Optimizing QoS and Energy using ESSA for Budget-constrained Workflows in the Cloud - C. K. Sripavithra (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Cloud computing has acquired colossal interest. Here resources are observed as services; hence employment of it in an organized manner is performed in two integral parts, i.e., asset provisioning as well as process organizing. Workflows are big data applications in the distributed community. Cloud infrastructures' flexibility makes them an appropriate choice for scientific workflow computations. An effective task scheduling technique must determine the order of tasks in the workflow for processing so as to satisfy the needs of the client. Most of the procedures concentrate on upgrading resource usage and fulfilling the QoS. Furthermore, these workflow computations require more computing power and the energy consumed to meet those requests are neglected. This fact persuaded us to present an Enhanced Salp Swarm Algorithm (ESSA) for scheduling scientific workflows with energy consciousness. On evaluating the potentiality, the proposed ESSA scheduling technique outperformed three other approaches in the simulation of synthetic scientific job trials. The solutions portray reduced execution time and energy usage by raising total resource consumption and employment.