Shmuel Kliger on Causely’s Integration with OpenTelemetry

This is Textron tv. Hey everyone, welcome back here to techron tv. We're happy to you, you're with us, our next guest.

It's his first time on here, techron tv. We, we featured this company once before, though. I think it was at a cube con or something, but we're gonna find, if you didn't see that one, don't worry.

I'm not gonna hold it against you. We're gonna bring you up to speed quick. Let me introduce you to Schmuel Kleger.

Schmuel is the founder of a company called Causley, and he joins us today from New York. Schmuel, welcome to Text Drug tv. It's nice to have you on.

Thank you for having me. It's a pleasure. Um, so Sel, I always like, you know, I, I founded, co-founded a few companies in my days too, and I always say, you gotta be a little crazy to found your own company, right?

It, it's taken a risk. It's, you, you put your, your blood, sweat and your tears, your kishkes, as they say in, in, uh, New York, in into these things. And the only way you do it is because you're passionate.

You, you believe that somehow what you're doing in some way is gonna make the world better for somebody or some people, people. What? Talk to us about your journey and where your passion came from for cosley.

Okay. So before cosley, I founded Cosley is actually my third startup. Uh, before Cosley, I, uh, start, I founded, uh, a company called Omic.

I started omic in 2009, uh, focusing on, uh, application resource management in virtualized and cloud environment. Uh, that company, uh, uh, became, uh, deleting providers of application resource management. And it was acquired by IBM in 2021 for $2 billion.

Prior to that, I was a CTO. At EMCI was the CTO of the resource management software group. I arrived to EMC in oh five as part of an acquisition of a company called Smarts.

Uh, I was A-A-C-T-O and a co-founder of Smarts. We founded Smarts in 93, focused on becoming the leading providers of root cause analysis, focusing on networks. And we were acquired by EMC in all five for $300 million.

Prior to that, I was a researcher at IBM Research in TJ Watson. I arrived there to do my postdoc, I did my PhD in compilation of logic programing, concurrent logic programming languages, which were the foundation of AI during the first type of AI of the, the first type of ai. And, uh, if you go back all the way, I started my career as a system programmer on mainframes in the Israel, in the Israeli army.

So now why go? And you ask me, where's my passion to Cosley? Because if you look at my entire career with, with the break for my PhD, it's focused on IT management, IT operation.

And in the two startups that I had, uh, focusing on one on the side of how do I automate the troubleshooting? And in the, in the TUR omic, I focused on how to automate the resource allocation. And, but it's all within the same journey of trying.

I always believe that IT operation is a very labor intensive, uh, uh, part of the market. And there is room for reducing the label and, and, uh, gets software to do a lot of things that people are doing today. They shouldn't be doing engineers and, and IT operation in general.

And I like to say that both in smarts and in omic, we made some good steps towards that, but we actually didn't get to what I would call the nirvana in which, and still IT operation is still a very labor intensive. Humans are very much involved in every little details of the operating of the IT and making sure that everything is op, uh, working and applications are delivering on their, uh, goals. And, and, uh, so there is, I like to say there is something left for me to prove that we can do better when it comes to how to operate an environment in a way that applications are performing well.

Excellent. First of all, congratulations. What a great, what a great life.

A story, right? Man, that that is, you know, it's a, for a lot of people out here, it's a dream and it's, it doesn't, I'm sure it doesn't come easy. It comes with hard work, smart being patient and doing and, and working hard.

So, congratulations. Thank you. When, when did you found Causley?

We founded causley in, uh, 2222. 'cause you know, as I was listening to you about making things less labor intensive, of course, pretty much since 22, 23, you know, AI comes out, a gen AI burst on the scene. And a lot of, a lot of executives are thinking, how can we do things more in software more with AI and less labor intensive.

Of course, you know, labor probably represents one of the biggest, uh, cogs in, you know, costs in the business. And, um, was, was AI kind of on the radar when you thought about this? Or you were just thinking more software in general?

And automation? I, this is a very good question. Uh, well, AI is there for 30 years.

It's it, yes. And it goes to, to multiple hypes. And, uh, uh, if you look at the, in my first company, uh, the, uh, we also kind of ask ourself is that AI or not AI in ev and actually in both companies, every time, my view is I am solving, I'm building company to solve a problem, whatever the problem is.

And we can talk about the problem we are solving in cosley. And for, to solve that problem, you need to develop some algorithm that solve that problem. Just saying, I'm doing AI is like, it doesn't tell me much.

I you have to have some algorithm that solves the problem that you're trying to solve. And what we are building in costly is that a collection of algorithms that works together to solve a problem, you want to label ai, label it ai, uh, are there elements of AI that we are using? Yes.

But I wouldn't call it like, it's not like that what we do in LY is, oh, here is a bunch of data to it into some LLM and the LLM will give you the answer. I don't believe that that's where we have to go. LLM can help in certain areas, or machine learning in general can help in certain areas, but it's not like a magic bullet that you just throw everything to it and it gives you the answer.

Uh, you have to kind of pick and choose where do you use it to improve some of the answers that you are providing. Got it. Excellent.

Um, so give us, you know, so we, we get the reason behind Causley since 22. Give us an idea of the engagement of, you know, what, like typical customer where, where's, what's the problem that the customer comes to you with, that you're, you know, your typical customer now, kind of the persona, you know, that you causally the great answer for, for, right. So I, so I can, I can answer that in so many levels, but let me be very direct, very, if you are a customer and you try to make sure that the applications are performing, so what do you do?

You monitor the environment, you deploy some kind of monitoring, whether it's uh, uh, native stuff that you can get in cloud native environment like so, uh, or things like that. Sure. Maybe open telemetry, maybe you buy some tools that gives you some, uh, some a PM tools, whatever you are monitoring the environment and okay, what do you monitor you monitoring because you care about the performance.

