Senser Unveils AIOps Platform Using eBPF to Collect Data

Senser emerged from stealth this week to launch an artificial intelligence for IT operations (AIOps) platform that leverages extended Berkeley Packet Filter (eBPF) running in the microkernel of Linux operating systems to collect data from IT environments.

Fresh from raising $9.5 million in funding, Senser CEO Amir Krayden said the company’s namesake platform then applies machine learning algorithms to that data to identify issues that could lead to outages. Those insights are surfaced using graph technology to make it simpler to both observe IT environments and triage issues at scale because the AIOps platform is running processes at the microkernel level rather than in user space.

The approach provides IT teams with a more efficient and holistic approach to observability at a level of scale legacy platforms can’t achieve, said Krayden.

The use of machine learning algorithms also reduces the cognitive load on DevOps teams because issues involving, for example, performance degradations are automatically surfaced, he added. In addition, the company is working toward adding generative AI capabilities to provide summaries that explain what IT events have occurred, noted Krayden.

In effect, eBPF changes the way operating systems are designed because it enables networking, storage and observability software to scale at much higher levels of throughput since they are no longer running in user space. That’s especially critical for observability and AIOps platforms that need to dynamically process massive amounts of data in near-real-time.

As the number of organizations running the latest versions of Linux continues to increase, more hands-on experience with eBPF will be gained. IT teams may not need to concern themselves with what is occurring in the microkernel of the operating systems, but they do need to understand how eBPF ultimately reduces the total cost of running IT at scale.

AI and graph technology, in combination with eBPF, will fundamentally change how IT is implemented and managed. The current complexity of application environments is already exceeding the ability of IT teams to cost-effectively manage them at scale, so the need for a different approach is already apparent. Many IT environments are already too complex for IT personnel to manage without the help of some form of AI.

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It’s not clear precisely how much AI will automate the management of IT, but it’s not likely the need for humans to manage and supervise these environments will happen any time soon. However, the level of scale at which an IT environment can be effectively managed is changing as AI makes it easier to identify issues and understand their impact. Too often today, there are simply too many dependencies within an IT environment to keep track of using legacy monitoring tools that only track a set of pre-defined metrics.

It may be a while before AI is pervasively employed across IT environments, but it’s now more a question of when rather than if. The issue now is determining where the interface between the humans and the machines that are jointly managing IT environments lies.