A Comparative Study On Dynamic Task Scheduling Algorithms
Chouhan Kumar Rath · i-manager s Journal on Information Technology · 2018
Parallelism has been employed for many years, for high performance computing. Parallel computers can be classified according to the level at which the hardware supports parallelism with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, Massively Parallel Processors (MPPs), and grids use multiple computer to work on the same task. Scheduling and Mapping of heterogeneous tasks to heterogeneous processor dynamically in a distributed environment has been one of the challenging area of research in the field of Grid Computing System. Several general purpose approaches with some modified techniques has been developed. This paper presents a comparative study of different algorithms such as Directed Search Optimization (DSO) trained Artificial Neural Network (ANN), Parallel Orthogonal Particle Swarm Optimization (POPSO), Lazy Ant Colony Optimization (LACO), and Genetic Algorithms (GA), in the basis of workflow scheduling in grid environment of multiprocessors. It also presents various heuristic based methods used in task scheduling.