Towards Operational Cost Minimization in Hybrid Clouds for Dynamic Resource Provisioning with Delay-Aware Optimization

Recently, hybrid cloud computing paradigm has be widely advocated as a promising solution for Software-as-a-Service (SaaS) providers to effectively handle the dynamic user requests. With such a paradigm, the SaaS providers can extend their local services into the public clouds seamlessly so that the dynamic user request workload to a SaaS can be elegantly processed with both the local servers and the rented computing capacity in the public cloud. However, although it is suggested that a hybrid cloud may save cost compared with building a powerful private cloud, considerable renting cost and communication cost are still introduced in such a paradigm. How to optimize such operational cost becomes one major concern for the SaaS providers to adopt the hybrid cloud computing paradigm. However, this critical problem remains unanswered in the current state of the art. In this paper, we focus on optimizing the operational cost for the hybrid cloud paradigm by theoretically analyzing the problem with a Lyapunov optimization framework. This allows us to design an online dynamic provision algorithm. In this way, our approach can address the real-world challenges where no a priori information of public cloud renting prices is available and the future probability distribution of user requests is unknown. We then conduct extensive experimental study based on a set of real-world data, and the results confirm that our algorithm can work effectively in reducing the operational cost.