talk some more about quantum inspired algorithms and I'm going to show how you can perform polynomials of matrices in time independent of the input dimension if you are given your input in this like quantum inspired access model that I covered last time. And just to recap what this model was, we had this quantum data structure that I defined that we said okay if this was in some quantum RAM then you could get efficient state preparation of whatever entries you're storing in your data structure. And then we noticed that if you could access it classically in order to devise like a classical algorithm that tries to match the runtime then you could get this access that's called sampling inquiry access. And based on this we defined this notion of oversampling inquiry access which is basically that we have oversampling inquiry access to a vector if we can answer these particular queries this is a VI if you give me an index and I can give you an entry. And also I have sampling inquiry access to VTILD where this means that I can query for entries VTILD i I can also perform measurements of VTILD as a state and I can also learn the norm of VTILD. So and this VTILD is going to have the property that it's like a reasonably small upper bound reasonably reasonably good tight upper bound on my entries of V. So all of my entries of VTILD are going to upper bound my entries of V and then you know the whole magnitude the whole the whole mass is going to be just five times the mass of the original vector. And with this we saw that you could get similar extensibility properties of this sampling inquiry access that you could with the blocking coding. So with sampling inquiry access to U and V we can get access to some linear combination and then the amount of overhead here is essentially is some the number of things in your linear combination times sort of the sums of the L2 masses.   So this is sort of like you can see how these are sort of similar maybe they're off by some factor in terms of the number of elements in your linear combination but there's some resemblance there. And what I'm going to show today is that you can get this statement about sampling inquiry access being closed or under taking products. So if you have sampling inquiry access to A and sampling inquiry access to B and you can get sampling inquiry access to something that's like approximately A, I think A dagger B. Okay and okay to do this I will it'll be useful to define the corresponding notions for matrices. So okay if you recall like our sampling inquiry access to a matrix was sampling inquiry access to the rows of A along with sampling inquiry access to the row norms. Yes, yes there is a reason why you need the dagger. There's some nuances in comparison if you are considering these like, like for example if I could give you both sampling inquiry access to A dagger and A and then be dagger and B and then be done with it there's some subtleties because typically if you want to actually like compare this to a quantum algorithm you're only going to store one of these guys in a data structure but in some sense like you're going to get a block encoding of A and then the inverse is going to be a block encoding of A dagger just immediately. And so something like that does work. So what you said just now would incur some error that's like scaling with the Frobenius number B so that would be an issue but there is a way to make this work. It's just, it is way more, it's like more complicated. Yeah but basically you can get like an approximate singular value decomposition and then you can like take the, or yeah you can just invert it in this way. Right. But yeah this has like, this is very slow I think the best way we know how to do it is very slow. Okay, good question. So right, so I was defining the sampling query access to A and it's just sampling query access to the rows and the vector of row norms. Similarly you can define sampling query access to over sampling and query access and what this is is it's just you have query access to A and then you have sampling query access to some A tilde where it satisfies these same properties.  And so with this you can say things like that given sampling query access to U and sampling query access to V, you can get sampling query access to U V dagger, they're out of product. And the way that you do this is just saying like, what I need to do is I need to compute entries but I can do this from computing entries of U and entries of V. I can compute norms by computing norms from V and then norms from U or like entries from the two. And then I can sample just by saying, okay, a row of U V dagger is a rescaled row of V dagger or a rescaled version of V and so I can use samples from V here and the row norms are, the vector of row norms is a rescaled version of U so I can sample from U to get these like row norm samples. So this is something that was in the problem set yesterday. And similarly we can extend this notion of, we can extend our linear combinations arguments here as well. And the value of phi is what you would expect it to be here. I'm just going to write it for completeness. But basically it follows from the same protocol and okay, so this will be all of the statements that I'll need for later. And now we're going to talk about how you actually get this, how you actually get this product and you'll see that it's like an approximate statement so we're going to use some approximation. We're going to need to use some other tools other than the one I've just been describing and what that is is this idea of sketching and the, if you haven't seen it before the idea of sketching matrices is that suppose I have some matrix A and it's like M by N and then M and N are too big and I can't compute anything on that. Then imagine I want to compute the top, I want to compute the spectrum or something, but this is too large so you can imagine I can multiply it left and right, multiply it by some sketch matrices and then bring this down to something that's much smaller, something S by T and then my hope is that the properties of A will be inherited by this sketch, sorry which I'm calling SAT and for a particular nice choice of S and T. So one case of this is like Johnson-Lindenstrauss where these S and T's can be like Gaussian and for us these will be sampling matrices. So if we have some distribution we're going to define a corresponding sampling matrix which we're going to get by taking each row to be some, selecting some row independently or random and how this works is that, so we're going to call this a, we're going to say this is sampled from P, if we have that, okay, the rows of I are independent, chosen independently or random and then this row in particular is going to be some computational basis vector and then it's going to be rescaled by the probability, by the root of the probability and then this will be the value of this row with probability Pk. So what your S matrix is going to look like is it's going to look like, okay say it's like here this is 1 over root SP1 and then here it might be 1 over root SP5, so it might look something like this and all of these other entries are zero and furthermore what's happening if we apply it to a matrix A is that if you look at what's happening we're selecting rows, yes, so this row will be A1 over square root of SP1 and then the other rows will also be the corresponding rows of A, so this is just what's happening when we choose our S and the thing to note is that if we have a sampling query access to A then we can form some sketch S officially and then what we do is we sample it from little a which is our vector of row norms. So in particular like our P, our probabilities are going to be the row norms rescaled by the Rupinias one, row norm squared, okay and you can sort of see how to do this, so this will tell you how to do this and then this will take O of S queries and sort of see how to do this because we just need to pull all of these samples from A which we have because of our access model and then we need to compute these probabilities themselves and we can do these with queries to our A, we might need to query the norms of A and then in total we can just describe this S as like a list of the non-zero entries here, so we can just describe all of these and then we can, this is our description of S. And secondly the thing that I want to observe is that if we have our A and then S sampled as above, so in this setting when we have this essay we can also sample again to get S-A-T. And how we can say this, how we can show this is that we say that we have sampling query access to both S-A and it's conjugate transpose and then the overhead will be O of 1 and O of S and so if we can have this then we can go from sampling query access to A and then we can construct S and T such so we can sample S from A and then T from S-A dagger and then we can get some sketch S-A-T with probability or sorry in time S-T or in S-T queries I guess. And if you wanted to try to prove this I won't go through the whole thing but the main thing that would give you trouble is the prospect of trying to sample from the column norms of S-A. Everything else is like you can do it in a sort of obvious way. Like for example all of the rows of S-A are all of the rows of S-A are rows of A so you can sample from them as normal and all of your row norms of S-A if you look at what the values are all the row norms are actually equal so all the row norms are equal to the Frobenius norm rescaled by S and so you can sample from the row norms of A. You can hear it right? So as I said before all of the row norms of S-A are the same and so you can sample from the row norms by sampling a row uniformly at random. How you sample from the column norms. Here what I want to do is I want to sample J with probability I guess the row or I guess the column of J over the Frobenius norm and there's a trick to doing this which is that we have access to S-A so what we can do is we can sample so this is what we want and what we can do is we can sample I with probability proportional to the rows and then we can sample some J with respect to that row that we sample from. Okay and if we do this procedure where we sample a row and then sample from the corresponding row then this will give us an index IJ and the probability of this IJ will be proportional to the entry squared so it's like we're sampling from S-A when written out as a vector and then if we discard our I then we're sampling J with probability proportional to the sum over I of these of these entries. Okay and this is precisely the thing that we wanted here so we're sampling an entry and then ignoring the row and this will give us the right thing. Right so I've now argued that you can efficiently create these sketches but it's I haven't yet told you what approximation properties this will have like what the point of this all is and I guess I'll demonstrate by saying okay consider we have some S and I'm going to consider a one by M matrix. So here we're just thinking about this as like a single row I guess it's a selecting a single row of my matrix and then if I look what this value is is it's what this is is it's some row AI over root of S-P-I with probability P-I and so what I can note is that if I have some matrix product