Load balancing for grid computing environments using auto controlled Ant colony optimization technique

An auto controlled Ant colony optimization algorithm it controls the behavior of the ant colony algorithm automatically based on a priori heuristic models. Lazy ants are basically mutated version of active ants that remains alive till the fitter lazy ants are generated in the successive generations. This work presents an improved auto controlled ACO algorithm using the lazy ant concept. Grid Scheduling is defined as the process of making scheduling decisions involving resources over multiple administrative domains. An Improved Enhanced Gridsim with Deadline Control (IEGDC) model is used to reduce load balancing. It enhances the utilization of the resources and prevents the resource overloading. It enhances the utilization of the resources and prevents the resource overloading. Our selection method considers the state of resource bandwidth and capacity of the resources. The incorporation of the said features of the proposed method makes it quite attractive in grid applications. During the experimental study of auto controlled ACO algorithm on grid scheduling problem, it was observed that the induction of lazy ants not only reduces the time complexity of the algorithm but also produces better the results on the given objectives. It is Simulated GridSim Platform.