Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds

Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.