sorry if I have some matrix product and I look at the sketched version of this product where I add a sketch in between so there's a I put like two sketching matrices in between A and B then what this is is it's the outer product the outer product row of A times the row of B I guess there's a darker on one of these so it's an outer product and then over S times P-I but here S is one so I guess we can ignore this over P-I with probability P-I okay and the thing to observe is that in expectation this is the sum of the sum of these outer products and this is going to be equal to A dagger B so when we sketch things down we can create an unbiased estimator of products of two matrices and so far I haven't used any properties of my probabilities probabilities and so far I haven't used any properties of my probabilities probabilities but what you can observe is that if we were sampling this P-I let's say P-I was sampled 50-50 from the row norms of A and the row norms of B so what I mean by this is that P-I if we take P-I to be equal to one half of okay we flip a coin and then based on that coin we either sample from A the row norms of A or the row norms of B and by AMGM this is going to be at least the geometric mean of these two which is this product here then what you can observe is that this imply these two imply that the norm of S-A dagger S-B is always upper bounded by this Frobenius norm product right because here what's happening is that these two row norms are cancelling out with the row norms here so in some sense all of the all of my estimators always going to be bounded and this allows me to use concentration inequalities to show that if you take a bunch of samples of this and then average them it'll be an unbiased S it'll be a it'll converge quickly to A times B A dagger times B okay. Okay so the I'm going to state some version of this that I for concreteness I guess and what that is is if we have some again A and B and then we have some S sample one half from A and then one half from B then if I take so here this is a size S sketch and then if I take S to be up to a log factor it's needs to be the I think this value then it's true that my sketch approximates my product up to operator norm and here you can think about these factors as being some rank quantity of A and B okay. With this I can show you why we can get our extensibility property so with this I'll just sketch it out how you get this. So we have sampling query access to X and Y and so what this allows us to do is it allows us to produce this sketch then with this sketch we know that this product is approximately our desired product to estimate and okay now if you think about how you get this you can figure out what this product is as I mentioned before this is an average of outer products of the rows so this is an average of the outer products of the rows or I guess a sum and each of these are rescaled rows of X and Y respectively so I have sampling query access to them and so I have sampling query access to SX and SY which means I have sampling query access to these outer products and finally this means I have sampling query access to this whole expression because it's a linear combination of X and Y respectively these outer products so I have the sampling query access to the whole thing up to some oversampling constant and this is approximately X times Y. Now why do we need to sketch things down it's because that when we are taking these linear combinations the overhead that we incur I didn't write this down above but when we do linear combinations we incur some overhead that's the number of sum ends in our linear combination and so naively we're taking a linear combination here without the S it would be a linear combination of some like large dimension thing so we need it to be size S where S is something here that's like independence of my input dimension in order to then be able to have this linear combination protocol be efficient and it's in the lecture notes and eventually what you get is that you can get this value of phi to be the Frobenius norm of X squared times the Frobenius norm of Y squared which is sort of I guess what you what one might expect from just combining upper bounds. Right so are there any questions about this? Right so in a similar way I have this in the lecture notes also but we can also use this to de-quantize the simple blocking coding protocol I had before which is that if I had a blocking coding protocol I would have a blocking of A and the state psi then I could get a copy of the state A times psi with probability proportional to the norm of A times psi squared. When we have this as given as input in a data structure then this A is going to be rescaled by the Frobenius norm and so this you're going to see some Frobenius norm scaling up here in the probability and so you can think about this as having a runtime of I guess it's the Frobenius norm square times the norm of psi squared over the norm of A psi squared. What I give a more thorough argument with the parameters and stuff in the lecture notes but in the same setting if I have sampling query access to A and sampling query access to psi then what I can do is I can sort of approximate this by with a sketching matrix as I mentioned before and then using the same ideas as again up here this allows me to get sampling query access to this sketched matrix which is approximately A times psi and so you can imagine like okay if I want a sample from the output then I could use this to get a sample from my output as well and so I'm sort of matching what the quantum algorithm is doing and if you look at the runtime of this algorithm the runtime will be in order to get a sample it'll be basically proportional to get a sample is let's see so it's equal to Frobenius norm to the fourth times so you can see that sorry you can see that this is the same but it's there's this issue of there's this issue of there's an additional factor that appears here this is like a polynomial factor namely this factor is smaller than this factor so it's like you're only losing quadratically and then you might be wondering that there's like you might be wondering that there's like an epsilon here and there's no epsilon in this runtime and whether that's an issue and the resolution to this is that for these like sampling questions you can see these like gaps of like log one over epsilon to epsilon I don't know of a formal argument that this is like inherent it might not be but if you wanted to use these samples to actually like construct some estimator like for example like learn some properties is this true or false then this epsilon one over epsilon will appear because you're trying to distinguish two states that are maybe epsilon far apart now I'll move on to like general polynomials but any questions about this alright so now I've shown how to give these extensibility properties for both linear combinations of of my input and also products and using this I can sort of you can imagine me chaining all of these together to get sampling and query access to more complicated objects and in the same way I mentioned before where this is like sort of not it doesn't necessarily give you everything here we also have to do some more trickery to get the ability to perform any polynomial on a matrix. And and do this efficiently. So in the rest of the time I'll sketch how I'll give like a sketchy description of how you get a classical algorithm for the singular value transformation task and what this task is is that if we have sample and query access to A and sample and query access to B you want sample and query access to some I guess I'll say call it Y where Y is approximately P of A times B and here we have P which is bounded between minus one and one and also the degree of P I'm using D for now sorry the degree of P is small and what we want is we want some runtime that's we want a runtime that's polynomial in one over epsilon and D and then the Frobenius norm over the spectral norm here so this is like our rank notion is that it I think so and also yeah we should use that we should normalize things so the spectral norm of A is at most one I guess we can say let's say it's equal to one and so we can just drop this term and say Frobenius norm and so the idea is that a quantumly if you give me a blocking coding of A and copies of my state B then I can do this and it'll take time that's it'll take time I think D times Frobenius norm of A and again this epsilon doesn't appear but if you wanted to do something there would be a one over epsilon that appears okay and I'll tell you I'll tell you first the idea for monomials and then I'll extend it to general polynomials okay so consider some monomial I'll just say actually I guess it's like yeah okay what this is is it's this is a dagger cubed times B and if we wanted to sketch this down what we could do is we could say we have these constructed we can construct these S and T and these are sketching matrices that we can sample efficiently and they have this property that if you put them in between matrix matrices they can approximate the products and specifically what happens is that it approximates these products of A times A well, S approximates A times A well and then T approximates SA times SA well so and this is because we sampled our T from SA we sampled our T from the column norms of SA and so it's going to approximate SA well and so with these approximation properties in hand we can just say that this polynomial this monomial is you can approximate it by okay first of all we can say that it's here we can just approximate each one each thing by its product okay and here this SB I didn't state this property but you can show that it holds if you have like a matrix vector in your sampling you can sketch down this matrix product as well and then you can sketch it again to say that okay this is approximately SAT dagger SAT so I'm sketching this product here I'm sketching this product here to get wait no no I'm sketching this here so what I'm doing is I'm sketching this interior right so I can approximate all the way down to this like fully sketched on matrix and the thing to note is that this these dimensions are now very small so this SAT is S times T this SAT dagger is T times S and this is a S by one matrix and so I can compute all of these in O of TS time and what I'm left with is something that's like of the form SA dagger times V and what this is is this is a all the columns of SA dagger are rows of A right SA is rows of A and then this V is some small vector so what this is is I'm taking a linear combination of a small number of rows of A and this is my solution and so this means that I have sampling query access to the output because it's a small linear combination of rows of A and I have sampling query access to my input okay so so in this setting it basically becomes a game of I take my input and I just hit it with as many sketches as I can until things are independent of dimension and then when things are independent of dimension here my S and T are going to be size I don't know so I think S and T here need to be size for be near norm squared over epsilon squared and for this choice then I can then everything small enough that I can just multiply everything through and then there will