ADWEH: A Dynamic Prioritized Workflow Task Scheduling Approach Based on the Enhanced Harris Hawk Optimization Algorithm

Cloud Computing has transformed IT service delivery by offering scalable, on-demand resources essential for managing diverse and complex workloads. However, efficient task scheduling remains a critical challenge due to the NP-hard nature of mapping workflows to virtualized resources, impacting performance, resource utilization, energy efficiency, and reliability. This highlights the urgent need for advanced scheduling strategies. This paper presents ADWEH (A Dynamic Prioritized Workflow Task Scheduling approach based on the Enhanced Harris Hawk Optimization), a novel framework designed to overcome these challenges. ADWEH combines deep reinforcement learning with dynamic prioritization strategies to assign task priorities based on dependencies and runtime requirements. This integrated approach ensures optimal resource allocation, enhances scheduling efficiency, and improves adaptability to varying workloads. The proposed approach has been implemented and thoroughly tested using WorkflowSim, a simulation toolkit for modeling and evaluating workflow scheduling strategies. ADWEH's performance is benchmarked against state-of-the-art algorithms, including Deep Q-Network(DQN), Advantage Actor-Critic(AC2), and PWSA3C, using scientific workflows including Montage, Cybershake, SIPHT, LIGO. These Four workflows are tested on datasets like HPC2N, NASA, Uniform, and Random, ensuring robust evaluation under diverse scenarios. Simulation results demonstrate that ADWEH significantly outperforms DQN, A2C, and PWSA3C approaches, achieving up to 31.9% shorter makespan, a 24% reduced in energy consumption, a 29% Enhancement in scalability efficiency, and a 52% increase in resource utilization, while reducing failure rates by over 47%. The results position ADWEH as a robust, efficient solution for improving the performance, reliability of cloud-based workflows, addressing the evolving demands of modern cloud environments.