Towards Energy-Aware Resource Scheduling to Maximize Reliability in Cloud Computing Systems

Cloud computing has become increasingly popular due to deployment of cloud solutions that will enable enterprises to cost reduction and more operational flexibility. Reliability is a key metric for assessing performance in such systems. Fault tolerance methods are extensively used to enhance reliability in Cloud Computing Systems (CCS). However, these methods impose extra hardware and/or software cost. Proper resource allocation is an alternative approach which can significantly improve system reliability without any extra overhead. On the other hand, contemplating reliability irrespective of energy consumption and Quality of Service (QoS) requirements is not desirable in CCSs. In this paper, an analytical model to analyze system reliability besides energy consumption and QoS requirements is introduced. Based on the proposed model, a new online resource allocation algorithm to find the right compromise between system reliability and energy consumption while satisfying QoS requirements is suggested. The algorithm is a new swarm intelligence technique based on imperialist competition which elaborately combines the strengths of some well-known meta-heuristic algorithms with an effective fast local search. A wide range of simulation results, based on real data, clearly demonstrate high efficiency of the proposed algorithm.