be a final matrix on the outside that I can't sketch away and this will lift my small vector back into the space of the space of my output okay so this is the approach for a monomial how I want to do general polynomials is I use Chebyshev polynomials and specifically what I want to do is I want to be able to evaluate a polynomial by with operations that are like multiply and add so so if I have some polynomial that's again it's like bounded and it's degree degree is small then as I discussed before we can write P of X as this linear combination of Chebyshev polynomials and here you can think of my AKs as all being relatively small so right these AKs are bounded by two from something from previous lecture the question is how do I evaluate this P you might think that you can evaluate P in the monomial basis by saying P of X is equal to a sorry maybe a different CD X to the D plus CD minus one X to the D minus one and so on right because I already showed how you can do these monomials and so you can probably just change them but the problem is that these CDs can be exponentially large and so if we have some algorithm that incurs epsilon error in my approximation here then when I multiply it by CD it can be blown up by two to the D and this will break our dreams okay so an alternative is we want to use something some recurrence or some description of my polynomial that is going to be stable the entire time it's never going to blow up and this is what this clencha recurrence is so I'm going to write it one way and then I'm going to write it a different way so the version that you'll most commonly see is this version where you start with zeros and then you can evaluate this polynomial and then you can evaluate this polynomial by saying it's similar to the recurrence relation defining the championship polynomials themselves plus a K and then so the idea is that I start off with zeros and then I run this recurrence relation maybe I should do it for and what this will do is it'll sorry I start from QD plus one and then I construct QD and then QD minus one all the way down to Q zero and then what you can show is that P of X is equal to one half Q zero minus Q two so we are evaluating these we can evaluate this and what we are doing is we're just taking this recurrence and then what you can show is that all of these QKs are now to be bounded and so whenever you're taking your approximations you're never scaling it by too much and so if you so you should think about like this being better in a way that might become more clear in a second. Now formally what we're going to do is we're going to do a slightly different thing for some technical reasons which is we're going to evaluate this recurrence here which is just the version that works for it's the version that works and it maintains that all of your iterates are odd so now I'm going to restrict to the case that P is odd and this means that all my even coefficients are zero and what my iteration is going to be is the following and then if you do this all the way to zero then your polynomial is going to be Q zero minus Q one and okay you're asking what does this have anything to do with matrices? Well all these scalar recurrences lift to the matrix setting naturally. If I define some other recurrence and say that if I define this recurrence where I replace all of the x's with like a's and I replace this here with is this right? I add this B here on this thing that I'm adding to make it a vector. Then what I get is that my final outcome will be equal to P of A times B and right so if I wanted to compute this and I didn't care about the runtime what I would do is I would just compute this every for all my iterations and then get my output and the output would it'll be so if we're imagining A is M by N then this will take O of D times M N time or M N can be like the number of non-zero entries of A if you want. Okay so this is going to be our recurrence and the idea will be the same as before which is that we have some approximation properties of our sketch and then we can use this and so maybe I'll just maybe I'll do some slightly bad form and I'll edit the equation itself. But what we can do is we can say that we maintain instead of maintaining Q K which is something that's large we can maintain it as a linear combination of small a small number of rows of A. So by this I mean we can maintain instead of instead of maintaining these U's what we can maintain is some V where V is small. Okay and then what our recurrence will be is equal to A times V K minus okay and then what we can do is we can and then what we can do is we can start hitting things with sketches. So this will be approximately SA dagger SA minus oh actually what I'm going to do okay specifically what I'm going to maintain is this. So this will actually enforce that my linear combination is small because it's going to just be linear combinations of the S things in my sketch. Okay so then what we're going to have is and so on. Oh actually I want to also approximate this final thing too and with this I can pull every pull out this SA here. Okay I think I'm almost I think I'm out of time so I'll just say here that you get some expression in here and this expression is going to be some vector some matrix vector computation and it's dimension S by 1 so we've already shrunk down our dimension and by doing some like one more sketch in here we can get something that we can compute efficiently and then when we compute this efficiently what we sacrifice is that okay we're able to compute this in time independent of dimension but we're incurring some epsilon factor and then you can use an argument about the scalars how the scalars errors propagate to show that the matrix errors also do not blow up too badly and so this will give you an algorithm that has this desired property has this desired run time. Okay that's it thanks.


C&O and McPon held an all-hands call on Friday at Joint Expeditionary Base Little Creek Fort Story. The top two Navy leaders told more than 1,600 sailors and Marines they will look at seapay to see how it can keep up with inflation. C&O also said tuition assistance will be available at 2013 levels. See what else C&O and McPon had to say on the Navy live blog. Effective January 22nd, the instruction for the accommodation of religious practices has changed. The updated instruction details how commands will seek approval for religious requests. The DOD places a high value on your rights to observe your religion. Check out the DOD instruction for more details on the changes. This new instruction will provide Navy leaders another way to promote a positive command climate. Read more about these stories at Navy.mil. From all hands update, thanks for watching.


okay what's going on we're here Christchurch New Zealand the date is November the 4th yes I'm late but better late than never guys this is it I feel like this is it this is gonna be the intro clip to Rugby Player React to the top 100 NFL players of 2020 a task a mammoth task that I've had in the back of my head for a good two months it's been a long two months guys if you lost faith they don't blame you but I have to ask you one question and one question only when have I ever said I'm gonna do something and not follow through exactly so guys I'm not gonna take up any more of your time I've literally got three windows open in front of me we've got the top 100 players of 2020 the playlist we've got Wikipedia and we've got pro football reference so guys I sit here and invite you to watch with me the top 100 players of 2020 in the NFL and the reaction series starts now so peace out and I'll see you in the next one for number 100 holy fucking shit what have I just done


Okay, the year's coming to an end. I'm trying to get healthy. So I'm gonna stop baking and start cooking. Take it from me. A balanced diet is super important. Ooh, my super healthy soup is done. So I'm here to tell you I'm officially changing my name from Johnny Kicks to Johnny Cooks, okay? No more cake for me. Okay, maybe one more cake. You can't expect me to cut cake out cold turkey. That wouldn't be fair. I promise I'll start cooking healthy soon in all these beautiful crusades that I purchased at the Colorful Friday sale. Until then, I'll just eat some.


Happy 10 girls walk, girls walk, and the reed girls walk, their clothes arrive Remember to set a timer when you put your clothes in the washer. We all know what it's like to be waiting forever on a washing machine, so let's try to make it as painless as possible for everybody, forever.  We don't want any sudsy overflows, right ladies?


The government of St. Louis, who hates, has to find tangible support for the national fight against the COVID-19 epidemic. Minister for Health, Wellness, and Elderly Affairs, Honourable Moseley-Moseley, on Tuesday, 14th September 2021, during the sitting of the House of Assembly, provided details on the various measures taken over the last year. Honourable Jabbatis told the House that major improvements would have been completing at the respiratory hospital, bringing relief to patients as well as the minister reported that the functioning of the telecommunications system is in place. Critically, the Ministry of Health has increased personnel at the respiratory hospital to ensure the monitoring of COVID patients and those awaiting themselves. Honourable Jabbatis further informed that additional staff will be made available. The system of monitoring from testing to results, to home monitoring of positive cases, to the transfer to the Victoria Hospital if necessary, need to be streamlined and our professionals are meeting today, this afternoon, to strengthen and tighten that system. For, community testing and vaccinations will receive a boost with the purchase of a new vehicle thanks to the Minister for Finance and Prime Minister. For this purpose with staffing, the vehicle will complement another vehicle which is being repaired for the purpose of having a permanent mobile unit for the vaccination and testing around the communities in St. Lucia. We are trying, Mr Speaker, to limit the movement of suspected cases that is from travelling from their homes through our transportation system to the hospital to be tested and sometimes they do not get the tests and have to return on the very bus back to their homes. We are trying our best to take the test possibly even to the homes of suspected cases. To avert any shortage of medical gas the Ministry of Health in partnership with the Ministry of External Affairs, take your supplies from the Martinique. We caused a shipment of liquefied oxygen to reach St. Lucia from Martinique urgently just as a backup in the event that our gas at our medical gas at Victoria Hospital got us, got into problems. We had a situation where we had to seek the assistance of the OKEU Hospital and also the Tapion Hospital and I want to take this opportunity to thank the management of the OKEU Hospital and also the Tapion Hospital for working with us. But we got a shipment from Martinique on Saturday and I wish to thank Dr Eugene and Ms Nancy Francis for ensuring that things went well at the ports. Honourable Mosey Jabati says government remains in dialogue with property owners in order to increase the room stock for the team.


