a novel discrete grey wolf optimizer for scientific workflow scheduling in heterogeneous cloud computing platforms

There are several scientific workflow applications which need vast amount of processing so the cloud offerings give the sense of economic. workflow scheduling has drastic impact on gaining desired quality of service (qos). the main objective of workflow scheduling is to minimize the makespan. the workflow scheduling is formulated to a discrete optimization problem which is np-hard. this paper presents a novel discrete grey wolf optimizer (d-gwo) for scientific workflow scheduling problems in heterogeneous cloud computing platforms in the aim of minimizing makespan. although the original gwo had great achievements in continuous optimization problems, it seems clear gap in utilizing gwo for combinatorial discrete optimization problems. it revolves around the fact that the continuous changes in search space during the course of discrete optimization lead inefficient or meaningless solutions. to this end, the proposed algorithm is customized in such a way to optimize discrete workflow scheduling problem by leveraging some new binary operators and walking around approaches to balance between exploration and exploitation in discrete search space. scientific unstructured workflows were investigated in different circumstances to prove effectiveness of proposed novel meta-heuristic algorithm. the simulation results witnessed the superiority of proposed d-gwo against other state-of-the-arts in terms of scheduling metrics.