ESSA Scheduling Algorithm for Optimizing Budget-Constrained Workflows

Workflows are a systematic approach for defining various scientific applications of distributed systems. They break down complicated, data-intensive processes into minor activities that can be executed serially or in parallel according to the type of application. Cloud systems need to allocate resources and schedule workflows efficiently. Despite many studies on job scheduling and resource provisioning, an efficient solution isn't found. Therefore, techniques are required to enhance resource utilization for optimal cloud computing platforms. Hence, user and provider quality of service (QoS) goals, like shortening workflows and ensuring budget limits with low energy utilization, must be considered. Enhanced Salp Swarm Optimization (ESSA) is designed to optimize makespan and QoS metrics in cloud systems. A Virtual Machine (VM's) compute capacity is related to Central Processing Unit (CPU) and memory. Size and memory demand is considered for tasks in the workflow, and task execution time is evaluated using both CPU and memory. The collated experimental outcomes convey that the newly presented technique boosts the workflows' energy utilization (up to 89%) and pushes the normalized makespan results to 3.2ms.