Proactive virtualized resource management for service workflows in the cloud

Abstract

A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.