An online algorithm for optimally releasing multiple on-demand instances in IaaS clouds

Abstract

With the popularity of cloud computing increasing rapidly in recent years, the use of cloud, represented by IaaS and SaaS, is being embraced by more and more users. One of the flexibility of the cloud lies in its pay-as-you-go usage model, which allows users to purchase and release cloud instances on their own demand, reducing the possible financial loss caused by wasted resources. For a SaaS provider that uses the pay-as-you-go payment model to purchase cloud instances, it is important to make a reasonable decision on when to release as many as idle on-demand cloud instances to achieve cost savings when the number of incoming user demands is in a declining phase, taking into account the cost of the start-up time to acquire new cloud instances and the penalty cost that may be incurred while SaaS users wait. In order to make optimal decisions when there is not enough knowledge to predict the future trend of incoming demands, we propose an online instance releasing algorithm which can effectively help SaaS providers to reduce the cost when using on-demand instances. Through theoretical analysis we show our online algorithm can achieve a competitive ratio of less than 2 for a variety of penalty functions. Our extensive simulation experiments based on both real Google workload data and simulated demand sequences demonstrate that the proposed online algorithm is stable and efficient.