Dynamic Computation Offloading for Green Things-Edge-Cloud Computing with Local Caching

With the increasing popularity of the internet of things (IoT) and 5G, emerging things-edge-cloud computing (TEC) paradigm provides a flexible way for execution of delay-sensitive and computation-intensive applications running on the user equipment (UE). By offloading these workloads to the mobile edge computing (MEC) or mobile cloud computing (MCC) server, the quality of experience, e.g., the execution delay, could be greatly improved. Nevertheless, conventional battery-powered devices face the challenge of battery exhaustion for task offloading. Using renewable energy via energy harvesting (EH) technologies has become a promising way to power these devices. In this paper, we investigate a multi-user green TEC system with EH UEs, each has a task buffer with limited capacity. A joint offloading decision and resource allocation problem is formulated, which addresses the long-term average execution delay, the task dropping and the long-term average energy cost constraint. A low-complexity online algorithm is proposed leveraging Lyapunov optimization framework and matroid theory, which jointly decides the offloading decision, the MEC server CPU frequencies and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the arrival tasks, wireless channel state, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot. Simulation results show that our proposed algorithm makes a best trade-off between minimizing the long-term average generalized delay and satisfying the long-term average energy cost constraint. Impacts of various parameters on the delay and energy cost performance are also discussed.