Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing

Abstract This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule given application onto the available machines environment with minimum makespan (i.e. length) processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users providers. proposed called Enhanced Front (EBABC-PF). Our starts by listing tasks according priority defined Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution applying Greedy Randomized Adaptive Search Procedure (GRASP) finally schedules (BABC). Further, several modifications are considered BABC improve local searching process circular shift operator mutation on food sources population improvement rate. simulated implemented WorkflowSim which extends existing CloudSim tool. performance compared Deadline (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) standard algorithm using sizes various benchmark workflows. results clearly demonstrate efficiency terms makespan, resources utilization.