Abstrait

Optimized Data Center in IaaS Using Cloud Computing Systems

K. Hemapriya, J. Deepa, S. Kaviarasan

Cloud Computing provides scalable computing and storage resources. More and more data-intensive applications are developed in this computing environment.Evaluation of cloud data center performance is a key attribute in propagation of IaaS cloud services. This evaluation is necessary to quantify the cost benefit and Quality Of Service (QoS) of Cloud services. Cloud system managers find it difficult to maintain performance data due to the huge number of parameters that need to be monitored in a cloud system. Different applications have different quality-ofservice (QoS) requirements. 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. We are also notifying the resiliency analysis to take into account the 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.