage. Capital F is that given the master proper key, the challenge ID, the leakage output, and a set of secret key leads from the key expression oracle, then no PPT algorithm can output the secret key of the challenge ID. And what's the difference between our model and the length bond leakage model for IBE? Is that we combine two separate leakage oracles from the length bond leakage model previously. We allow leakage from the master secret key as well as the ID-based secret key at the same time. And we model it in this way because they may share the same randomness. And moreover, we do not need to store the amount of leakage for master secret key and the ID-based secret key. And so we don't need a set of handles of key as in previous papers. So this is the roadmap of our construction. And we construct our scheme following some previous works. And firstly, it follows the LW-adaptive ID IBE. They use the dual system encryption technique for the security proof, which instantiate the BB-IBE in composite order group. And in that paper, the dual system is used for the adaptive ID security. And then we also try to use the CDRW-IBE. In that paper, the single user secret key leakage is done through a single tag. And later on, the LRW-IBE, they use a multiple test for a multiple leakage. And the ID-based keys for undetermined ID will be referred to the master secret key. And we will try to use those techniques in this paper to construct our scheme. We use the multi-placing idea as user key level. And then we try to do it again as the master key level. But how to get our leakage resilient and adaptive ID security? The answer is that we know how to fake everything by the dual system method, because we can use the dual system to construct some fake secret keys and we can leak them. But we should be careful that such a leaking should not be spoiled the faking. This is because the correlation between the semi-functional objects is information theoretically hidden by the definition of our leakage, because the leakage per key is suitably bounded. We have some further design constraint. Firstly, by the modified GL theorem, we should only use a small binding factor in the semi-functional keys. And secondly, we are relying on the information theoretic argument where the key is extracted. So we extend our one equation to a non-argument in the LW-IBE to 3M equation, 3M plus 2 unknown in our security proof. When the secret key is leaked, an invertible function of the key can be created from an invertible function of those binding factors. And the inner product equal to 0 in the modified GL theorem will imply the exponent in the subgroup is equal to 0. So it can be used to simulate the semi-functional keys. And our next contribution in this paper is to construct the first hierarchical IBE with auxiliary inputs, and of course, the first IBE in continue auxiliary leakage model. And we extend our basic scheme to support the leakage of randomness during setup. We use in that scheme, we use a lattice-based assumption in our pairing-based construction. And then we will propose the continue auxiliary leakage model. Firstly, the setup is split into the common reference string generation and the master key generation algorithm. Compared with the model I taught previously, we add two additional oracle, which is called the update master key and the update user secret key. These two models are provided to the adversary as well in the security game. And we change the definition of the leakage function family as well. So in the original and the model, the leakage function only applies on the master secret key as well as the IBE secret key. And now in our continue auxiliary leakage model, we allow the leakage of a list of master secret key and a list of IBE secret key as well. And those lists are containing those keys that are ever produced. And additionally, our model can be modified to keep leakage during setup as well. From the construction, this is relatively similar to our basic model without continuous leakage. So we just add the re-randomized factor component in the cipher test as well as the secret keys. And if we want to extend our scheme to a hierarchical IBE, we just change the structure of the components relates to the identity. And finally, we give our extension to support the leakage during setup phase. So in this setup, we have a matrix of v used as a randomness and some slanted bits alpha j also as the randomness. And we define the q as a product of those matrix to power the alpha. So the master secret key is a pairing of these numbers. And we have an anchor piece of them in the scheme. And by this matrix type construction, we need to use the LaTeC base assumption. And as a result, we need that assumption in our security proof. And of course, our public parameters will be now in size O lambda, where lambda is the security parameters. And finally, so this is the end of my presentation. And we would like to thank Alfred and Jonathan for the helpful comments. And this is the summary of our paper. So we can see that a scheme provides more tolerance to the leakage. And of course, the complexity is slightly more complex than the previous paper in TCC 2011. But we think it's still not too complex. And we think it allows more leakage. And that's the end of our presentation. And questions are welcomed.