Joe Giordi spent 10 years as the Yankees manager he spent a lot of time in that home dugout I asked him if it was a little odd walking into the visitor's clubhouse today That's just somewhat awkward in and of itself because that's not something that I really did for 10 years I always came in through the garage down the stairs into the home clubhouse And this we came through the loading dock and you know from center field all the way around so that was different I did see a lot of familiar faces in Luke Akusa and his clubhouse staff So that seemed kind of normal, but you're on the opposite side and it's different What is the reunion been like between you and Dede your glorious and a couple other former Yankees as well It's been good. It's great having Dede on our team. He's played extremely well, especially in this this 2.0 as we call it And and I think he's grown really fond of his teammates and his teammates love him You know as you said, there's some other Yankees that we've have in camp You know what Tareas is here and everyone loves toe I mean, how can you not love toe and the energy that it brings and him and Gene Segura have a great banter going on During every inner squad game that we have so it's been enjoyable having the guys on the team I'm just curious as much as the separation has been just walking back into the building Bring back emotions that you didn't anticipate. Well, how do you kind of make that separation? Yeah, I think it will probably come out a little bit more during the game, you know when you're in a clubhouse It's different than being on the field. It's like you're in you're in a room, right? But I'm sure there'll be some emotions. I mean, I have you know 14 really really really 15 really fun years of being here whether I was a player a coach or a manager and They were the great days and a great part of my life. So there's there's really a lot of fond memories Joe Girardi's face won't be the only familiar one in that Phillies dugout check out the number of players who used to wear Yankee pinstripes now donning red and white including Dee Dee Gregorius, he was a fan favorite when he was in the Bronx He spent five seasons with the Yankees including to with manager Aaron Boone Dee Dee's one of those guys that brings a smile to your face and Plays the game hard He's tough. He's a really good player and You know, I just think about I You know even when I was from afar before I got here You know, I followed his career pretty closely and you know to know the situation he walked into here following Derek Jeter You know knowing how his career started that first month where he had his struggles that that could have easily buried a you know a lesser person or player it it almost made him better and he was a really good Yankee while he was here and Got a lot of big hits and and Was it a really important part of some really good teams?


We developed an innovative vaccine technology which allows us to quickly adapt to emerging threats by precision engineering vaccines. This is based on the ability of microbial cells and their endogenous assembly pathways which we are hijacking and utilizing in this process. So what we basically do is we take information, genetic information from the virus and incorporate that into microbial production hosts. We have obtained the genetic information and commenced with this work. So we have now developed four vaccine candidates and these candidates contain components of the virus that causes COVID-19. These candidates are produced in the lab and we have them available for now animal trials. We have a local manufacturing organization in DERA, Luina Bio. They are very well set up to actually take up our technology and block it into their industrial manufacturing processes. And what we anticipate is that using their capabilities and expertise that we will be able within two to three months from now able to produce millions of doses per week. So currently we are producing those vaccine candidates and we are reaching a point after the animal trials where we urgently need funding to move to the next step.


From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. As you well know by now, the cloud is about shifting IT labor to more strategic initiatives. Or as Andy Jassy laid out years ago, removing the undifferentiated heavy lifting associated with deploying and managing IT infrastructure. Cloud is also about changing the operating model and rapidly scaling a business operation or a company. Often overlooked with cloud however, is the innovation piece of the puzzle. A main source of that innovation is venture-funded startup companies that have brilliant technologists who are mission-driven and have a vision to solve really hard problems and enter a large market at scale to disrupt it. Hello everyone and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we're pleased to welcome a special guest and author of The Elite 80, a report that details the hottest privately held cybersecurity and IT infrastructure companies in the world. Eric Suppinger is that author and joins us today from JMP Securities. Eric, welcome to theCUBE, thanks for being here. Thank you very much, Dave. I'm looking forward to having the discussion here with you. Yeah, me too, this is going to be great. So let's dive right in to The Elite 80. First, if you could, tell us about JMP securities and fill us in on the report, its history, your approach to picking the 80 companies out of thousands of choices. Sure, so JMP is a middle markets investment bank. We're a full service investment bank based in San Francisco. We were founded in 2000 and we focus on technology, healthcare, financial services and real estate. I've been with JMP since 2011. I cover cybersecurity companies, public companies. I cover IT infrastructure companies more broadly. And we have, having been based here in San Francisco, I've long kept a good dialogue with private companies that compete with the public companies that I cover. And so about seven years ago, I started developing this report which is really designed to highlight emerging private companies that I think are well positioned to be leaders in their respective markets. And over time, we've built the list up to about 80 companies and we publish this report every year. It's designed to keep tabs on the companies that are doing well and we rotate about 15 to 20 to 25% of the companies out of the report every year, either as they get acquired or they do an IPO or they, if we think that they're slowing and others are getting a little bit more exciting. And you talk directly to the companies, that's part of your methodology as well. You do a lot of background research, digging into funding, but you also talk to the executives at these companies, correct? Yes, for the most part, we try to talk to the CEOs, at least the CFOs. The object here is to build a relationship with these companies so that we have some good insights into how they're doing and how the market trends are evolving as they relate to those companies. In particular, some of the dynamics that go into us selecting companies is one, we do have to talk to the management teams. Two, we base our decisions on who we include on how the companies are performing, on how their competitors are discussing those companies, their performance, how other industry contacts talk about those companies. And then we track their hiring and how they've, you know, other metrics that we can gauge them by. Got it. Okay, so I dug into the report a little bit and tried to summarize a few key takeaways. So let's take a look at those. And if you allow me, just set up the points and then I'll ask you to add some color. So the first two things that really jumped out and I want to comment on are the perspectives of the technology companies, and then of course the other side is the buyers. So it seems that the pandemic really got startups to sharpen their focus. I remember talking to a number of VCs early on in the shutdown and they were all over their portfolio companies to reset their ICP, their ideal customer profile and sharpen their UVP, their unique value proposition. And they wanted them to do that specifically in the context of the pandemic and the new reality. And then on the buy side, let's face it, if you weren't a digital business, you were out of business. So picking up on those two thoughts, Eric, what can you share with us in terms of the findings that you have? Well, that's very consistent with what we had found. Basically when the pandemic first, when the lockdown came in March, we reached out to quite a few companies in the industry context at that time. The feedback was, it was a period of great uncertainty and a lot of budgets were tightened pretty quickly. But it didn't take very long and a lot of these companies having been innovation engines and emerging players, what they found was that the broader market quickly adopted digital transformation in response to the pandemic. Basically that was how they facilitated keeping their doors open, so to speak. And so the ones that were able to leverage need for emerging technologies because of an acceleration in digital transformation, they really stepped up and quite a few of these companies, they kept hiring, they kept their sales did very well. And ultimately a lot of the VCs that had been putting on the breaks, they actually stepped up and continued funding pretty generously. Yeah, and we've got some data on that that we want to look into, so thank you for that. Now, let's take a look at some of the specific data of the study, just break that down. The Elite 80 raised more than $3 billion last year, eclipsing the previous highs in your studies of 2019. And then a big portion of that capital went to pretty small, not only 10 of the 80 firms and most of that went to cyber security plays. So what do you make of these numbers, especially given your history with this group of elite companies and the high concentration this year, this past year? So one of the trends that we've seen in the public, in the public market or the IPO market is companies are waiting till they're a little bit more mature than they used to be. So what we've seen is the funding for companies, the larger rounds are far larger than they used to be. These companies typically are waiting till they're of size, maybe now they're waiting to be 200 million in annual revenue versus 100 million before. And so they're consuming quite a bit, the larger rounds are much bigger than they used to be. In the most recent report that we published, we had one round that was over half a billion and another one that was over 400 million.  