A User Preference and Service Time Mix-aware Resource Provisioning Strategy for Multi-tier Cloud Services

Abstract

As the majority of cloud services deploy a multi-tier architecture, it poses a real challenge to resource provisioning. Because of the cross-tier dependencies of multi-tier cloud services, the traditional tailor-made resource provisioning methods for single-tier applications can’t be adopted or extended directly. Resource provisioning in the cloud is usually driven by performance predictions, so it's important to characterize workload fluctuations accurately in order to understand how to allocate resources. And user preference also contributes to workload variants. Therefore, we propose a dynamic resource provisioning strategy for multi-tier cloud services that employs (i) a user preference and service time mix-aware workload prediction method to be used as a foundation of resource provisioning, and (ii) a dynamic resource provisioning strategy based on the queuing theory to determine how many resources to be provisioned to each tier. Experimental results demonstrate that our workload prediction method is accurate, and our provisioning strategy is able to improve the accuracy of resource provisioning, reduce the allocated resources and SLAs violations.