Summary
Cloud computing has garnered unprecedented growth in recent years in the field of Information Technology. It has emerged as a high‐performance computing option owing to its infrastructure that comprises of heterogeneous collection of autonomous computers and adaptable network architecture. The tasks that are scheduled in an optimized manner for their execution could be classified under NP‐hard problems. Though meta‐heuristic scheduling algorithms emerge as scheduling options, they need to be much more consistent while dealing with the dynamic set up of the cloud environment. In this paper, we had proposed a multi‐objective meta‐heuristic scheduling algorithm namely Quasi Oppositional Genetic Spotted Hyena Optimization (QOGSHO) algorithm that globally optimizes the makespan, resource consumption and SLA violation QoS parameters, thereby improving the performance. The algorithm proposed is an amalgamated product of meta‐heuristic algorithms like Quasi Oppositional Based Learning (QOBL), Spotted Hyena Optimization (SHO), and Genetic Algorithm (GA). The performance efficiency of the proposed QOGSHO algorithm had been compared with various scheduling algorithms using uniform datasets by varying the data instance sizes in a simulated cloud environment. The obtained results clearly justify the task scheduling efficiency of the proposed algorithm with respect to the QoS parameters namely makespan, resource utilization and SLA violation.
In recent years, the WSN are emerging swiftly since it finds applications in various domains including weather monitoring, attack detection, industrial monitoring, monitoring of submarine organisms, patient monitoring as well as the monitoring of ecological disorders. But WSN is also influenced by various other factors like network lifetime and energy consumption. It is necessary to provide an energy effective protocol to conquer certain troubles that includes packet delivery ratio, network lifetime, residual energy as well as effective routing in WSN. Therefore, this article aims to propose a novel protocol to enhance the energy efficiency of the network thereby providing an optimal routing path. This can be achieved by selecting an optimal cluster head that maintains communication between the base station and the sensor node. In this article, a novel multi-objective moth swarm based sailfish (MOMS-SF) technique is employed in selecting an optimal cluster head. The proposed MOMS-SF technique enhances the network lifetime and minimizes the energy consumption of the network. Finally, the evaluation results are conducted to determine the network performances of the proposed MOMS-SF approach. Also, a comparative analysis is carried out and the graphical analyzes for various parameters are made for various approaches to determine the effectiveness of the proposed system.
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