Efficient Resource Allocation to Enhance the Quality of Service in Cloud Computing

Pay-as-you-go models are used to grant users access to cloud services. While using the cloud, an imbalance workload on data centre resources degrades quality of service metrics like makespan, storage, high failure rate, and energy consumption. Hence proposed the heuristic based hybrid GA to enhance the QoS with resource allocation in cloud computing. The population is first initialized using the Binary encoding sorts the tasks according to priority. After that, the Best Fit algorithm compares the Best Fit with iterations of each fitness value depending on the computation time to shorten the make span. Heuristic crossover approach and mutation are then used to update the probability of the existing population with the new population lowers the failure rate by using the fitness value. Therefore, the proposed heuristic-based hybrid GA technique balanced the load and allocate the resources effectively to improve QoS performances. The outcome reveals that the proposed method of QoS performances attained less makespan, energy consumption, failure rate and execution time with effectively allocated the resources of 1% to 39% when compared to the previous methods in cloud computing.