An utility-based job scheduling algorithm for current computing Cloud considering reliability factor

The analysis and research of power system necessitates the current computing. However, the bottleneck of current computing lies in the limited computing capacity in power system. Cloud computing's service-oriented characteristics advance a new way of service provisioning called utility based computing, which could provide powerful computing capability for current computing. However, toward the deployment of practical current computing Cloud, we encounter one challenge that the existing job scheduling algorithms under utility based computing do not take hardware/software failure and recovery in the Cloud into account. In an attempt to address this challenge, we introduce the failure and recovery scenario in the current Cloud computing entities and propose a Reinforcement Learning (RL) based algorithm to make job scheduling in the current computing Cloud fault tolerant. We carry out experimental comparison with Resource-constrained Utility Accrual algorithm (RUA), Utility Accrual Packet scheduling algorithm (UPA) and LBESA to demonstrate the feasibility of our proposed approach.