IMMUNE GENETIC ALGORITHM FOR SCHEDULING SERVICE WORKFLOWS WITH QOS CONSTRAINTS IN CLOUD COMPUTING

Resources allocation and scheduling of service workflows is an important challenge in distributed computing. This is particularly true in a cloud computing environment, where many computer resources may be available at specified locations, as and when required. Quality-of-service (QoS) issues such as execution time and running costs must also be considered. Meeting this challenge requires that two classic computational problems be tackled. The first problem is allocating resources to each of the tasks in the composite web services or workflow. The second problem involves scheduling resources when each resource may be used by more than one task, and may be needed at different times. Existing approaches to scheduling workflows or composite web services in cloud computing focus only on reducing the constraint problem - such as the deadline constraint, or the cost constraint (bi-objective optimisation). This paper proposes a new genetic algorithm that solves a scheduling problem by considering more than two constraints (multi-objective optimisation). Experimental results demonstrate the effectiveness and scalability of the proposed algorithm.