Hello and welcome back. Like I've been saying for a while now They're going to outlaw political opposition to the Democrat Party I present to you a former Naval Chief Petty Officer turned MSNBC Intelligence Analysts and Ukrainian war larper Malcolm Nance. Ukraine's unique territory There'll be three Stand by Wait for one more. They're fired in 32nd. They fire them in 32nd interval Smoke Stand by three cruise missile caliber Stand by. We'll dive right into this clip after I quickly tell you about this free coin offer from protectosecond.com Shipping and handling not included. The recent shootings, especially those in schools is bringing about stricter gun reform bills Congress has just passed the most significant gun reform bill we've seen in decades But as we all know bad guys will always have access to guns Unfortunately for law-abiding citizens the Second Amendment has never been more at risk of being infringed than this year a new site is showing Their support for the Second Amendment to create change and you can get in on it, too Get your free Second Amendment coin today by going to www.supportthesecond.com. That's www.supportthesecond.com. Support gun owners, gun rights, and the Second Amendment As a terrorism expert, I usually remove phrases like Muslim or Christian or Catholic or white But I'm talking about extremists. Most extremists have an ideology that they're following But here in the United States to characterize that to understand just what kind of terrorism we might be dealing with You have to actually label it according to what as white extremism Because we have 30% of the population of the United States who no longer believe in the Democratic norms that were established at the founding of the country. Let's just be honest about that 30% why that's about the percentage of white males in this country according to the totally not racist or insane Malcolm Nance Nearly all white men in America are white extremists and are a domestic threat white men in America no longer believe in Democratic norms, of course He is a shill for the party that currently promotes Democratic norms such as throwing out the electoral college Demanding that people in the country illegally be allowed to vote revoking the Second Amendment tearing down the monuments to our founding fathers opening the borders Declaring the American flag a symbol of hate Expanding or abolishing the Supreme Court injecting far-left Marxist ideology into every institution Limiting free speech specifically of their critics and ideological opponents It may be worst of all because they're supposed to be the guardians that prevent these things from happening using the fourth estate free press As a tool to keep democratic power permanent and propagandize all who oppose them case and point Here's what don limit has to say Republicans are doing something that is very Dangerous to our society and we have to acknowledge that we have to acknowledge that as Americans We must acknowledge that as journalists Because if we don't we're not doing our job The job of journalists is to hold power to account and currently that power is democrat party power Who control both the white house and congress now watch laman's mental gymnastics as he rationalizes being a party Propagandist we cannot sit here and pretend like well, you republicans. It's a cut them a break Let's we want to hear what they have a lot to answer for in this moment You have the inmates running the asylum basically you have the extremists because I know they're republicans who are sitting out there now No, it's not what we are you cannot expect list chain your adam kinsinger or or jeff dunkin to become democrats overnight They're not they are republican and you must allow them to be who they are They just happen to be sober and smart It's so obvious Everyone must see the game here the media is an obvious democrat party tool They have completely abandoned their fourth estate responsibilities So they must justify it to their dwindling viewers and rationalize it to themselves by casting their critics and their political opponents As literally in human monsters that must be stopped American democracy is under direct and immediate attack by people who would see 245 years of democratic governance Destroy it's just a matter of seeing who comes up as the provisional irish republicans In this story and starts carrying out acts of violence in order to affect change So we are well onto our way to a multi-year campaign That we're already two years Into this campaign where we will may have to fight They the they in my title and the they in my who want to kill americans are your neighbors Wow those are some real american democratic norms. Yes. Yes democratic norms do typically involve the media Turning neighbor against neighbor instilling a deeply dividing paranoid fear of being murdered by your white neighbor Don't they this guy never recovered from the intellectual beatdown that he received from Ben Shapiro 40,000 people did not assault the capital. Okay, it's just factually inaccurate There are maybe a thousand people that is not making light of the evil of those people Who all will end up rotting in prison as they should how many people came to that rally? Were all of those people assaulting the capital? Is that your content? They all marched on the capital and then the leading They all marched on the capital. It's just inaccurate Malcolm. Yeah, it isn't no. Yes. No. It is inaccurate Malcolm. Max, Ben Yes, you're not mentioning them Malcolm. You have a terrible habit of conflating people who do actual violent activity with a broader rubric of everyone for example voted for trump which was 75 million people critical race theory essentially argues That racism is baked into all the systems of American society And that any sort of neutral system is in fact a guise for racial power if we agree That history should be taught. Why are you defending critical race theory? Which is not history? Did I not just say a moment ago that I think that term has been hijacked and that's not okay So let's say critical racing. I'm going to go back and repeat that. I didn't say that You are literally defending critical race theory by redefining it as just teaching history Which is a cheap semantic trick and you know it.   They basically just say whatever will incite the most hate of their opponents Mark my words right now They will use all of those things as a pretext to shut down opposition media And if they can pull it off the republican party, just use your imagination on what comes next That's all I have for that one. Thanks for watching. If you enjoyed it Please share it hit that like button subscribe and make sure to vent some frustration by leaving a comment Thanks a lot. Have a great weekend


Hello everyone. Welcome to the session on parallelism in relation operations for parallel databases. Talking about the learning outcome of this session, at the end of this session you will be able to apply few parallelisms on the operations like selection, projection, aggregation operations on any given relations. So, earlier we have seen the inter-operation parallelism few techniques like parallel sort and parallel joint techniques. You can refer my videos for these one. Now today in this session we will be talking about the other relational operators like selection, duplicate elimination, projection, grouping aggregation, where the large number of data is there and we want to do the operations on these, how we can apply the parallelism for this one. Let us see for this. Start with the selection operation. We want to parallelize the selection operation. The selection operation may be select start from some relation, but more number of tuples are there and we want to do that in parallel. We are considering here the relation as employee assuming here that there is an employee relation, we will see that later. Assume that the table is partitioned which is stored in some disks. So, we have n partitions. So, we have n disk that is d0, d1, dn minus 1, these are the disks associated with the processors p0, p1, pn minus 1. So, parallely every processor will work on a particular operation. The operation may be selection or any other operation here. Now employee is partitioned, we are considering here that the partitioning technique which we have applied is the range partitioning technique on this employee relation. Let us move ahead. So, this is the scenario. We have the employee relation here. This relation on the range partitioning technique based on range partitioning technique, we have partitioned based on salary. You can see the salary with 25,000, below 25,000 is in this partition. More than 25,000, less than 30,000, it might be stored in this partition and more than 30,000 salary data is stored in this partition. So, 3 partitions are there which are divided in 3 disks and every partition is associated with 1-1 processor. So, you can say that p0 is associated here, p1 is associated here, p2 is associated here. Now let us see how parallelism we can