So that's been a distinct change. And I think that's not necessarily just a function of the pandemic, but I think the pandemic caused some companies to kind of step up the size of their rounds. And so there were a handful of very large rounds, certainly bigger than what we'd ever seen before. Yeah, those are great observations. I mean, you're right, it was 100 million used to be the magic number to go public. And now you get so much late money coming in, locking in maybe smaller gains, but giving that company a little more time to get their act together pre-IPO. Let's take a look at where the money went. You know, talking about follow the money and Eric, you and your team, you segmented that $3 billion into a number of different categories. As I said, most of it going to cybersecurity categories like application security, assessment and risk, and endpoint boomed during the pandemic. Same with identity. And this chart really shows those categories that you created to better understand these dynamics and sort of figure out where the money went. How did you come up with these categories? And what does this data tell you? So these categories were basically homegrown. These are how I think of these companies. It's a little bit of pulling some information out of the likes of Gartner, but for the most part, this was how I conceptualized the landscape in my mind. The interesting thing to me is, a lot of that data is skewed by a few large transactions. So if you think about the allocation of those different categories and the investments in those categories, it's skewed by large transactions. And what was most interesting to me was one, the application security space is a space that had quite a few additional smaller rounds. And I think that's one that's pretty interesting going forward. And then the one that was a surprise to me more than that was the data management. Outside of cybersecurity, data management is a space that's getting a lot more attention and it's getting some pretty good growth. So that's a space that we're paying some good attention to as well. Yeah, that's interesting. I mean, of course, data management means a lot of different things to a lot of different people. And VCs throw money at it, maybe trying to define it. And then the AI ops and that data management piece took a portion of it, but wow, the cyber guys really are killing it. And now, as we mentioned, 10 companies sucked up the lion's share of the funding and this next chart shows that concentration of those 10 investments. So Eric, some big numbers here with one trust secured more than a half a billion dollars, four others nabbed more than a quarter billion in funding. Give us your thoughts on this. What do you make of that high concentration? Well, I think this is a function of companies that are waiting longer than they used to. These companies are getting to be of considerable scale. I mean, Tainium would be a good example. That's a company that could have gone public years ago. And I don't think they're particularly eager to get out the door. They provide liquidity to their previous investors by raising money and buying those shares back. And so they basically just continue to grow without the burden or the demands that being a public company create. So that's really a function of companies just waiting longer before they get out the door. Got it. Now, here's another view of that data. So the left side of this chart that we want to show you next gives you a sense of the size of the companies, the revenue in the elite 80. And most of these companies have broken through the $100 million revenue mark, as you say. And they're still private. And so you can see the breakdown and then the right hand side of the chart shows the most active investors. We just pulled out those with three or more transactions. And it's interesting to see the players there. And of course you've got some strategic, you got city in there, you got Cisco, along with a little bit of P&E, private equity action. Maybe your thoughts on this data. So to give you a little flavor around the size of these companies, when we first started publishing this report, a little bit of the goal was to try to keep those categories relatively equal. And as you can see, they've skewed far to the left towards the larger revenue size. So that just goes to the point that the companies that are getting, a lot of these private companies, they're of considerable size before they really go out the door. And I think that's a reflection of the caliber of, or the quality of investments that are out there today. These are companies that have built very mature businesses and they're not going into the market until they can demonstrate high confidence and consistency in their performance. Yeah, I remember when Cloudera took that massive, I think it was $750 million investment from Intel way back when that bridged them to IPO. And that was sort of, if I recall, started that trend. And then now you get an IPO last year like Snowflake, which is price to perfection. And you got guys that really know how to do this. They've done it a number of times. And so it really has somewhat changed that dynamic for IPOs, which of course came booming back. It was so quiet there for so many years. But let's look into these markets a bit. I want to talk about the security space and the IT infrastructure space. And here's a chart from Optiv, which is one of the Elite 80 ironically. And we've shared this with our audience before. And the point of this is that the cybersecurity space is it's highly fragmented. We've reported on this a lot. It's got hundreds and hundreds of companies in there. It's just mosaic of solutions. So very complicated and bespoke sets of tooling. And combine that with a lack of skilled expertise. CISOs tell us the lack of talent is their biggest challenge. It makes it a really dynamic market. And Eric, this is part of the reason why VCs, they want in. So the takeaway I get from that chart is we have a lot of, we still have a great need for best of breed. Digital transformation, cloud, mobile, all these trends are creating such a disruption that there's still a great opportunity for somebody that can deliver a real best of breed solution. In spite of all the challenges that IT departments are having with trying to meet security requirements and things like that. The world has embraced digital delivery. And your success is oftentimes dependent on your digital differentiation. And if that's the case, then there's always going to be opportunity for a better technology out there. So that in the end is why Optiv has a line card that's as long as you can read it. I'm glad you brought up the point about best of breed. It's an age old debate in the industry. Is do we go best of breed? Or do we go integrated suites? You look at a company like Microsoft, obviously, that works very well for them. You have companies like Cisco. But so this next set of data, we're going to bring in some ETR customer spending data and see where the momentum is. And I think it'll really underscore the points that you're making there in terms of best of breed. So this chart shares a popular view that we like to share with our community. On the vertical axis is net score, or that's spending velocity. And the horizontal axis shows market share or pervasiveness in the data. As we've said before, anything above 40% that red line on the vertical axis is considered elevated. And you can see a lot of companies in cybersecurity are above that mark. Now a couple of points I want to make here before we bring Eric back in. First is the market, it's fragmented, but it's pretty large at over $100 billion, depending on which research firm you look at. It's growing at the low double digits. So nice growth is putting on $10 billion a year into that number. And there are some big pure plays like Palo Alto Networks and Fortinet. But the market includes some other large whales like Cisco. They've built up a sizable security business, Microsoft. Microsoft is in most markets and serves its software customers. So, but you can see how crowded this market is. Now we've superimposed in the red recent valuations for some of the companies. And the other point we want to make is there's some big numbers here and some divergence between as Eric was saying the best of breed and the integrated suites. And the pandemic, as we've talked about a lot is fueled a shift in cyber strategies toward endpoint, identity and cloud. And you can see that in CrowdStrike's $50 billion plus valuation. Okta, another best of breed, $34 billion in identity. They just bought Auth0 and paid $4.5 billion for Auth0 to get access to the developer community. Zscaler, $28 billion. Proofpoint is going private at a $12 billion number. So you can see why VCs are pouring money into this market. Really attractive valuations. Eric, what are your thoughts on this data? So my interpretation is that's just further validation that the security markets are getting disrupted. And the truth of the matter is there's only one really well positioned platform player in there, Palo Alto. The rest of them are platforms within their respective security technology space. But there's not very many broad security solution providers today. And the reason for that is because we've got such a transformation going on across technology that the need for best of breed is getting recognized day in, day out. Yeah, you're right. Palo Alto, CISOs love to work with Palo Alto. They're kind of the high end gold standard. And we reported last year on the divergence and valuations between Fortinet and Palo Alto networks. Fortinet was doing a better job pivoting to the cloud. We said Palo Alto will get its act together, it did. But then you see these pure play best of breeds really doing well. So now let's take a look at the IT infrastructure space. And it's quite different in terms of the dynamics of the market. So here's that same view of the ETR data and we've cut it by three categories. We cut on networking, servers and storage. And this is a very large market. It's over $200 billion. But it's much more of an oligopoly in that you've got great concentration at the top. You've got some really big companies like Cisco and Dell which is spinning out VMware. So we're going to unlock more value of the core Dell company. Dell's valuation is 79 billion. That includes its 80% ownership in VMware. So you do the math and figure out what core Dell is worth. HPE is much smaller. It's notable that its valuation is comparable to NetApp. NetApp's around one fifth the size revenue-wise, HPE. Now, Eric, Arista, they stand out as the lone player that's having some success clearly against Cisco. What are your thoughts on the infrastructure space? So a couple of things I'll take away from that. First off, you mentioned Arista. Arista is a bit of an anomaly. A switching company, a networking company that is in that upper echelon, like you've pointed out above 40%, it is unique. And basically they kind of cracked the code. They figured out how to beat Cisco at Cisco's core competency, which is traditionally switching and routing. And they did that by delivering a very differentiated hardware product that they were able to tap into some markets that even Cisco hasn't been able to open up. And those would be the hyperscale hosting vendors like Google and Facebook and Microsoft. But I would put Arista kind of in a unique situation. The other thing that I'll just point out that I think is an interesting takeaway from the slide that you showed is there are some infrastructure or what I would consider bordering on data management type companies.  