Fault-Tolerant Resource Provisioning with Deadline-Driven Optimization in Hybrid Clouds

Abstract

Abstract-Resource provisioning remains as one of the challenging research problems in cloud computing, more importantly when considered together with service reliability. Fault-tolerance techniques such as fault-recovery is one of the techniques that can be employed to improve on service reliability. Technically, fault-recovery has obvious impact on service performance. Such impact requires detailed studies. Only few works on hybrid cloud resource provisioning address fault recovery and its impact. In this paper, we investigate the problem of resource provisioning in hybrid Clouds, considering the probability of hybrid cloud resource failure during job execution. We formulate this problem as an optimization model with operational cost as an objective function subject to the deadline constraint of jobs. Based on our proposed optimization model, we design a heuristic-based algorithm called dynamic resource provisioning algorithm (DRPA). We then perform extensive experiments to evaluate performance of the proposed algorithm based on a real world set of data. The results confirm the obvious impact of fault recovery on the performance metrics (operational cost and deadline violation rate) and also confirms that DRPA can be useful in minimizing operational cost.