apply on this one. So, we can recall that there are 3 types of queries based on the data access like in the partitioning also we have seen. One of the type of query is called as a point query. What is point query? The point query is for the example where condition is there and some attribute is assigned value we are checking with equal to. And we are getting few tuples or one of the tuples. So, this is called as point query. Another type is called as range query where based on the range we want to get the data. So, the range may be something greater than equal to value and something less than equal to that value or something between some value 1 and value 2. So, such kind of queries are also there in the selection operation. So, these are called as the range queries. Another type is we want to do the entire scanning of the relation where hold relation we are scanning. Basically in the point queries and range query not necessary that the entire relation is scanned because if the proper partitioning technique we have applied and the proper data we want to read according to the partitioning what we have done then automatically it will go to the particular disk and it will get the value. Whereas in the third scenario we need to do the entire relation scan and we want to apply the parallelism where non non key attributes are involved. For example, you can see that here in the range query I have done the partitioning based on the attribute only what we are searching like attribute name. So, if this is the case then it will go for range query. Other than that if we are we are instead of using that partitioning attribute we are searching for another attribute then we can say that that is a non key attribute. So, in this one we have to go for complete scanning of the relation. So, these are the three types of queries. So, based on this let us see how we can parallelize these one. So, the example is something like this that select some list of attributes from the table where some condition is theta. So, what is this theta condition? Theta condition is applied for the where clause. So, the first condition you you can see that is on the point query. So, what is the theta condition that is ai is equal to some value attribute ai is equal to some value is there. For example, here you can see that our employee relation is there. So, select star from employee where salary is equal to some 20,000. So, if this is the case the employee relation may be taken as named as EMP here. So, so the salary equal to 20,000 and what is our partitioning attribute? The partitioning attribute is also salary we have partitioned our data based on salary itself. So, what it will do? It will get it will check that in which range it is coming. So, in first range it is going for. So, automatically it will go to the first range data first redisk it will get the value immediately. So, here in the first disk automatically it will get the value that is salary less than 20,000 automatically. So, in this one as we have partition no need to check in this disk automatically the time required is less because it is only checking the disk where the data is present. The second case that is of the range query. So, range query you can see the example that this is the attribute AI with some lower value and the upper value we are checking and say the example here is it is select star from EMP employee table is taken here as EMP where salary is between 25,000 to 30,000. So, this is what this is the second range of disk you can say. So, how it is checking it will directly go to the disk and it will get the value because the range of this disk is more than 25,000 and less than this 30,000. So, automatically it will get the data from this. So, all this data will come as a result here. So, as partition is applied parallelism has worked unnecessary checking of disk D0 and D2 has not done only one disk where the data is available has taken. The third case very important where a complete scan is required. So, you can see here that what we are searching for we are searching for employee name with Sawyer. Now, what is the partitioning attribute we have taken we have partitioned our data by salary. So, whenever I am applying this query we have to go for searching all the relation entire relation based on this one. So, this is a scenario we have seen that this is the partitioning. You can pause this video and think about how that query will execute parallelly in this. How it is working? See the parallel scanning of the relations in all the partitions will work why because the attributes are not partitioned according to the name. So, it will search employee name Sawyer here also, parallelly it will search here also and finally it will search here also and it will get the data from this one. But all the relation a complete entire relation is scanned here because whatever the partitions we have done those are not done by the attribute what we have searched for. The attribute is employee name, but the partition is on salary. Therefore, parallelly all the processors all these three days are searched by the processors and finally it will get the data. So, here is what the parallelism is applied. Talking about the next operation like duplicate elimination a very simple thing is there if we want to eliminate duplication what we have to do is the first thing is we have to do the parallel sorting. So, sort the relation entire relation using the parallel sorting technique we have seen earlier the parallel sorting techniques like range partitioning sort and external merge sort. So, we can apply any of the parallel sort. So, parallelly entire relation is sorted and then the duplicates are eliminated as soon as they found during the sorting techniques. So, simple is there the parallelism may be done by the sorting may be done by the range one or you can apply the hash partitioning also and duplicate elimination is done locally at each processor because every processor is taking part in the sorting techniques. So, automatically locally at every session every partition it is removing the duplications. Talking about the next operation say projection in the projection what happens the two things are the projection without duplicate elimination or duplicate elimination is required. If project without duplicate elimination is there it can be performed like the normal selection operation and if duplicate elimination is required then we can apply the duplicate elimination parallelism where sorting is available. Now, talking about the grouping and aggregation what will happen that say that we want to group something we want to compute something. So, in that one we want to do the parallel execution also. So, the part partly the computing will done in this let us see the scenario first I will come to this one. So, what we are doing we are say that we want to do the sum operation. So, if this is the data what we have this is a relation. So, the first relation is partitioned into some disks. So, these are the these many disks are there. So, that relation is partitioned we if we want to calculate sum. So, at locally on every disk sum is calculated. So, this may be some 1 this may be some 2 this may be some 3 some 4 some 5 and finally, all the sums are added ok. So, automatically the parallelism has done on this grouping. So, let us say what the same thing is given here that consider the sum aggregation is there we are performing the aggregation operation on each processor PI on those tuples which are stored on disk DI and every disk DI is containing the partitions. So, the results are again stored as a partial sums. So, every partial sum is finally, taken for the for the final result. So, every processor is doing the local aggregation at their end and finally, those are merged ok. So, like this parallel it is doing. For calculating the cost of parallel evaluation operations what are the things? Basically 2 things are there if no skew is there if no partition skew is there no executions skew is there then what happens it will not require any overhead. So, if no overhead is there then automatically ideally the parallelism is applied as 1 by n if we have n processors ok. But if skew is there if overheads are there if communication problem is there. So, in that case it is take it is not exactly 1 by n it is estimated somewhat more than that ok. How it is calculated? It is calculated by 3 things. What is that one addition of these 3? The first one is the time taken for partitioning second one time taken to collecting the result that is you may call it as a assembling and the now all every processor whichever is taken part in the parallel operation whichever processor is taking maximum time that will be considered because everyone is parallely doing that. So, the maximum of that maximum time of the processor which is taking maximum time that is taken here and then that is added ok. So, if we are adding all these one there we are calculating the cost of parallel evaluation of these operations. These are my references. Thank you.


