Dynamic Resource Allocation via Distributed Decisions in Cloud Environment

Abstract

The adaptation of virtualization technologies and the Cloud Compute model by Web service providers is accelerating. These technologies commonly known as Cloud Compute Model are built upon an efficient and reliable dynamic resource allocation system. Maintaining sufficient resources to meet peak workloads while minimizing cost determines to a large extend the profitability of a Cloud service provider. Traditional centralized approach of resource provisioning with global optimization and statistical strategies can be complex, difficult to scale, computational intensive and often non-traceable which adds to the cost and efficiency of Cloud operation, especially in industrial environments. As we have learned in real life, the most efficient economic system is the one that provides individuals with incentives for their own decisions. It is also true for computing systems. In this paper, we present an architecture for dynamic resource provisioning via distributed decisions. We will illustrate our approach with a Cloud based scenario, in which each physical resource makes its own utilization decision based on its own current system and workload characteristics, and a light-weight provisioning optimizer with a replaceable routing algorithm for resource provisioning and scaling. This approach enables resource provisioning system to be more scalable, reliable, traceable, and simple to manage. In an industrial setting, the importance of these characteristics often exceeds the goal of squeezing the absolute last CPU cycles of the underlying physical resources.