The article explains how Linux’s multicore scheduler distributes tasks across CPU cores, describes the core CFS and real‑time algorithms, details load‑balancing mechanisms such as pull/push and active/passive strategies, and discusses power, thermal, and algorithmic optimizations for servers and embedded devices.
This article explains how Linux’s real‑time scheduler works, covering the underlying concepts, key kernel data structures such as rt_rq and rt_prio_array, the SCHED_FIFO and SCHED_RR policies, practical code examples, configuration APIs, and optimization tips for industrial and multimedia applications.
This article explains the fundamentals of CPU scheduling in Linux, covering real-time scheduling for high-priority tasks and the fair scheduling algorithm for user processes, including code examples.
The article details Linux’s real‑time thread CPU‑selection process, explaining how priorities (0‑99 for rt, 100‑139 for CFS) set the sched_class, how functions like select_task_rq_rt, find_lowest_rq, cpupri_find_fitness and __cpupri_find build a candidate CPU mask, and how the final CPU is chosen by previous affinity, domain order, wake‑up CPU or random selection, while warning that excessive rt tasks can increase power use and cause scheduling delays.
The article surveys traditional elevator dispatching methods such as FCFS, SSTF, SCAN, LOOK, and SATF, then examines real‑time strategies like EDF, SCAN‑EDF, PI, and FD‑SCAN, and concludes with a discussion of modern group‑control research and detailed system requirement analysis.
This article introduces Ele.me's intelligent scheduling platform, focusing on the pressure‑balancing subsystem and demonstrating how machine‑learning models such as rider capacity estimation and team pressure‑coefficient prediction are designed, trained, and deployed to improve real‑time O2O delivery operations.
Meituan’s delivery team leverages massive real‑time data mining, distributed parallel optimization and a simulation platform to intelligently assign orders, boost rider efficiency, cut costs, and continuously refine algorithms, while integrating offline‑online learning and upstream‑downstream coordination to enhance overall logistics performance.
This article examines Uber's rapid 38‑fold growth and the engineering choices behind its real‑time market platform, detailing the scheduling system, geographic indexing, microservices, Ringpop, TChannel, and strategies for scalability, availability, and fault tolerance.