Along with the ideas of Buddhism and Hinduism, the culture of Indian deities traveled across the whole of Asia. Very interestingly, more than 80% of Japanese gods are originally Indian gods. This should be recognized by Japanese people. The bottom of Japanese culture, Indian culture is very strongly imprinted and developed in the Japanese way. When the Japanese studied Sanskrit in the 8th and 9th centuries, Sanskrit was synonymous with the Siddham script. This was used for writing Sanskrit since the 5th century in India. Thus, Japan and India were very closely connected. If you go to Japanese tomb, you may find Siddhan letters. So perhaps, Japan is a time capsule of Indian culture. Already, India disappeared. In Japan, they also used the Indian letters for writing of sacred syllables or Bidak saras, which have the power of mantras. One of the most revered deities of Japan is Saraswati. There are scores of shrines built to her. In many ways, the original concept of Saraswati and her association with the natural order and good fortune are very well preserved in Japan. She is often visualized as a sacred body of water. The annals of the Todaiji Temple mentioned that the celebration of the worship of Lakshmi and Saraswati was held for the first time in this temple in 722 AD. Ever since, it has been an annual celebration. In 1836, Saito Sachio wrote a guidebook to the Edo area or modern Tokyo. Saraswati was mentioned as the most popular deity here at that time. Worship of Lakshmi was done to ensure the peace of the land, wind and rain in good season, good harvests, the joy of the people and the good of all sentient beings. The great intellectual genius of Japan was Kokai or Kobo Daishi of the 9th century. He learned the Sanskrit script from the Kashmiri Acharya Pragya. Kobo Daishi placed emphasis on a knowledge of Sanskrit. He said that the Japanese have had to rely upon translations to study Buddhism. However, the true words in the original language are exceedingly important. Every word has a profound meaning. A shrine was dedicated to Varuna in the Fudodo Sanctuary in Tokyo in 1836. The temple also has a representation of Kamadeva. He is worshipped by many young couples. There are many temples dedicated to Yama in Japan. In the 7th century, Prince Shotoku created the nation state of Japan after vanquishing his rivals. He took the term Tenno for emperor from Sanskrit texts. Havan or Homa continues in Japan where it is called Goma. Goma in the Shingon and Tendai Buddhist sects of Japan has striking parallels with early Hindu rites of Agni worship. There are also deep similarities with the later Tantric tradition. The Tantric Shingon school of Japan sings Sanskrit hymns. To this day, followers of the sect perform their worship in 1200 temples throughout Japan. A Chinese sutra compares the cosmic force of the mantra with the mighty roar of a rising flood. The mantras were transcribed into ideograms and to conserve their full power they were also written in Siddham. Indian letters to a Japanese are sacred and stand for divine beings. To this day, the Japanese write mantras in Siddham. Every god has his own Bijakshara. Every temple gives much importance to those Bijaksharas because they are equal to God. Mist surrounds the legend of Bodhidharma. Tradition points out that he was a prince of Kanchipuram in South India. The Japanese form of his name is Bodhidharma. It is very close to the Tamil form which is Bodhidharma. Zen was taken to Japan in the 12th century by Isai. Its influence on Japanese art, attitudes and language has been profound. Zen Buddhism became very popular because it says that life is a fleeting phenomenon. And the samurai, when they went to war, they were never sure that they would come back. When we sit down in a Zen monastery, we have to really do very hard penance. And hard work in Japan is perfection. So, the Zen tradition gave them a perfection in work, which became a very important part of the modern development, perfection in everything. Japan is the one country where Buddhism is flourishing in all its facets. Here, technology and transcendence are living together. Besides the Buddha, so many ancient Indian deities and practices are preserved in these temples. And Indian feels quite at home in Japan.


