An Optimization-based Multi-Objective Scheduling Approach with Dynamic Voltage and Frequency Scaling for Workflow Scheduling in Cloud Computing

Cloud computing has rapidly gained prominence due to its fault tolerance, flexibility, scalability, and pay-as-youuse policy, making it ideal for hosting diverse applications. It has become a dominant paradigm for executing workflow applications with optimized resource utilization. Among its critical components, workflow scheduling plays a pivotal role inmanaging complex constraints and processing large volumes of data. However, while existing multi-objective workflow scheduling algorithms assign tasks effectively, they oftenstruggle to minimize both cost and execution time simultaneously under dynamic conditions. To address these limitations, this article proposes an advanced optimizationbased multi-objective scheduling algorithm named Chronological One-to-One Based Optimization Work schedule (COOBO Work schedule). The main objective of this study is todevelop an optimization-based multi-objective scheduling approach that achieves efficient task allocation while reducing make span, energy consumption, and memory usage, andimproving resource utilization. The approach begins with the simulation of a cloud computing environment incorporating dynamic voltage and frequency scaling. Task scheduling isguided by multiple fitness parameters, including energy consumption predicted using a Deep Feedforward Network. Subsequently, COOBO is employed to optimize the schedulingprocess. Experimental results demonstrate that COOBO-Work schedule outperforms existing methods in terms of make span of 0.016, resource utilization of 0.936, energy consumption of65.877J, memory usage of 23.765 MB, and computational overhead of 0.357sec, thereby validating its effectiveness in cloud workflow management.