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This article experimentally examines how thread count, CPU‑bound versus I/O‑bound workloads, and context‑switch overhead affect CPU utilization in Java, presents a practical formula for estimating optimal threads, and advises testing and tuning rather than relying on fixed rules.
Performance testing involves monitoring various metrics to assess system behavior under different conditions, including response time, throughput, CPU usage, memory utilization, and error rates.
Xiaohongshu’s cloud‑native platform adopted a four‑stage mixed‑workload scheduling strategy—reusing idle nodes, whole‑machine time‑sharing, normal mixed pools, and a unified scheduler (Tusker) that coordinates CPU, GPU and memory across Kubernetes and YARN—boosting average cluster CPU utilization from under 20 % to over 45 % and delivering millions of low‑cost core‑hours while preserving QoS for latency‑sensitive, mid, and batch jobs.
This article explains how to size a thread pool by understanding CPU core limits, I/O wait effects, empirical testing on a multi‑core machine, and a practical formula, while emphasizing that real‑world workloads require iterative performance testing to find the best thread count.
This article presents the design and evaluation of the Remote Aware Load Balance (RALB) algorithm applied to JD’s search‑recommendation architecture, describing its CPU‑centric load‑balancing principles, implementation details, functional verification, throughput and boundary testing, and the observed improvements in CPU utilization and overall system performance.
This article explains how to correctly obtain CPU utilization inside containers, compares host and container metrics, describes the use of lxcfs and cgroup files (including cgroup V1/V2) for accurate measurement, and clarifies why container statistics omit nice, irq, and softirq fields.
This article explains how Linux calculates CPU utilization shown by the top command, detailing the role of /proc/stat, kernel sampling mechanisms, and the handling of user, nice, system, IRQ, soft‑IRQ, iowait and idle times, while providing code examples and practical insights.
This article explains what Linux load average actually measures, distinguishes it from CPU utilization, and demonstrates how to interpret and monitor load with practical stress‑test scenarios using tools like uptime, mpstat, and pidstat.
Tencent’s TLinux team introduced a kernel‑level mixed‑deployment framework that adds an offline scheduling class and load‑balancing algorithm, enabling online tasks to instantly pre‑empt offline work and boosting whole‑machine CPU utilization to as high as 90% while preserving latency‑sensitive service performance.
This article explains that high concurrency is fundamentally about efficiently squeezing CPU resources, discusses the evolution of concurrency models, presents a control‑variable analysis of HTTP request flow, and compares PHP‑Swoole with Java‑Netty through detailed benchmark results and code examples.
This article explains why the traditional %CPU metric can be deceptive, describes how CPU stalls and memory bottlenecks affect perceived utilization, and offers practical guidance on using IPC and performance counters to obtain a more accurate view of system performance.
The article explains how Unix-like systems calculate load average using an exponentially damped weighted moving average, how the values reflect CPU and I/O contention on single- and multi‑CPU machines, and why different kernel implementations may count processes and threads differently, affecting performance monitoring.