Hello and welcome to the Grand Line Review, your source for everything one piece. And today we have a review of episode 894, He'll Come, The Legend of Ace in the Land of Wano. And what a title that is seemingly out of nowhere. It's a title that I think may initially give a lot of hope to a very, very small group of people who saw the preview last week and became wildly insistent that Ace was still alive and was somehow going to play a major role on Wano. And it's moments like that where as a Manga reader, you just need to, you know, stay quiet and let them learn from their mistakes. So as pretty bluntly stated by Luffy during this episode, Ace is dead. And that's not to undersell that moment at all. I know he Tetsu said Luffy delivered it a bit too casually in terms of the timing relating to Tama, but that one line was just superbly performed. It certainly didn't feel casual to me at all. In fact, it was more like Luffy mustering the strength to effectively get the words out. Mayumi Tanaka's delivery was spot on and it's always fantastic to listen to her completely nail Luffy's more serious side because I'm so conditioned to thinking of her voice as a goofy rubber boy. And it's always a nice surprise when she delves into her range to make Luffy a believable authority. In addition to that, the sound was just as integral in giving Luffy's words that much needed impact as it was reduced to near silence with only birds chirping in the background, which is an interesting choice because it's not a sound that I would naturally gravitate towards enhancing heavy words, but it works really well. And I think that's because the birds provide a realistic feel to the atmosphere. You know, we don't have any crazy creatures like giant baboons making noise or over the top stylized music or even music at all in this case, there's just birds. The sound that we are all very, very familiar with and it creates that sense of realism that really makes Luffy's line hit home. Now, in terms of Ace, while it was great to see him, I was honestly hoping for a bit more from the anime here. Having seen what they've done with Zora's quote unquote story, I feel like there was a missed opportunity here with Ace. I mean, they certainly expanded upon the manga material which was about four panels, only two of which actually featured Ace. And easily the best thing they did throw in was that little section of the montage where Tama is teaching Ace how to weave hats, which is a connection that is really nice to highlight because it brings the whole Ace and Oz Junior mini flashback to a close. And as it turns out, we can essentially thank Tama for providing Oz Junior with a hat.  However, I do think there was a missed opportunity here in terms of perhaps exploring the spade pirates a bit more. Because remember, this is before Ace became affiliated with Whitebeard. So he was still the captain of his own crew and that's something that has never really had any light shone upon it, apart from the Ace novels. Although I can understand the creative drive behind not going into them too much because it may detract from focusing on the bond that developed between Ace and Tama. But that said, I still think it could have been done effectively. And what I'm thinking of is in that montage of still images, for example, the one of Ace and Tama eating dango, maybe the spade pirates could have been in the background also eating or arguing or helping the village or something that develops them as a crew even slightly. Because here's the thing actually, in this anime adaptation, he Tetsu only ever says Ace. You know, he has lines like Ace came to one or Ace untied himself. Whereas in the manga, he Tetsu said that the pirates came to one or the pirates untied themselves and so on. Which is a small but interesting change. One that I'm not so much a fan of, but I get it. You want to focus on Ace, but at the same time I can't help but think there is a chance for that connective tissue that I just mentioned I love so much within the One Piece world by expanding upon the role of the spade pirates ever ever so slightly rather than actively diminishing their presence through dialogue choices. But hey, that is a minor complaint. So let's move on to something else I am very much enjoying and that is Tama. A lot of people pointed out to me last week that Tama is voiced by Megumi Han, which is fantastic, because if we have any Hunter Hunter fans in the house, well, she's the same actor who portrayed Gorn Freaks in the 2011 anime. And I did not notice at all, which I suppose is the ultimate credit to her performance in One Piece because I've watched the 2011 Hunter Hunter series many times over now. And not only that, but Killua's voice actor is also set to make an appearance as a certain one or character, which is incredibly exciting. But in general, I'm finding that I'm having a lot of fun with Tama's adaptation. In fact, my favorite moment this week was when she announced that she was going to become a Bowichin Kunoichi. And then she did her Bowichin movements to go along with it. I'm a big fan of that sort of energy being injected into her character, as well as the bits of extra emotion, which brings us to the rice cooking scene. This is probably the biggest difference between the anime and the manga this week, and how it originally appeared was a very casual affair. We see one panel of Tama cooking, looks pretty normal, nothing special. And then she immediately serves the rice to Luffy. And the intent behind this was to make the act of feeding Luffy the rice so incredibly mundane that the impact of learning how special this rice was really amped up when we did find out. Now the anime chose to go in a different direction with this, really drawing out the cooking process. And it was a very nice scene actually, very well supported by a minimalist, but inviting and homely soundtrack, almost as if it were a child waiting for their mother's home cooking. The bit that really gets me though is when Tama sees the cooked rice for the first time and then she has that look of utter amazement and bliss on her face. So knowing what was going to happen that was ever so slightly heartbreaking for me, but I really appreciate what the anime did there. And you know what? It also made me crave some plain white rice actually. Like you know, one of the most flavorless foods possible, but watching it be cooked and seeing Tama's reaction. Look, I'm going to be honest. Immediately