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A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems
March 2014 (vol. 25 no. 3)
pp. 560-569
Dario Bruneo, Università di Messina, Contrada di Dio
Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of cloud computing infrastructures is required to predict and quantify the cost-benefit of a strategy portfolio and the corresponding quality of service (QoS) experienced by users. Such analyses are not feasible by simulation or on-the-field experimentation, due to the great number of parameters that have to be investigated. In this paper, we present an analytical model, based on stochastic reward nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions.
Index Terms:
Multiplexing,Analytical models,Cloud computing,Load modeling,Stochastic processes,Quality of service,Computational modeling,responsiveness,Cloud computing,stochastic reward nets,cloud-oriented performance metrics,resiliency
Dario Bruneo, "A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems," IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 3, pp. 560-569, March 2014, doi:10.1109/TPDS.2013.67
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