ADVISE – A Framework for Evaluating Cloud Service Elasticity Behavior
Complex cloud services rely on different elasticity control processes to deal with dynamic requirement changes and workloads. However, enforcing an elasticity control process to a cloud service does not always lead to an optimal gain in terms of quality or cost, due to the complexity of service structures, deployment strategies, and underlying infrastructure dynamics. Therefore, being able, a priori, to estimate and evaluate the relation between cloud service elasticity behavior and elasticity control processes is crucial for runtime choices of appropriate elasticity control processes. In this paper we present ADVISE, a framework for estimating and evaluating cloud service elasticity behavior. ADVISE gathers service structure, deployment, service runtime, control processes, and cloud infrastructure information. Based on this information, ADVISE utilizes clustering techniques to identify cloud elasticity behavior produced by elasticity control. Our experiments show that ADVISE can estimate the expected elasticity behavior, in time, for different cloud services thus being a useful tool to elasticity controllers for improving the quality of runtime elasticity control decisions.
KeywordsCloud Service Elasticity Behavior Cloud Provider Cloud Platform Cloud Application
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- 1.Al-Shishtawy, A., Vlassov, V.: Elastman: Autonomic elasticity manager for cloud-based key-value stores. In: Proceedings of the 22nd International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2013, pp. 115–116. ACM, New York (2013)Google Scholar
- 2.Wang, W., Li, B., Liang, B.: To reserve or not to reserve: Optimal online multi-instance acquisition in IaaS clouds. Presented as part of the 10th International Conference on Autonomic Computing, Berkeley, CA, USENIX, pp. 13–22 (2013)Google Scholar
- 3.Verma, A., Kumar, G., Koller, R.: The cost of reconfiguration in a cloud. In: Proceedings of the 11th International Middleware Conference Industrial Track. Middleware Industrial Track 2010, pp. 11–16. ACM, New York (2010)Google Scholar
- 4.Zhang, L., Meng, X., Meng, S., Tan, J.: K-scope: Online performance tracking for dynamic cloud applications. Presented as part of the 10th International Conference on Autonomic Computing, Berkeley, CA, USENIX, pp. 29–32 (2013)Google Scholar
- 5.Shen, Z., Subbiah, S., Gu, X., Wilkes, J.: Cloudscale: elastic resource scaling for multi-tenant cloud systems. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC 2011, pp. 5:1–5:14. ACM, New York (2011)Google Scholar
- 6.Moldovan, D., Copil, G., Truong, H.L., Dustdar, S.: Mela: Monitoring and analyzing elasticity of cloud services. In: 2013 IEEE Fifth International Conference on Cloud Computing Technology and Science, CloudCom (2013)Google Scholar
- 7.OASIS Committee Specification Draft 01: Topology and Orchestration Specification for Cloud Applications Version 1.0 (2012)Google Scholar
- 9.Trihinas, D., Pallis, G., Dikaiakos, M.D.: JCatascopia: Monitoring Elastically Adaptive Applications in the Cloud. In: 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (2014)Google Scholar
- 12.Li, A., Zong, X., Kandula, S., Yang, X., Zhang, M.: Cloudprophet: towards application performance prediction in cloud. In: Proceedings of the ACM SIGCOMM 2011 Conference, SIGCOMM 2011. ACM, New York (2011)Google Scholar