after the episode was over, I made a bowl of plain white rice just because. So for a piece of media to have that sort of effect on me is pretty awesome. However, I can't help but wonder if the increased focus on the rice does minimize the effect of the ultimate reveal by flagging its importance to Tama a bit too early. But at the same time, I have to admit that I am way, way too deep into this whole storytelling business given that I work in theater. So to the average viewer, there may not be any significant difference. And who knows, maybe extending the cooking scene even made the overall effect that much more profound. And that is a question I will leave open to all of you anime only watchers. Another thing that really struck me about this episode though were the interesting little risks that the one or error is taking. I'm not talking about anything huge, but there were flares of creativity that pop up here and there that as a long time one piece viewer, I just wasn't expecting. And a good example of what I mean would be where we came back from the eye catches.  Artistically, I think I kind of get it because it's perhaps representing movie's memory and the flickers are there to imply the momentary shock that he's feeling with ace having been brought up. And I'm not sure how much I like that specific device so much, but I am really enjoying these odd little creative decisions that are being implemented because it's about time that we started to push the boundaries of what the one piece anime is and what it can bring to the table. So if they continue pushing for small things like this and not fall into an established method of storytelling, then I do have some decent hope that the anime will not easily fall into stagnation again. One creative bit of flare that I thought worked really well though was the transition from ace and Tama in the flashback to the Monday where Luffy and Kitetsu were standing in their places. It's a very simple transition and something that's been done to death in almost all forms of visual media, but for good reason because it's incredibly effective. It really gave me a sense of Luffy being caught up in ace's legacy and feeling that drive to carry it forward. And that's sort of enhancement I really look towards an anime being able to provide. And another one of these decisions can be seen in the Drake segment where his ex appears on screen which gave me a bit of a laugh actually. Highly inappropriate for the context of his situation being a terror to the people of Amigasa village, but the ex popping up just gave me these supervillain vibes.  The action wasn't anything super special but Drake is one of the more intriguing members of the worst generation to me. So I doubt I'll ever be complaining with more of him thrown in. Then of course, we also have Hawkins appearing in this episode, although not in the way I was expecting. So his scene was changed, not radically at all but in the manga he gets a call out about losing contact with his dude bras on Curry Beach. And then there is this wonderful panel of him stating not to tell Kaido that he will go and sort it out. Whereas in the anime he gets the same call but he seems like he's already on the way to the beach or somewhere else. And I don't know, that might be a knock on effect of introducing him earlier than intended with the last episode but I definitely prefer the way the manga did it. And I'm not sure why the anime is making these small changes to Hawkins. I mean, perhaps they plan to use him for some extended filler sequences that require him to be galloping off somewhere at this very moment. We'll see I guess. But he also doesn't get his little 1-0 introduction card yet which he did at this point in the manga. And look, I don't know, there seems to be some sort of plan to use Hawkins at play. So whilst in the past I would have complained incessantly I'm going to do something unusual and decide to trust the direction for now. I just hope that it pays off because at the moment I feel like we sacrificed a really beautiful manga panel for something much less effective. Although the saddest part about this week's episode is the preview, which reminded me that we've got some one piece stampede tie-in episodes coming up. And to me, this is awful timing. Not for the film's release of course but because we were just getting into 1-0 and I'm loving just about everything I'm seeing so far. So much so that I really do not want to leave, especially for movie tie-in stuff which let's be real is historically awful. I think the best we can hope for out of the next two episodes are a glimpse of Douglas Bullet to really hype up stampede. But even then it's so difficult to be excited for that film because I have no idea if it's even coming to Australia. And even if it does, we could be looking at a wait of several more months. So with all of that in mind I would personally rather stay in 1-0 but I understand that the one piece anime is not made for me which is something I've known for an awfully long time now and that it's catered towards a Japanese audience who seem to really appreciate this kind of stuff. But the really sad thing is that the stampede tie-in episodes look like they've still been done in the standard one piece style of animation that we are used to. So going from these episodes and then stepping back into that it's a big ask for a one piece snob like me. So to be perfectly honest, I'm not entirely sure if I'll be reviewing the next two episodes until 1-0 comes back but as usual I'll kind of leave that up to you guys. If it's something you would really like to see then do let me know in the comments and yeah, I'll probably get onto them. If not, just say no and message received. But that pretty much does it for episode 894. If you enjoyed this video and the content this channel produced in general then please do consider donating to the Grand Line Review Patreon because the support of all of your amazing people is what continues to make this channel possible. And if you'd like to see more videos like this but apply to other anime manga series then please do check out my second channel New World Review for all of your wider needs. And if you'd like to join the fun at any time then please do head over to my Discord server where a wide array of shenanigan retakes place on a daily basis. And finally please do comment with your thoughts on the episode. This has been the Grand Line Review and I'll see you next time.


