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Cloud Performance Modeling with Benchmark Evaluation of Elastic Scaling Strategies.

Authors :
Hwang, Kai
Bai, Xiaoying
Shi, Yue
Li, Muyang
Chen, Wen-Guang
Wu, Yongwei
Source :
IEEE Transactions on Parallel & Distributed Systems. Jan2016, Vol. 27 Issue 1, p130-143. 14p.
Publication Year :
2016

Abstract

In this paper, we present generic cloud performance models for evaluating Iaas, PaaS, SaaS, and mashup or hybrid clouds. We test clouds with real-life benchmark programs and propose some new performance metrics. Our benchmark experiments are conducted mainly on IaaS cloud platforms over scale-out and scale-up workloads. Cloud benchmarking results are analyzed with the efficiency, elasticity, QoS, productivity, and scalability of cloud performance. Five cloud benchmarks were tested on Amazon IaaS EC2 cloud: namely YCSB, CloudSuite, HiBench, BenchClouds, and TPC-W. To satisfy production services, the choice of scale-up or scale-out solutions should be made primarily by the workload patterns and resources utilization rates required. Scaling-out machine instances have much lower overhead than those experienced in scale-up experiments. However, scaling up is found more cost-effective in sustaining heavier workload. The cloud productivity is greatly attributed to system elasticity, efficiency, QoS and scalability. We find that auto-scaling is easy to implement but tends to over provision the resources. Lower resource utilization rate may result from auto-scaling, compared with using scale-out or scale-up strategies. We also demonstrate that the proposed cloud performance models are applicable to evaluate PaaS, SaaS and hybrid clouds as well. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10459219
Volume :
27
Issue :
1
Database :
Academic Search Index
Journal :
IEEE Transactions on Parallel & Distributed Systems
Publication Type :
Academic Journal
Accession number :
111881297
Full Text :
https://doi.org/10.1109/TPDS.2015.2398438