Multiobjective Optimization in the Cloud Computing Environment for Storage Service Selection

Many cloud services are currently available to provide services for users in a cloud environment. By the advent of the internet of things (IoT), the importance of cloud processing is increased. One of the services in cloud providers is storage service. Since the energy consumption and the fair distribution of load between the servers are significant for satisfying the Quality of service (QoS) constraint, a lot of research has been done in this regard. To respond faster to users, the architecture of the edge computing is used. Furthermore, to prevent overload in one service, making decisions is done in one layer before the cloud service layer. In this article, the MOWCA algorithm is used which will be run in the Fog-layer. MOWCA have been evaluated with other algorithms that have been used earlier in this regard. The Pareto's results have improvements in both Energy consumption and Load Balancing between solutions while satisfying the spacing metric.