Kernelsight-AI: Intelligent Kernel Profiling and Bottleneck Detection in Cloud Infrastructure

Abstract

Modern cloud infrastructures are built on large scales and are very complex. So, even minor inefficiencies in the behavior of the kernel can lead to a significant drop in performance, latency, which is difficult to predict, and increase operational costs. Although the operating system kernel, which manages compute, networking, and storage, is of paramount importance, the current profiling tools are still fragmented, labor-intensive, and reactive rather than predictive. Moreover, they often record the symptoms instead of the causes, which results in engineers not having clear and actionable insights. KernelSight-AI solves the problem by launching an intelligent, end-to-end framework that automatically profiles kernel activity, detects anomalous patterns, and identifies system bottlenecks using machine-learning-driven inference. This framework combines minimal overhead kernel instrumentation with a responsive analytics engine that tracks and correlates scheduler behavior, I/O paths, memory access patterns, and syscall latency to locate performance hotspots in real time. Our experimental evaluation of KernelSight-AI across heterogeneous cloud workloads, including microservices, data-intensive pipelines, and container-orchestrated clusters, shows that the tool can trace bottlenecks that are barely visible and overlooked by traditional profilers. For example, it reveals subtle contention on shared kernel locks, inefficient context switching caused by noisy neighbors, and cache-level interference under bursty traffic. These results indicate that the throughput of the workload can be increased by up to 27% and tail latency can be significantly reduced when the system's recommendations are implemented. Additionally, the research points to the benefits of automating kernel-level diagnostics apart from the performance improvements. Thus, engineers get the time that they would have spent on going through logs and tracing data to perform other tasks and operators receive early warnings about the issues that will become service disruptions if they are not dealt with. KernelSight-AI, by merging intelligent profiling with automated detection and interpretable insights, stands as a viable solution for the realization of cloud platforms that are more resilient, efficient, and capable of self-optimization. As a result, the tool radically changes the way performance engineering is done in modern distributed ​‍​‌‍​‍‌environments.