Job Scheduling using Ant Colony Optimization in Grid environment

Grid computing proposes a dynamic and earthly distributed organization of resources that harvest ideal CPU cycle to drift advance computing demands and accommodate user's prerequisites. Heterogeneous gridsdemand efficient allocation and scheduling strategies to cope up with the expanding grid automations. In order to obtain optimal scheduling solutions, primary focus of research has shifted towards metaheuristic techniques. The paper uses different parameters to provide analytical study of variants of Ant Colony Optimization for scheduling sequential jobs in grid systems. Based on the literature analysis, one can summarize that ACO is the most convincing technique for schedulingproblems. However, incapacitation of ACO to fix up a systematized startup and poor scattering capability cast down its efficiency. To overpower these constraints researchers have proposed different hybridizations of ACO that manages to sustain more effective results than standalone ACO.