Quantum-Inspired Optimization for Energy-Efficient Task Scheduling in Cloud Computing

The increasing demands for energy by cloud data centers have made it even more urgent to devise scheduling techniques that address the competing issues of achieving performance goals and maintaining sustainable practices. The proposed approach in this paper is a Quantum-Inspired Energy-Aware Scheduler (QIEAS) that utilizes concepts of quantum superposition along with a rotation gate-based local search strategy to schedule tasks on virtual machines. Contrary to hardware-assisted quantum-inspired techniques, QIEAS runs on purely classical systems but employs quantum state modeling with probabilistic amplitude coding technique. A multi-objective function consisting of energy consumption, makespan and load balance is employed by the proposed scheduler and a quantum measurement strategy is used to collapse possible solutions. Comparison of QIEAS against three meta-heuristic-based algorithms shows a reduction in energy consumption and scheduling delay in all scenarios.