All right. Good morning, folks. Welcome to our presentation here. Let's get started. We'll talk about multi-tenant traffic visibility and why is it important in an open-stack environment, primarily for enterprises as customers move into the enterprise clouds, and also for the service providers and carriers, especially in NFVI architecture. I am Sesh, one of the product managers on the portfolio. Why traffic visibility? Why is it important? Primarily because network traffic doesn't lie.  So we need that. Thank you, sir, for cheering me on. And modern data center and security tools rely on this traffic for analysis, right? I mean, logs are at point in time, but this gives you a lot more. You can peek into the traffic for inspection. And mobile operators, especially as they serve as subscribers, a lot of these subscribers need this traffic for analysis or providing better services for their customers, so they could also use this traffic for analysis. And finally, I mean, we don't want to say anything negative about log data. You still need that for alarms and events and so on, but we believe traffic augmented with log data will give you your entire picture into your network. And why do you need pervasive monitoring, right? And here are some use cases, increasing threats, right? You cannot secure what you cannot see. So logs may give you that data a little bit, but not the entire packet information that you want to capture and analyze. Next use case, you've got your distributed applications that have east-west traffic patterns, where these VMs are migrating across your entire infrastructure. How do you, as they propagate through this infrastructure, how do you visualize them, or how do you inspect that traffic? All these new blind spots, VXLAN, anybody using VXLAN here or looking at overlay technologies? There you go, how do you get your tools to be offloaded? They don't have to do the inspection. How can we help you with that as well? So these blind spots, they're optimized. They're optimized because those overlay technologies help you, but we also want to make sure the investments that you have preserved in your tools can still be applied for those new traffic patterns. And then finally, NFE. We are here at OpenStack Summit. There's a lot of NFE discussion. There's a lot of overlay discussions with SDN and OpenStack and KVM. And as you consider migrating to this infrastructure, you want to maintain your visibility across that spectrum as well. So again, the key drivers, security, application performance, as well as NFE monitoring for subscribers. These are the core tenants, what we would say, need visibility into that traffic. All right, so let's, if I can get this. OK, who deploys Gigamon? How many have you heard of Gigamon? All right, well, that's cool. That's great. So again, here's, you know, we are in a lot of these institutions that I won't read through them, but we are in the Fortune 100. All kinds of customer base here with all the verticals. So we've been deployed there. But these customers are now migrating over into the virtual infrastructure. Both VMWare environments, OpenStack environments and others, and they need this continuous visibility as they migrate to the next business infrastructure. But we're not doing this by ourselves, right? We are helping the tools optimize with the traffic that we provide them. We have our friends from Viabi here who also, and I'll walk through a demo with them, that we have integrated with their ecosystem as well to follow these tools. We also are working with RSA, the security analytics company, to get this traffic as well. So rich set of these tools. If you are a customer who recognize these tools, obviously you would need these packets to analyze them. Anybody recognize these tools in the environments that you have? That's cool. So finally, what are the challenges? Why did we get into this place? Why are we in the OpenStack Summit talking about this? Let's click through some use cases. If you have a typical server, your tier one server that is physical, you could span the traffic or tap the traffic and then inspect it. But if you virtualize that same server now with high consolidation ratios going anywhere from 10 to 100 per host, you get this kind of deployment where this east-west traffic is a blind spot. Nobody's inspecting that. How do you get that traffic to be to your tools for analysis? So this is where Gigamon fits in to solve that specific challenge. Again, security, application, and VNF monitoring, all of them who deploy this OpenStack infrastructure need that visibility. So here is how we have some of the customers have tried to address it. They might put these virtual probes on each of your hypervisors. Now, that's a good solution temporarily, but it takes up space on the host. It eats into your consolidation ratios. And if you want to put in the new tool to monitor that, that becomes a challenge. So a better approach is actually get one probe, or one virtual tap, if you will, in the hypervisor and get the traffic for all the tools that need it. So you only grab the traffic once from the virtual infrastructure and deliver to all your monitoring tools that need it. The other advantage of this is, as you scale to new infrastructures or new tools that need this, you just plug into the Gigamon infrastructure and they have that traffic readily available. So you don't have to go into the hypervisor and do anything there. So if you take it about a 1,000 foot level or 10,000 foot level, here's how it would look in a typical deployment. You deploy a gig of UVM. We're calling this a virtual tap, if you will, a Gigamon virtual machine that gets the traffic from the hypervisor and then routes it over your production network into your visibility fabric that can provide to the tools. So you're using your own production infrastructure for your traffic analysis as well. Now, some of the customers that we have spoken to also, let me build one more, are also physical. I mean, the database servers, for example, haven't migrated over to the virtual infrastructure. So it's not just within the hypervisor, but in some cases it could be a hypervisor to a database server that needs this visibility. Now, this is not a all virtual solution, even though you could deploy it. It is a combination of intelligence at the edge, get the traffic from the virtual hypervisor, and then also some traffic optimization on our hardware appliances so your tools get better visualization of the traffic. That's all great. So we've had the solution with VMware, ESX, NSX for a while, but over the last one year, we've been looking at OpenStack. As OpenStack has taken on, it's become pretty prevalent in the enterprise and service providers. How do you get this visibility? Some of the challenges with OpenStack itself is it's tenant-friendly, very tenant-friendly, in which case you as a tenant cannot go to your operator and say, give me port mirroring. Give me that traffic, because then that becomes a barrier to entry, in which case how do you get visibility? That's what we're trying to solve here. We looked at two use cases. We proposed TAP as a service. Anil here, our distinguished engineer from Gigamon. Wave, Anil. So he has a speech today. He