Multi-Objective Deep Reinforcement Learning for Dynamic Task Scheduling Under Time-of-Use Electricity Price in Cloud Data Centers

The high energy consumption and substantial electricity costs of cloud data centers pose significant challenges related to carbon emissions and operational expenses for service providers. The temporal variability of electricity pricing in real-world scenarios adds complexity to this problem while simultaneously offering novel opportunities for mitigation. This study addresses the task scheduling optimization problem under time-of-use pricing conditions in cloud computing environments by proposing an innovative task scheduling approach. To balance the three competing objectives of electricity cost, energy consumption, and task delay, we formulate a price-aware, multi-objective task scheduling optimization problem and establish a Markov decision process model. By integrating prioritized experience replay with a multi-objective preference vector selection mechanism, we design a dynamic, multi-objective deep reinforcement learning algorithm named TEPTS. The simulation results demonstrate that TEPTS achieves superior convergence and diversity compared to three other multi-objective optimization methods while exhibiting excellent scalability across varying test durations and system workload intensities. Specifically, under the TOU pricing scenario, the task migration rate during peak periods exceeds 33.90%, achieving a 13.89% to 36.89% reduction in energy consumption and a 14.09% to 45.33% reduction in electricity costs.