An adaptive scheduling approach based on integrated best-worst and VIKOR for cloud computing

Cloud computing is a new distributed environment to provide on-demand services through the internet and has become popular due to this specific feature. By increasing the count of user requests and different criteria in applying the cloud resources, there exist challenges for managing these requests and their optimal allocation. Some of these challenges consist of considering the dynamic nature of the cloud environment, finding the values of the different criteria and the scheduling tasks by considering the user's Quality of Service (QoS) preferences, load balancing, etc. Static load balancers distribute requests based on pre-known server capability ratios and these models do not produce optimal throughput when the server capabilities change overtime. None of the other scheduling algorithms is able to provide an adaptive approach to consider both of the load balancing and optimizing the QoS requirements. To solve this problem, this paper proposes a new adaptive approach based on a combination of the concept of the best-worst multi criteria decision-making method (BWM), and compromise ranking method (VIKOR). The VIKOR method is implemented as a decision maker to specify the task priorities. The proposed approach is validated with numerical experiments and is compared with the existing scheduling algorithms over different performance metrics. The simulation results indicate that the proposed approach improves the performance metrics like throughput, makespan, waiting time, virtual machine (VM) utilization and VM usage cost for all considered experimental scenarios in comparison with its counterparts.