A Salp Swarm Optimization for Dynamic Resource Management to Improve Quality of Service in Cloud Computing and IoT Environment

Background: Cloud Computing can process and utilize efficient resources within a metered premise. This feature is a significant research problem, giving great Quality-of-Services (QoS) to the clients in cloud. Objective: The objective of this study is to confirm QoS with minimum resource utilization time and costs, cloud service providers ought to receive self-versatile resource provisioning at each level. Various guidelines and model-based methodologies have been proposed for the management of resources in cloud services. Methods: In this research article, resource allocation is done using the Salp Swarm Algorithm (SSA), which combines various VMs on a constrained Data Center with SLA and QoS factors. Results: Different existing optimization algorithms are available such as First Fit, Greedy Crow Search (GCS) and Hybrid Crow Search algorithm (TSPCS). The combination of the Travelling Salesman Problem (TSP) and Crow Search Algorithm (CSA) is more efficient than the Fist Fit, GCS, and TSPCS in terms of the parameters such as resource utilization and response time. It is clearly shown that a user’s request takes minimum time and maximum QoS when employing the SSA algorithm in cloud computing. Conclusion: The proposed mechanism is simulated on Cloudsim Simulator. The simulation results show less migration time that improve the QoS and minimizes the energy consumption in a cloud and IoT environment.