Energy and Deadline Aware Workflow Scheduling Based on Task Classification

ABSTRACT The scalability, adaptability, and pay‐per‐use nature of cloud computing have contributed to its meteoric rise to prominence, enabling customers to access services regardless of their physical location. A major obstacle to effective resource management is the wide variety of services provided and the wide variety of user needs. Due to inadequate resource use and suboptimal scheduling tactics, cloud data centers, which consist of physical machines (PMs) hosting many virtual machines (VMs), frequently experience significant energy consumption. A task scheduling technique is introduced in this study to tackle energy efficiency in cloud environments. It integrates two meta‐heuristic algorithms. A Slack‐based classification algorithm is used first to cluster tasks and then rank them according to their criticality. In order to schedule vital work, we use the Remora Optimization Algorithm (ROA). For noncritical jobs, we use Particle Swarm Optimization (PSO). Researchers tested several configurations ofVMs and job counts in an experimental setting and then compared the outcomes to those of more conventional approaches like Genetic Algorithm (GA) and baseline PSO. The proposed approach shows promise as an efficient scheduling approach for environmentally conscious cloud computing, thanks to its substantial reductions in execution time and energy consumption. Evaluations were carried out in a simulated cloud environment, incorporating different task counts and VM configurations. The proposed mechanism underwent a comparative analysis with eight benchmark methods. The findings indicate that the proposed method shows a marked superiority over current techniques, realizing a 33.5% decrease in execution time (168.57 s compared to 253.47 s) and an 11%–52% enhancement in energy efficiency (0.653 kWh vs. a maximum of 0.852 kWh). The results validate the efficacy of the scheduling strategy in improving energy efficiency and performance within cloud computing environments.