Performance Isolation of Data-Intensive Scale-out Applications in a Multi-tenant Cloud

被引:15
|
作者
Lama, Palden [1 ]
Wang, Shaoqi [2 ]
Zhou, Xiaobo [2 ]
Cheng, Dazhao [3 ]
机构
[1] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
[2] Univ Colorado, Dept Comp Sci, Colorado Springs, CO 80907 USA
[3] Univ N Carolina, Dept Comp Sci, Charlotte, NC USA
来源
2018 32ND IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS) | 2018年
关键词
performance isolation; resource scheduling; cloud;
D O I
10.1109/IPDPS.2018.00019
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Data-intensive applications often suffer from performance variability and degradation in the cloud due to intrinsically complex problem of performance interference that arises from multi-tenancy. Although application-level approach of straggler mitigation for scale-out data processing frameworks such as MapReduce and Spark, address the issue to some extent, they incur extra resource and often react after tasks have already slowed down. In this paper, we present PerfCloud, a novel system software that utilizes system level performance metrics for early detection of performance interference in a multi-tenant cloud, and provides non-invasive performance isolation through fine-grained resource control. Unlike existing works, PerfCloud does not require time-consuming workload profiling, or intrusive modification of the application framework and the operating system. We implemented PerfCloud on NSF Cloud's Chameleon testbed using KVM for virtualization, and OpenStack for cloud management. Experimental results with Hadoop MapReduce and Spark benchmarks show that PerfCloud effectively reduces their job completion time, decreases performance variability, and improves resource utilization efficiency while minimizing the performance degradation of other colocated VMs.
引用
收藏
页码:85 / 94
页数:10
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