Liuhua Chen
ABSTRACT
To provide robust infrastructure as a service (IaaS), clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, in this paper, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of Service Level Agreement (SLA) violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time, energy) and delay by quickly identifying VMs and destination PMs to migrate. Our trace-driven experiments show that our algorithm outperforms both previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency and long-term load balance maintenance.
- Microsoft Azure. http://www.windowsazure.com.Google Scholar
- BEA System Inc. http://www.bea.com.Google Scholar
- Amazon. Amazon Web Service. http://aws.amazon.com/.Google Scholar
- E. Arzuaga and D. R. Kaeli. Quantifying load imbalance on virtualized enterprise servers. In Proc. of WOSP/SIPEW, 2010. Google ScholarDigital Library
- R. Bellman. Dynamic Programming. Princeton University Press, 1957. Google ScholarDigital Library
- A. Beloglazov and R. Buyya. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. CCPE, 24(13):1397--1420, 2011. Google ScholarDigital Library
- A. Beloglazov and R. Buyya. Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. TPDS, 24(7): 1366--1379, 2013. Google ScholarDigital Library
- N. Bobroff, A. Kochut, and K. Beaty. Dynamic placement of virtual machines for managing sla violations. In Proc. of IM, 2007.Google ScholarCross Ref
- R. N. Calheiros, R. Ranjan, A. Beloglazov, C. De Rose, and R. Buyya. Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. SPE, 41(1):23--50, 2011. Google ScholarDigital Library
- A. Chandra, W. Gong, and P. J. Shenoy. Dynamic resource allocation for shared data centers using online measurements. In Proc. of SIGMETRICS, 2003. Google ScholarDigital Library
- L. Chen, H. Shen, and S. Sapra. RIAL: Resource intensity aware load balancing in clouds. In Proc. of INFOCOM, 2014.Google ScholarCross Ref
- Z. Gong, X. Gu, and J. Wilkes. PRESS: Predictive elastic resource scaling for cloud systems. In Proc. of CNSM, 2010.Google Scholar
- GoogleTraceWebsite. Google cluster data. https://code.google.com/p/googleclusterdata/.Google Scholar
- R. A. Howard. Dynamic Programming and Markov Processes. MIT Press, 1960.Google Scholar
- D. Kondo, B. Javadi, P. Malecot, F. Cappello, and D. P. Anderson. Cost-benefit analysis of cloud computing versus desktop grids. In Proc. of IPDPS, 2009. Google ScholarDigital Library
- M. Lauri and E. Brad. Energy Efficiency for Information Technology: How to Reduce Power Consumption in Servers and Data Centers. Intel Press, 2009.Google Scholar
- A. Sallam and K. Li. A multi-objective virtual machine migration policy in cloud systems. The Computer Journal, 57(2):195--204, 2013.Google ScholarCross Ref
- U. Sharma, P. J. Shenoy, S. Sahu, and A. Shaikh. A cost-aware elasticity provisioning system for the cloud. In Proc. of ICDCS, 2011. Google ScholarDigital Library
- Z. Shen, S. Subbiah, X. Gu, and J. Wilkes. CloudScale: Elastic resource scaling for multi-tenant cloud systems. In Proc. of SOCC, 2011. Google ScholarDigital Library
- A. Singh, M. R. Korupolu, and D. Mohapatra. Server-storage virtualization: integration and load balancing in data centers. In Proc. of SC, 2008. Google ScholarDigital Library
- M. Tarighi, S. A. Motamedi, and S. Sharifian. A new model for virtual machine migration in virtualized cluster server based on fuzzy decision making. CoRR, 1(1):40--51, 2010.Google Scholar
- T. Wood, P. J. Shenoy, A. Venkataramani, and M. S. Yousif. Black-box and gray-box strategies for virtual machine migration. In Proc. of NSDI, 2007. Google ScholarDigital Library
- T. Wood, P. Shenoy, A. Venkataramani, and M. Yousif. Sandpiper: Black-box and gray-box resource management for virtual machines. Computer Networks, 53(17):2923--2938, 2009. Google ScholarDigital Library
Index Terms
- Distributed Autonomous Virtual Resource Management in Datacenters Using Finite-Markov Decision Process
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