Deep reinforcement learning-based task offloading and service migrating policies in service caching-assisted mobile edge computing

Emerging mobile edge computing (MEC) is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment (MWE) with limited computational resources and energy. Due to the homogeneity of request tasks from one MWE during a long-term time period, it is vital to predeploy the particular service cachings required by the request tasks at the MEC server. In this paper, we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks. Furthermore, we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme (MBOMS) to minimize the long-term average weighted cost. The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution. Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.