And I think that comes back to what I was mentioning earlier where there is some pretty interesting innovation going on in data management which has traditionally not had a lot of innovation. So I would bet you those names would have bubbled up just in the last year or two where that's been a market that hasn't had a lot of innovation and now there's some interesting things coming down the pipe. You know, that's interesting comments that you're making there because if you think back to sort of last decade Arista obviously broke out the only two other companies in the core infrastructure space and this was a hardware game historically but it's obviously becoming a software game but take a look at pure storage and Nutanix. You can see their valuations at $5 billion and $7.4 billion respectively. And then to your point Cohesity, you got them at 3.7 billion just did a recent round. Rubrik, 3.3 billion, that's from 2019. And so presumably that's a higher valuation now. Veeam got taken out last January at $5 billion by Insight Capital. And so I think they're doing very well and they're probably up from that and SUSE is going public at a reported $7 billion valuation. So quite a bit different dynamic in the infrastructure space. So Eric, I want to bring it back to the elite 80 and startups in general. My first question to you is what do you look for from successful startups to make this elite 80 list? So a few factors. First off, their performance is one of the primary situations. If it's a company that's not growing, we'll probably pull it from the list. I would say it is also very much a function of my perception of the quality of management. We do meet with all these management teams. We feel like they're putting together a leadership team that's gonna be around for a long time and they've got a product position that's pretty attractive. Those would certainly be two key aspects of what I look for. Beyond that, certainly feedback that we get from competitors, feedback that we get from industry contacts like resellers. And then I'd also just say my enthusiasm for their respective market that they're in. If it's a market that I think is gonna be difficult or flat or not very interesting, then that would certainly be a reason to not include them. Conversely, even if it's a small company, if it's a sector that I think's gonna be around for quite a while and it's very differentiated, then we'll include a lot of the smaller companies too. A good example of that's like a Weka. I mean, I don't wanna go into these companies, but 80 companies are gonna leave somebody out, but that's a good example of a smaller company that wants to be disruptive. How should enterprise customers, the buyers, do you think evaluate and filter startups? Do you have any sense of that? Well, a couple of things that I struggle with that would be something that's a lot more readily available to them is just the quality of the product. I mean, that's obviously why they're looking at it, but if it's a company that's got a unique product that is built, that works, that would be the starting point. And then beyond that, it's also, is it a management team? Is the behavior of the company something that reflects a management team that's a high quality management team? If they are responsive, if they're following up, if they're not trying to pull in business quickly, if they're priced appropriately, metrics like that would certainly be key aspects that would be readily available to the buyers of technology. Beyond that, I think the viability of that market is gonna be a key aspect as to whether or not that company's gonna be around, even if it's a good company, if it's a highly competitive market that's gonna have some big players that can kind of integrate it and to make it a feature across other product lines, then that's gonna make it a tough road to go for a startup these days. You know, the other thing I wanted to talk about was the risks and the rewards of working with startup companies. And I've had CIOs and enterprise architects tell me that when they have to do an RFP, they'll pull out the Gartner Magic Quadrant, they'll always pick a couple in the top right just to cover their butts. But many say, you know what, we also pick some of those in the challenger space because that are really interesting to us and we run them through the paces and we manage those risks, we don't run the company on them, but it helps us find these diamonds in the rough.  But do you have any thoughts on how people should manage those risks and how should they should think about the upside? I don't think today a company can work today using legacy technologies. I think the greater risk is falling behind from a digital transformation perspective. This era, I think the pandemic is probably the best proof point of this. You can't go with just a traditional legacy architecture in a key aspect of your business. And so the startups, I think you've got to take the quote unquote risk of working with a startup that's got a viability concern or sustainability concern, the risk of having an IT infrastructure that's inadequate is a far greater risk from my perspective. So I think that the startups right now are in a very strong position. And they're well-funded. And that's the last question I wanted to talk about is how will startups kind of penetrate the enterprise in this modern era? I mean, you know, this is really a software world and software is a sort of capital efficient business but yet you're seeing companies raise hundreds of millions of dollars. I mean, that's not even absurd these days. You see companies go to IPO that have raised over a billion dollars. And much of that, if not most of it goes to promotion and go to market. So maybe you could give us your perspectives on how you see startups getting into the enterprise in these sectors. So one of the really interesting things that we've seen in the last couple of years is a lot of changes to sales models. And if you look at the mid market, the ability to leverage viral sales models has been wildly successful for some companies. It's been, you know, a great strategy. There's a public company, Ubiquity, that built a multi-billion dollar, you know, business on without a sales organization. So there's some pretty interesting directions that I think sales and go to market is going to incur over the coming years. Traditional enterprise sales, I think are still pretty standard today. But I think that the efficiency of, you know, social networking and what would, the delivery of products in a digital format is going to change the way that we do sales. So I think there's a lot of efficiencies that are going to come in sales over the coming years. That's interesting because then you'll, you know, I think you're right. And instead of just pouring money at promotion, maybe get more efficient there and pour money into engineering because that really is the long-term sustainable value that these companies are going to create. Yeah, I would absolutely agree with that. Again, if you look at the charts of the well-established players that you had mentioned, those companies are where they are, that the ones at the top are where they are because of their technology. I mean, it's not because of their go-to-market. It's because they have something that other people can't replicate. All right. Well, Eric, hey, it's been great having you on today. Thanks so much for joining us. Really appreciate your time. Well, Dave, I greatly appreciate it. It's been a lot of fun. So thank you. All right, hey, go get the Elite80 report. All you got to do is search JMP Elite80 and it'll come up and there's a lot of data out there. So it's really worthwhile reference tool so thank you, everybody, for watching. Remember, these episodes are all available as podcasts wherever you listen. You can check out ETR's website at ETR.plus and we also publish weekly a full report on wikibon.com and siliconangle.com. You can email me, david.volante at siliconangle.com or DM me on Twitter, at dvolante. Hit our LinkedIn post and really appreciate those comments. This is Dave Volante for theCUBE Insights, powered by ETR. Have a great week, everybody. Stay safe and we'll see you next time.


Okay, good. All right, so my name is Melvin Hillsman, as you can see from the slide there, right? OpenStack Engineering Rackspace. And this presentation, learning some OpenStack for great good. It's gonna be very high level, very simple. I'm hoping by the time I get through with the talk that the DevStack install will be done, unfortunately, Hotel had some internet issues yesterday. So I didn't get a chance to get everybody set up with the lab. All right, so keep in touch with me. Mr. Hillsman everywhere at mrhillsman.com, Mr. Hillsman at Gmail, at Mr. Hillsman on Twitter. IRC is Mr. Hillsman, so there shouldn't be any problem getting in contact with me if you decide you want to, right? Okay, let's dive into it. So non-volatile Mary Lane cloud and virtualization concept. So this is essentially what you should already know. Okay? So pre-virtualization, right? Before we started utilizing KVM and all these other virtualization technologies, we had basically this room with these big, big desktops in them, essentially, these big servers taking up a lot of space, using up a lot of energy. Pretty much problematic, right? So essentially what you had to do is if you wanted an application, you would have to do a change order request, purchase order, so forth and so on. You'd have to order your equipment, order drives, so forth and so on. Then you gotta wait for someone to approve it. It's pretty much it's not on demand, right? You can't just go and grab some resources and set stuff up as quickly as you would like to. So pre-virtualization essentially was a headache. You have to coordinate between a lot of different folks and sometimes not just different departments within your company, but other companies outside of your company. So then we moved on to virtualization. So virtualization basically provides a logical division of resources. So all those things that we saw in the data center previously are basically we can do the same thing with them but they're logical. They're not necessarily physical devices, right? They're virtualized. There's two different types. You got type one, which is bare metal native, so you can take an actual physical machine and it's seen as a virtual machine per se. And then you've got type two hosted, where, so again with these two different types you have, we hosted essentially the hypervisor, I'm sorry, type one hypervisor, type two hypervisor, apologies for messing that up on y'all. So type one native hypervisors are those that are function at the bare metal level, right? They know what's going on with the actual physical components of that particular server. Type two hypervisors are hosted and those are kind of like your parallels, virtual box stuff like that, right? It's a virtualization technology that lives inside of an existing OS. Okay, you also got hardware virtualization, desktop virtualization, nested virtualization. So essentially virtualizing everything. So, in this what we're gonna do is we're moving towards, right, we're moving towards talking about OpenStack. And so essentially you have to have an idea of what makes something a cloud. Why is it considered a cloud? So essentially there's three things that will make something or what people will attribute it to be in a cloud. The first thing is that it's on demand, right? It's available as needed, it doesn't require third party. So you don't have to make those calls necessarily to folks outside of your organization or even sometimes other departments. You can essentially just get your resources or determine the resources you need and go and get them on demand. It's elastic, so it can grow, it can shrink as you need it to. Essentially, again, falling back on to the on demand thing. You don't have to necessarily, with growing shrink you essentially can, let's say today you need 100 servers and tomorrow you only need 50. Or in a few hours you need 100 servers and you only have 50 right now. So you can grow and you can shrink as you need within specific time frames and not have to go through all the hoops of contacting these different people and doing a lot of paperwork and trying to cut all these different red tapes