has a discussion with a couple of the vendors today, later in the evening, to talk about TAP as a service as well. But while that's going on through the OpenStack community, we also were looking at what are the other options available. So we came up with an alternate approach called intelligence of the edge visibility, where you get the traffic right in the VM itself and then analyze it, while we still are investing in TAP as a service for it to be adopted in the community. Once we have that, you need an orchestration. How do you integrate with OpenStack itself? And that's where our centralized orchestration layer will integrate with OpenStack to configure these traffic policies. And then finally, that traffic, once it is captured from the virtual infrastructure, can be optimized by our visibility fabric nodes before delivering it to the tools. So it's an intelligence of the edge layer with a traffic optimization layer before the tool is inspected. So a holistic solution for scale, as well as reach. So here is how it works. I mean, obviously this is a TAP as a service. Again, Anil will talk about this today, the plan is to get into OpenStack as part of the end release, and hopefully you guys can listen into that speech. Quick plug for that, it's at 4.10 today. So I know a lot of people have stopped by our booth and asked about that, or you can talk to Anil after the session and talk to him as well. But while that is going on, here is what we are looking at, the intelligence of the edge and core. So here's a solution. It's a light footprint agent that sits in the target virtual machine. It could be packaged into your VM itself as part of a glance repository image, and every VM that gets spun up can have this agent in. It can do filtering and sampling as well. So you got the traffic at the edge. You can aggregate it into our virtual gigaview VM, which then can do the forwarding of the traffic to the physical infrastructure where the optimizations happen. So you can look for optimized traffic with SSL decryption. For example, GTP correlation for subscriber awareness. You can generate metadata out of that. You can optimize the traffic before the tools inspect it. So the tools are not burdened with all these optimizations. They're doing their best job and we can help them with that. So that's our solution today. It's available. It's GA right now. I'll walk through a quick demo of that as well. But before that, as we talked about it, this is a sense feeding the tools. The tools have to be on board on this. So we have two use cases. Sorry. Two use cases. One with RSA, who are a security analytics company, a vendor, obviously. They take this traffic and feed it in the security analytics infrastructure for securing the OpenStack private clouds. That's one use case. But the second important use case, probably most of you are also familiar, is the NFVI space, where you have a bunch of virtual network functions. Now how many of you are in the VNFs going into VNF environments? So this VNFs, where you actually have to need this monitoring solutions, you deploy this agent, aggregate the virtual traffic, and then deliver it to the tools. So that's where you get your visibility into your VNF. So as your P gateways or S gateways scale out, you can also deploy this as well. So a quick version. This is your before view, where as you virtualize all your VNF functions, like IMS or EPC, got all these blind spots now that you cannot inspect. But the new normal, after deploying the Gigamon solution, is now you got it covered. So you don't have any blind spots, and that'll help you get your full inspection with subscriber awareness if needed. So the before and the after picture of how the visibility can help your infrastructure. So the other advantage, obviously, also is as you roll out new services, you can use this infrastructure to inspect it before providing subscriber services as well. So I'm gonna walk through a demo here with our friends, Viavi, they're here. So if you wanna stop by and ask them how this integration work, we'll do that. So give me one second here. So here's a case where we have deployed a SIP application, a voice application, and put an agent in there to get the voice traffic over, aggregated it at the Gigaview VM virtual layer, orchestrated by the fabric manager for all these virtual policies, and then feeding it to the tools. That's the Viavi monitoring suite that looks at the traffic. All of you recognize this picture, this is Liberty with the SIP server and the SIP client, talking to the service, with the virtual traffic visibility node and an orchestration platform also in the same cloud, and also the Viavi set of tools that are inspecting this traffic. Here's a quick preview of the fabric manager, which is a centralized orchestration platform. It has integration with multiple clouds, VMware, ESX, NSX, and OpenStack, where you can define these traffic policies for inspecting the traffic. So here's a good example of where we're capturing SIP traffic with the capture rule of getting all the traffic over. A quick layout of where the ingress and where the destination tool is. There's a quick view of that. And then finally, we're gonna initiate some SIP traffic, generate a PN and SIPC traffic, and then we wanna inspect that and then deliver it to a tool. And here's the Viavi analyzer itself that is looking at the performance analyzer and then getting that traffic and inspecting it on the fly. So this way, as you look at any call drops or anything like that, you can monitor it through the Viavi application. And if you wanna double click a little bit deeper into that inspection, you got that as well. So this is a holistic system of capturing at the edge, delivering it to the tool, and all of it with gigamon orchestrating this infrastructure. That way, your call services can be inspected. So that's pretty much what I had with the demo. Let me go back and summarize some key benefits here. Again, as you consider migrating into this infrastructure, you're looking at maintaining compliance, security, and monitoring as you migrate over from the new infrastructure. You got your optimized monitoring tools themselves. You don't have to have probes. You can inspect it centrally and then forward it to the tools for analysis. Remote data centers. Most customers have a centralized code and a remote data center that you need to grab the traffic as well. So this also allows you to do that function. So if you have a remote open stack cloud that needs to forward traffic into a centralized open stack infrastructure, you got that. Multi-hypervisor support. A quick show of hands. How many of you use EMware and open stack in your deployments? Cool. So I guess either for new applications, greenfield, brownfield, but again, you got covered from both sides. And obviously we also have a thought leadership working with the open stack community as well. Again, just a quick plug for the tap of the service discussion. So if you want to attend that and get more details, more than welcome to do that. That's all I had. Thanks for your time. We're all here. So if you have any specific questions on how this can help, hang out and we'll talk more. Thank you very much. Appreciate your time.