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Hybrid genetic, variable neighbourhood search and particle swarm optimisation-based job scheduling for cloud computing

In cloud computing environment (CCE), many scheduling mechanisms have been proposed so far to balance the load between the given set of distributed servers. Genetic algorithm (GA) has been verified to be the best technique to reduce energy consumed by distributed servers, but it becomes unsuccessful to strengthen the exploration in the rising areas. The performance of particle swarm optimisation (PSO) depends upon initially selected random particles, i.e., wrongly selected particles may produce poor results. The variable neighbourhood search (VNS) can be used to set stability of non-local searching and local utilisation for an evolutionary processing period. Therefore, this paper proposes a hybrid of VNS, GA and PSO called HGVP in order to overcome the constraint of poorly selected initial amount of particles in case of PSO-based scheduling for CCE. The simulation of the proposed technique has shown effective results over the available techniques especially in terms of energy consumption.