everywhere. And also what makes a cloud is that it should be self-service. It's no physical deployment and consumer direct requests. Again, this all falls back to the not having to deal with other departments and a lot of red tape. Essentially I can go ahead and if I need to create a server, I don't have to go through the full cycle of physically deploying the server, plugging in network cabling, so forth and so on. I can essentially just say hey, I need a server that's got four CPUs, it's got 16 gigs of RAM and I make a request and in a few moments it's ready and it's available for me. So let's look at traditional computing stack. Essentially traditional computing stacks are managed end to end by an IT department. So there's no end user manipulation, as it says right there. You basically, everything from the top to the bottom of this stack is controlled by some IT department. You have to contact someone in every single aspect. So networking, storage, those are your infrastructure type things, servers, hypervisors, operating system, middleware, runtime, data, application. Each one of those aspects, whether you want to install PHP or something at the higher levels or you want to install a certain operating system, you have to contact someone in order to get that done, you couldn't do it yourself. So you got an IT department that handles all that, right? So infrastructure as a service is basically a service model that allows the cloud consumer to provision computing resources and software. So operating system and applications. The cloud consumer manages the images, operating system, applications, storage, networking, computing resources, VCPUs, VRAM. Right, so basically infrastructure as a service abstracts these bottom four layers for you. So you don't have to deal with networking, storage, server and hypervisor stuff. You come in at the operating system, middleware, runtime, data, application. So you can install whatever operating system you want. You don't have to worry about networking. That stuff is taken care of. You don't have to worry about storage. You essentially say I want this amount of storage. You don't have to worry about, again, specific servers or hypervisors that's all taken care of for you. All you care about is I want to be able to install an operating system on X amount of resources. So that's what infrastructure as a server offers you. All right, so moving further up, you got platform as a service. So platform as a service, right? It abstracts from you in addition to the bottom four layers, it also adds in runtime, middleware and operating system. So platform as a service provides the operating system, libraries, programming languages, et cetera, for you as a cloud consumer. The cloud consumer provides the application or applications to be deployed on the instances and the cloud consumer does not manage the underlying cloud infrastructure. So again, this is where, let's say for example, like I said, you want PHP. You just want to install a PHP application. You don't want to deal with whether it's PHP five point this or that, you don't want to deal with upgrades, so forth and so on of any of that kind of stuff. All you want to simply do is get a deployment that has specific technology in terms of, again, a library or programming language that you can use and it's made available to you and then you take care of the data and applications. You install whatever type of software you want to run PHP and you have your data as well. Sorry, your application at the end. So you're collecting data, you have your application that's running and that's all in your hands. So cloud computing now is at a place where you pretty much have anything as a service. So you have firewalls, you have load balancers. A lot of stuff is abstracted in terms of anything as a service. You've got desktops as a service. I mean, databases as a service, pretty much you name it, someone's out there got it as a service, right? There's even now Rackspace has OpenStack as a service. So anything as a service provides the operating system and all the software for you is a cloud consumer. The cloud consumer does not manage the underlying cloud infrastructure nor the application or applications contained in the instances, right? You essentially just say, hey, I want to be able to run a firewall, I want to be able to run OpenStack. I don't want to worry about any of the underlying infrastructure stuff all the way down to computing stack. I want you guys to take care of it. Just give me what I want and let me use it. Okay, so we've essentially gone from pre-virtualization and we've got into virtualization and then we've got an idea of what makes something a cloud and then also the three technologies or the three buzzwords in terms of clouds, infrastructures as a service, platform as a service and software as a service. So there's three types of clouds, right? There's public, there's private and there's hybrid. So the public cloud in a nutshell is generally the most well-known and straightforward type of cloud computing. This is like your AWS, right? So public commodity cloud providers typically offer convenience. It's easy for enterprises and developers to set up, use and access the public cloud. Additionally, scalability is often a driving factor for businesses utilizing public cloud because you can essentially spin up resources really quick, right? And you can spin them down really quick. However, this type of cloud is not without risk, okay? Then your private cloud for business is concerned about knowing exactly where their data is stored and having complete control over it. So who ultimately has access? That's what the private cloud is really concerned about. It provides a higher degree of peace of mind. Additionally, private clouds may be the best option for companies that must jump through a lot of regulatory hurdles or handle sensitive data or for companies concerned over their own intellectual property being hosted on the public cloud. Managed private clouds are one specific form, type of cloud computing. The service refers to clouds that though specific to an individual business, it receives some assistance such as operating system service, monitoring and patching from a third party provider, which is your managed provider. It's allows for a company to select the custom cloud model that fits its needs while leveraging secure third party help for maintenance. And hybrid cloud essentially is a mix of the two, right? So you're allowed to have your, in the case of Rackspace, actually we do private clouds in your data center, someone, a third party data center, or in ours. So you can essentially have a private cloud in one of those or as well as a public cloud with another service provider and you can, you know, mix and match services between those. So again, just to kind of highlight, to give a, you know, more of a synopsis of what I was just saying, again, public cloud is convenient with plenty of provider options, pay as you go model, use only what you want. You can, it can be somewhat unreliable and it is less secure because again, you're sharing resources, right? Private cloud concepts align with those of public cloud. It requires some IT expertise and you have more control over reliable, more control and reliability. And then hybrid cloud against flexible scalable provides a benefit of both public and private. It is cost effective because you can, you know, abstract some of those things that you would normally have to be doing with your private cloud or on-premise stuff to a public cloud provider. It's cost-effective and it is gaining some popularity, which, you know, I'm assuming most of you in here would agree with that. So again, here's kind of the assumption with private and hybrid clouds and public clouds. So you have a secure VPN connection that sits in between the two and at any point in time, you can move resources back and forth. So if you have something that's in your private cloud and you've got folks that are using it, it's starting to gain a lot of traction or let's say you have like an event and you need to say, hey, I need to shoot this application over to the public cloud real quick. I don't want to spin up a lot of my private cloud resources or I'm doing other stuff with them, but I want to be able to handle this mass increase in traffic or users. And so you can shoot your application over to the public cloud, spin up as many instances as you need for whatever that timeframe is that you'll have that increase and then spin it down and push it back over into your private cloud. So OpenStack is essentially infrastructure you like. So it's sitting down at the very base layer. It's the infrastructure as a service and there's also additional resources in terms of firewalls and service and so forth and so on. So OpenStack does give you a lot of the additional components but at the heart of it really it's infrastructure as a service related. Okay, so latest release is Mataka. All right, that's what we're all hoping to be using soon. Okay, OpenStack depends on a number of different services. So you can use Galera, Custon 4 or Morai DB for that matter, Prokona 4, your database. It needs some place to persist, right? Persist certain data and that's what these database options are for. It uses a messaging service as well, Cupid or RavidimQ. There's a couple others that you could use for essentially different services in OpenStack talk to one another. So you need a way to be able to, what we call cast and call. So send messages back and forth to make sure that some of those messages get received and also sometimes it's just, hey, send this message and I don't care if it gets received or not, just send it. All right, it also depends on memcast and certain instances as well as MongoDB for certain services. All right, so here's your core services. I thought that would be bigger than it is. So you've got sender, which provides your block storage. You've got Keystone. You've got Glance. Keystone, I'm sorry, does your identity. Glance is your image service. Nova does compute. Neutron does networking and Swift does object storage. Okay, so Keystone is a core component. Should be installed first. It provides identity management. It also provides a catalog of services and endpoints. And you can also connect it to external directory services like LDAP or Active Directory, for example. It's a very mature project. I like that the foundation has moved to this model where they actually show you adoption maturity and age for these different projects. And then also you can go and look at more details for each specific one that has this. And it gives you a bit more detail of where these specific items come into play. In other words, why does one have a 96 adoption an eight out of eight or five out of eight, whatever it may be, and its age. Essentially age is pretty self-explanatory. So OpenStack Nova is instance management. This is your virtual machine. So Nova takes care of that portion for you. It manages the life cycle of these instances, the spinning up and the spinning down of them, the destroying of them. In other words, making a creation of them. You have sales regions, aggregates, so this is basically being able to divide up your resources into different sections and apply metadata to those resources. Let's say like you have some servers that only have SSD drives in them.  