Task Scheduling Optimization in the Cloud using Quantum Annealing

Task scheduling is one of the crucial agendas in cloud computing technology. The system consists of various tasks namely termed cloudlets which need to be fed among various processing units for its execution. However, the algorithm that has been used to schedule tasks among various processing units should provide the optimum solution to a given problem. This research realizes the power of quantum computation specifically quantum annealing to provide the optimum solution to schedule the tasks among various processing units under the given constraints. Quantum Annealing is used for combinatorial optimization problems and as task scheduling is also one of such problems, it can be done in the quantum environment for optimal results. The Hybrid Solver provided by the D-Wave Quantum Computer has been used to solve the given problem.Leveraging the D-Wave Quantum Annealer, we analyze various scheduling scenarios and evaluate the performance in terms of makespan and throughput. Our findings reveal substantial improvements in scheduling efficiency compared to Partical Swarm Optimization scheduling. Specifically, we observed a notable reduction in makespan by up to 70% across diverse workload scenarios. Moreover, there was not much difference in throughput provided by both algorithms. The task scheduling among various processing units using quantum annealing has provided significant differences in makespan. Thus, using an annealing technique to schedule the tasks in cloud computing would significantly enhance in load balancing and efficient use of resources.