Performance-aware Speculative Resource Oversubscription for Large-scale Clusters

Yang, Renyu and Sun, Xiaoyang and Hu, Chunming and Garraghan, Peter and Wo, Tianyu and Wen, Zhenyu and Peng, Hao and Xu, Jie and Li, Chao (2020) Performance-aware Speculative Resource Oversubscription for Large-scale Clusters. IEEE Transactions on Parallel and Distributed Systems, 31 (7). pp. 1499-1517. ISSN 1045-9219

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Abstract

It is a long-standing challenge to achieve a high degree of resource utilization in cluster scheduling. Resource oversubscription has become a common practice in improving resource utilization and cost reduction. However, current centralized approaches to oversubscription suffer from the issue with resource mismatch and fail to take into account other performance requirements, e.g., tail latency. In this paper we present ROSE, a new resource management platform capable of conducting performance-aware resource oversubscription. ROSE allows latency-sensitive long-running applications (LRAs) to co-exist with computation-intensive batch jobs. Instead of waiting for resource allocation to be confirmed by the centralized scheduler, job managers in ROSE can independently request to launch speculative tasks within specific machines according to their suitability for oversubscription. Node agents of those machines can however avoid any excessive resource oversubscription by means of a mechanism for admission control using multi-resource threshold control and performance-aware resource throttle. Experiments show that in case of mixed co-location of batch jobs and latency-sensitive LRAs, the CPU utilization and the disk utilization can reach 56.34% and 43.49%, respectively, but the 95th percentile of read latency in YCSB workloads only increases by 5.4% against the case of executing the LRAs alone.