Designing Energy-Aware Scheduling and Task Allocation Algorithms for Online Reinforcement Learning Applications in Cloud Environments

With the rapid proliferation of machine learning applications in cloud computing environments, addressing crucial challenges concerning energy efficiency becomes pressing, including addressing the high power consumption of such workloads. In this regard, this work focuses much on the development of an energy-aware scheduling and task assignment algorithm that, while optimizing energy consumption, maintains required performance standards in deploying machine-learning applications in cloud environments. It therefore, pivots on leveraging online reinforcement learning to deduce an optimal planning and allocation strategy. This proposed algorithm leverages the capability of RL in making sequential decisions with the aim of achieving maximum cumulative rewards. The algorithm design and its implementation are examined in detail, considering the nature of workloads and how the computational resources are utilized. The algorithm’s performance is analyzed by looking into different performance metrics that assess the success of the model. All the results indicate that energy-aware scheduling combined with task assignment algorithms are bound to reduce energy consumption by a great margin while meeting the required performance for large-scale workloads. These results hold much promise for the improvement of sustainable cloud computing infrastructures and consequently, to energy-efficient machine learning. The future research directions involve enhancing the proposed algorithm’s generalization capabilities and addressing challenges related to scalability and convergence.