Providing fairness on shared-memory multiprocessors via process scheduling

Authors:
Di Xu;Chenggang Wu;Pen-Chung Yew;Jianjun Li;Zhenjiang Wang
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences & Graduate University of Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;University of Minnesota at Twin-Cities, Minneapolis, MN, USA;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Year:
2012

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Abstract

Competition for shared memory resources on multiprocessors is the most dominant cause for slowing down applications and makes their performance varies unpredictably. It exacerbates the need for Quality of Service (QoS) on such systems. In this paper, we propose a fair-progress process scheduling (FPS) policy to improve system fairness. Its strategy is to force the equally-weighted applications to have the same amount of slowdown when they run concurrently. The basic approach is to monitor the progress of all applications at runtime. When we find an application suffered more slowdown and accumulated less effective work than others, we allocate more CPU time to give it a better parity. Our policy also allows different weights to different threads, and provides an effective and robust tuner that allows the OS to freely make tradeoffs between system fairness and higher throughput. Evaluation results show that FPS can significantly improve system fairness by an average of 53.5% and 65.0% on a 4-core processor with a private cache and a 4-core processor with a shared cache, respectively. The penalty is about 1.1% and 1.6% of the system throughput. For memory-intensive workloads, FPS also improves system fairness by an average of 45.2% and 21.1% on 4-core and 8-core system respectively at the expense of a throughput loss of about 2%.