NDLI: PCSsampler: Sample-based, Private-state Cluster Scheduling

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PCSsampler: Sample-based, Private-state Cluster Scheduling

Content Provider	ACM Digital Library
Author	Shen, Jie Zhang, Heng Chen, Celia Wu, Yanjun Hao, Chunliang Li, Mingshu
Abstract	As a promising alternative to centralized scheduling, sample-based scheduling is especially suitable for high fan-out workloads that contain a large number of interactive jobs. Compared to centralized schedulers, existing sample-based schedulers do not hold a global view of the cluster's resource status. Instead, the scheduling decisions are made solely based on the status of a small set of randomly sampled workers. Although this simple approach is highly efficient in large clusters, the lack of global knowledge of the cluster can lead to sub-optimal task placement decisions and difficulties in enforcing global scheduling policies. In this paper, we address these challenges in existing sample-based scheduling approaches by allowing the scheduler to maintain an approximate version of the global resource status through caching the worker node's status extracted from reply messages. More specifically, we introduce the private-cluster-state technique (PCS) for the scheduler to obtain such global information. We show that the scheduler can make better scheduling decisions by utilizing PCS and the scheduler can become more capable in enforcing global scheduling policies. The use of PCS is of low cost since it does not initiate new communication in sample-based scheduling. Our approach is implemented in PSCSampler, a full distribute sample-based scheduler, which gains global knowledge from PCS. Experiment results from both simulation runs and Amazon cluster runs show that compared to Sparrow, PCSsampler can significantly reduce both 50th percentile and 90th percentile runtime. The firsttime success rate of PCSsampler in gang scheduling is closer to an omniscient centralized scheduler than baseline sample based scheduler.
Starting Page	599
Ending Page	608
Page Count	10
File Format	PDF
ISBN	9781509066100
DOI	10.1109/CCGRID.2017.50
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-14
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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