Or you have some resources that have 40 gig internet connection versus something that may have 10 or 20. So you want to be able to spin up resources in specific places. And that's what sales regions and aggregates allow you to do. It also provides drivers for interaction. So if I'm not mistaken, it works like Hyper-V. It works with, you know, UKVM. So your different hypervisors that you have on the back end, Nova provides the drivers to be able to communicate with those. And it also defies some networking. Nova Networks, which is, some people are still using it. A lot of people are using Neutron Networking as well. So I guess that's kind of a matter of choice or relevant to your environment, which one you decide to use. All right, Neutron offers the network management, okay?  It manages the SDN infrastructure and you can leverage physical devices as well, like some companies like F5 and some others. They have, you know, plugins that you can install into Neutron that allow you to offset some of that, some networking throughput or concerns to those actual physical devices and not have to keep everything virtualized. Okay, then you have OpenStack Glance. This is your image management.  I wanna be able to use two CPUs and four gigs of RAM. So essentially, Glance is going to store these different images, right? These different flavors is gonna allow you to be able to say, how do you want to make your resources available on a per instance basis? Whenever a person requests something, how much of a, how much resources is that person gonna be made, it's gonna be made available to that person per instance that they spin up. Also, so it can also use object storage as a storage backend, so you don't have to store everything on actual physical hosts in terms of, like let's see, have an infrastructure or a controller host, you don't have to store your Glance images there, which would use up space essentially on that host, right? You can store your images in object storage, which is Swift. Okay, sender offers you block storage. I like to think of it basically as a USB drive that I plug in or an external hard drive I plug into my workstation. So you can plug it in there, you can unplug it, plug it into another one, so that's essentially what sender allows you is persistent storage. And again, also you can divide that up, decide how much you wanna make available to certain people with quotas and so forth and so on. And then our final one here is OpenStack Swift, which is object storage. Again, so basically what this does is you just, it's like, hey, I just wanna store some stuff. Small files essentially is what you wanna do. You don't wanna try to store too much stuff in there unless really you have to, but it's best utilized to just store small images. It's good for like, if you have a website and you have a lot of images, you wanna be able to store them off somewhere and provide a centralized location for websites to use those images. And then you have optional services. So that's your Solometer, Trove, which is Databases, Manila. So you have a whole lot of optional services that you can use, but again, the core services are what most people are going to install is what you wanna install and that pretty much will get you where you need to get to. So that, again, like I said, it's just a high level overview and won't get too deep into it because really 40 minutes is not a lot of time to dive a lot into too much stuff. I would prefer to leave time for questions, for queuing, people be able to talk a little bit. I know me personally, I like to be able to communicate. I know y'all don't wanna just hear me talk the whole time, right? So let's see if we can get into one of these instances. Everything's over there. So does anybody have any questions while I'm trying to figure out what we can do in terms of accessing an actual install? Any questions? How many people are new to OpenStack? Okay, how many people has installed OpenStack? All right, good. All right, y'all are currently running OpenStack in production? Anybody? All right. Okay, nobody has any questions about running OpenStack in production even? Maybe? Okay. Where to run OpenStack in production? I guess high availability is, I'm using, it's a key. So for OpenStack software itself, how do you handle the high availability because for your controllers and your agents? Yeah, so at Rackspace what we do is we use three infrastructure nodes and we install, we use OpenStack Ansible to install, install OpenStack and all the services are inside containers. So in terms of high availability, we have Rabbit MQ cluster, we use Galera for clustering, you know, MySQL, but essentially those three infrastructure nodes provide you that high availability. So if any service goes down in one particular node with the use of a load balancer, the traffic can be sent to another node, right, to handle that. Each particular service will give you its issues because some work better than others in certain types of high availability situations, but in general the three node setup that we have, the way that we do it, the reference implementation or architecture that we do, it works very well. A lot of people are using it, a lot of people are satisfied with it, but that's generally how we handle it. Does that answer your question? Does that answer your question? Yes, okay, great. Any other questions? I'm kind of fishing for questions because like, you know, you hate live demos, so I'm hoping this is gonna play well with me. So I'm trying to get y'all, you know, to give me some more questions. Anybody? So either I did a great job or I did terrible. That's what you're telling me, right? It's one or the other. All right, let's see if this one's gonna work for us.  How easy was it to install OpenStack? Did anybody do it using OpenStack Ansible or everyone did it by hand? Does that kind of counts?  So I guess that kind of works. Depends on what the real way is. Yeah, so I mean, when I first installed OpenStack, I did it by hand and it was quite difficult. I think a lot of people run to a lot of difficulties because there's so many different things you have to make sure you get right in order for it to work. And so if you don't, it can be a problem trying to figure out what exactly you missed. All right, so I'm gonna try to get this up on the screen here. We've only got a few minutes left to be with each other. Bless you, will that work, yeah. Is that big enough? I can't see it, unfortunately, on my end here, let's see. Is that my password? Yeah, I can't see y'all, I'm sorry. Thank you for being so helpful. All right, let's see here. So this is essentially, oh, it's not gonna work no more. Let's get this out of the way. OpenStack. All right, that's huge. That's really big. All right, I can't see that. Okay, so when you log in, this is a bad angle, I'm sorry y'all. So when you log in, oh. All right, so when you log in, okay, you essentially get sent to this page, right? So it shows you an overview of your resources. Essentially, if I had, so this is resource usage. This is not what you actually have, but this is what's been used thus far. And let's see. So I'm logged in as an admin. And you can see on the left-hand side here, your beautiful tabs will show you your projects. Okay, your particular project that you're in right now. Okay, and then you've got compute. This is your compute resources. Networking resources. Orchestration is heat. I am, of course, again, the admin. So it shows me all that. And then identity is where you are going to manage projects and users. And Rackspace, we're not even gonna click that right now because I'm not trying to sell you guys any product. All right, so let's look at an overview really quick here. Oh, we're already there. All right, so again, that's, so here this is showing us what limits we have. All right, so this is project limits. So right now I can only create 10 instances, 20 VCPUs I'll have available, 50 gigs of RAM, 50 floating IP addresses, and security groups. Anybody know what does not know what security groups are? Oh, okay, security groups are essentially basically where you can, it allows you what access you do or don't have into an instances in terms of ports. So let's say, for example, you don't want anybody accessing your instances. I'm sorry, you don't want anybody accessing port 80 if they're not from a specific IP address. So security groups can provide that for you. And the underlying technology is IP tables. All right, I should have mirrored this because I'm not really feeling this guys. I'm sorry. All right, so let's go ahead and see if this is gonna let me create an instance. Though I should already have one there. Yep, there's one. It's got a, this is gateway net, which is access to the public. This is inside net or basically the private network for this particular project. And I'm hoping this is gonna allow us to install an instance. So you just click launch instance. All right, you can see here, this is my availability zone. Again, remember I told you earlier you have aggregates. You've got sales regions, availability zone. So this is essentially, I can say, where do I wanna spin up this instance at? Okay, let's give it a name. Let's just call it Austin. We'll go with small. And as you can see there, the resources at the bottom are growing based on which particular flavor I select. So again, this is glance. All right, this is glance determining or allowing me to have access to these different flavors or basically amount of resources. Okay, we just want one. And then we're gonna boot this from an image. I believe we got some great image there, which is that's gonna be Ubuntu. All right, so I named it RPCO, so don't pay attention to that. All right, then you wanna select a key name. So depending on what kind of instance you actually install, you can or cannot have access to it outside of using a key, a public key, right? So Ubuntu by default, it doesn't allow you to log in via password. You need a key. So we created a key. And then there's these different security groups. We're just gonna select that one there. All right, then you wanna do networking. So we wanna have this on or it's called inside net. So again, this is providing this particular instance with an IP address within my project. We don't wanna do any post creation. So this is like it. So again, Ubuntu images, CentOS images will have a software called CloudyNet installed. So whenever you spin up the instance, you can utilize a file, basically a file that will tell CloudyNet to do certain things within that instance. Or you can also write direct input here, which is basically just put those CloudyNet commands inside of this box here and it'll take care of it for you. All right, and then this partitioning can just leave that automatic. And we're gonna see if it works. Fingers crossed, that it failed. See, so no valid host found. All right, so you'll see this error. Let's, you know, in our last few moments, let's talk about this no valid host found error. This is a not very explanatory error, right?  So what you wanna do is you wanna look at Nova schedule logs and see why didn't it find a valid host? Why wasn't it able to schedule whatever resources or whatnot for that specific instance and it couldn't find a valid host? I'm not gonna go through all that because, again, I can't see that well and this is not helping that well either. So, and we only got a few minutes left. But essentially that's it. So join us if you have any further questions. If we can talk anymore about anything, you're welcome to join me outside after the presentation for a few minutes. Give me your business card, I'll take yours gladly. And that's pretty much it. If we don't have any questions.