You monitoring service latency, you are monitoring for the, the error rates and so on and so forth. And now the reality is because especially with cloud native and microservices, that with this complex web of relationship and dependencies is the reality is that when an issue happen in the environment, something doesn't behave, something doesn't go, doesn't operate the way it's supposed to operate, whether it's an API, someone that is, is API, somewhere that is slow, a database that is locked in some, uh, wrong way, uh, no that is overloaded, whatever. When those type of things happening in the environment and you are monitoring the environment, you are not getting one alert, one anomaly, oh, this service is slow or have high latency, you get a flood of alerts, you get all kind of services are having high error weights or high latency, and then you start chasing it and you start firefighting it, you are going to this what we call the troubleshooting process to actually pinpoint what is the root cause of this flood of services that are having high errors now or, or high latency.

And with Causley, we are automating this, we are telling you don't chase those alerts. Causley tells you pinpoint, this is the root cause. You don't have to underst to chase those alerts.

We tell you, this is the root cause. All of those alerts are caused by the, all of those anomalies that you observe are caused by this root cause. Got it.

You know, and, and this, what you just described is the, the poster child for observability, right? We used to call it application performance management and you know, all these other things. But, but this is what, what what people are, are trying to do now and or they've always tried to do it, but now we call it observability.

Um, but which brings us go ahead. But what they are missing is the key ingredients. They are, they're missing the understanding of what I call causality.

What is the cause and effect relationship between things, what you observe, whatever the things are. And to be honest, there is a lot of hype around in the industry of those cause and effect relationships. I'll give it to LLM and we learn them.

I actually comes from a school that says those cause and effect relationships are not so easily learned by a machine. There is some knowledge and some expertise that someone has to input the machine and let the machine do the less. But the key for what we are doing is start with some understanding of this cause and effect relationship and let them drive that the algorithms that makes the decisions and pinpoint the root causes.

And without that, uh, people are not really doing root causes in software. The, the best they can do is correlating events, but they leave the human the heavy lifting of really understanding what is the root cause and make the decision of what is the root cause instead of letting the system, the software make that decision. In some ways, fmi s we we're coming back full circle, back to root cause analysis to what you Did.

Yeah. I, years And years Ago, say that I, I like to say that, that this problem exists from the day we invented computers. It, this problem didn't start today.

This problem exists, uh, forever. And we are struggling with that problem forever, if you want, from the day we invented computers. Absolutely.

Hey, we're, I gotta pivot a little bit 'cause we'll run outta time before we even talk what we're supposed to talk about, which is, uh, today's topic of discussion. You just recently, or Causley just recently announced launching integration with the Open Telemetry Project and the Open Telemetry system. Talk to us about that, if you don't mind.

Yeah. So I think open telemetry is, uh, a very, very important paradigm shift that happens in the, uh, in the, if you want, in the observability space. It, it, it sees, uh, shifting the accountability and the ownership of telling me what's going on from the management station on the man, from the management software to the application itself, to the application developer.

That if the application needs to, if the application to be properly managed, it needs to be properly instrumented with open Telemetry. Using Open telemetry. We are finally putting the burden on the application to tell management, here I am, here is what whom I'm talking to, and here is some metrics about, uh, the characteristics of my conversation with, with things in the environment.

And that's a huge paradigm shift and it's a very important paradigm shift because now we have a lot of information, valuable information about the application, the of whom it, whom does it talk to, how does it perform, and things like that. But I like to say there's no free lunches. This comes with the cost because if I'm going to instrument properly my application now I'll overwhelm myself with a lot of information, tons of data, which brings with it some challenges.

Obviously it brings the challenges of just the bell cost of PO processing and storing this data. But more important, if you think about the problem that we are solving, which is the root cause analysis problem, it's actually make this problem even worse because at some level, root cause analysis at the very fundamental po uh, level of this is like looking for a needle in a haystack. And the open telemetry making the stack much larger, the haystack much larger and much bigger.

So look, finding the needle within that haystack becomes much harder. So, so that's why Open Parameter brings tremendous amount of value and important value. It's actually something that I wrote about that in the nineties that we have to shift for the application telling us who they are and what they do.

But you need system like Causley that can make sense, takes what's important and be able to get the insights that you need out of the data that is being collected by Open til Energy. I Love it. Screw, we're almost out of time.

For people who wanna get more information on Causley, where do you suggest they go? ai and ai. Yes.

Okay. And that's where you find Causley. What about, you're working with Open Telemetry, you're gonna be a Cube car, you're gonna be where, where, where can people be beyond the website?

So we are, what's a good way to interact? We Actually, we are working with Open Telemetry. We actually, uh, contributing to, uh, the Bailer project, which is an open source project that, that, uh, uh, uh, build that con uh, that provide the information about service dependencies and traces and things like that.

Uh, as for conferences will be in the SLE Con will be mm-hmm. In, uh, uh, human Acts, uh, yeah. Things like that.

Uh, to be honest, I'm not sure if we are in, you Are not the, you are not the event coordinator. Yes, I'm sure not. I think Adam, yeah, Adam probably knows more than me where we are going.

We'll, we'll try to put it up there. Well, listen, it's been a pleasure having you on here. You've got an open invitation.

Anytime you want to come on and talk about stuff, what's going on? I'd love to have you. Maybe next time I'm in New York.

We'll, we'll do it in person. I Would love to. I would love to.

Thank you so much. Thank you. ai.

Go check it out. They just got a new integration with Open Telemetry. We're gonna take a break here on Textron tv.

We'll be back in just a moment.