Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

In cloud computing environments, when a client wants to access any resources, hardware components, or application services, he needs get subscription for the same from service providers. The usages of each are monitored over network by providers and later on user will be charged services used. Cloud provider is responsible providing Quality Service clients. As number request increases in environment, face various issues such as scheduling allocation security, privacy virtual machine migration. Swarm intelligence, biological systems, physical chemical systems based metaheuristic algorithms have proved efficient used solve real world optimization problems. This review focused insight view nature-inspired their comparisons basis certain parameters that affects efficiency effectiveness applicability order schedule different tasks environment. work facilitates comparative analysis six techniques quantitatively like makespan resource utilization cost. objective this systematic find most optimal technique solving multi criteria problem. After evaluating comparing Ant Colony Optimization, Particle Genetic Algorithm, Artificial Bee algorithm, Crow Search Algorithm Penguin Optimization it has been identified algorithm terms cost with significant improvement others. Finally, promising research directions identified.