An Improved Adaptive Workflow Scheduling Algorithm in Cloud Environments

Cloud environments consist of a collection of tasks and heterogeneous resources, the problem of mapping tasks to resources is a major issue. In this paper, we proposed an Improved Adaptive heuristic algorithm (IAHA). At first, the IAHA algorithm makes tasks prioritization in complex graph considering their impact on each other, based on graph topology. Through this technique, completion time of application can be efficiently reduced. Then, it is based on adaptive crossover rate and mutation rate to cross and mutate to control and lead the algorithm to optimized solution. The experimental results show that the proposed method solution can obtain the response quickly moreover optimize makespan, load balancing on resources and failure rate of tasks. At the same time, the proposed algorithm presents the better results contrast with traditional genetic algorithm (GA) and HSGA.