Multi-Criteria Strategy for Job Scheduling and Resource Load Balancing in Cloud Computing Environment

Cloud computing is growing rapidly over the years and it faces challenges especially in resource management. Resource management in cloud computing is necessary due to its distributed nature with different user demands. Quality of Service (QoS), load balancing and throughput are identified as some of the benefits of proper resource management. This research focuses on job scheduling and resource load balancing in cloud environment. We proposed an efficient algorithm based on multi-criteria strategy. The algorithm consists of two main phases. In the first phase the shortest job completion time is measured based on the completion time of three techniques i.e. min-min, max-min and suffrage. Meanwhile in the second phase genetic algorithm is implemented for resource load balancing. Cloud Sim simulator is used to measure the performance and efficiency of the proposed algorithm. The proposed algorithm enhances jobs scheduling and resource load balancing by ensuring an efficient utilization of the available resources.