Adaptive Load Management of Web Applications on Software Defined Infrastructure

Auto-scalability is a common approach for management of cloud applications where resources are provisioned and de-provisioned on demand. Because of its automatic nature, auto-scaling can be exploited for various reasons that can hugely reduce the overall profit. Therefore, it becomes vital to consider the trade-off between the added revenue as a result of auto-scaling and its corresponding cost. To this end, we propose a novel autonomic solution to the management of cloud Web applications whose goal is to optimize the profit. At the core of the solution, there is an optimization module that considers revenue model as well as cost model and uses a number of run-time performance models to derive the best course of action if there is a need for adaptation. Using software defined features that include compute and network programmability, our proposed solution helps applications to optimize their resource allocation to best meet their business requirements. Our experiment results on a hybrid cloud validate the applicability of the proposed solution and demonstrate that it can increase the profit substantially higher compared to a number of baseline approaches.