Parallel computing gives us an environment through which we can execute numerous assignments at the same time. It enables us to take care of enormous problem by separating it into multiple small problems. As energy utilization while satisfy deadline constraint by PCs has become a concern in recent years. This paper has exhibited a comprehensive review on the different swarm intelligence based energy efficient scheduling techniques. It has been observed that the scheduling in parallel condition is NP-hard in nature. The research on meta-heuristic based job scheduling methods have demonstrated that the utilization of Quick energy aware processor merging has low convergence rate overall world wide minimum even at high numbers of dimensions. Gravitational Search optimization algorithm has been generally acknowledged as a global optimization algorithm of current enthusiasm for disseminated advancement and control. Particle swarm optimization is constrained to beginning arrangement of particles, wrongly chosen particles tends to poor outcomes. Moreover, comparison among different job scheduling methods have displayed that no strategy is ideal for each case. At last, a few considerations about future challenges have been exhibited.

Scheduling,Energy, Deadline, Power

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