Latency and Reliability-Aware Dynamic Task Offloading and Scheduling for Energy-Harvesting Systems in Mobile Edge Computing

The integration of Energy Harvesting (EH) technology into Mobile Edge Computing (MEC) presents a promising solution to the energy limitations faced by end devices (EDs) when performing computation-intensive tasks, ultimately enhancing the EDs’ sustainability. However, the intermittent and unpredictable nature of harvested energy introduces significant uncertainty in energy availability, complicating dynamic task execution and resource management. In EH-MEC systems, managing task scheduling and offloading dynamically is crucial for optimizing application latency while ensuring long-term battery energy stability and task reliability. Existing approaches inadequately address the time-coupling between task decisions caused by long-term battery energy stability constraints, and often neglect task reliability considerations. To overcome these limitations, we propose decomposing the original problem into 1) a decoupling problem that transforms the optimization with long-term battery energy constraints into a series of deterministic optimizations within individual time slots, 2) a task scheduling problem that determines task-to-ES assignment and computing resource allocation of ESs to offloaded tasks, and 3) a task offloading problem that determines the optimal offloading decision to achieve minimal latency while meeting energy stability and server reliability constraints. To tackle these subproblems, we design a Lyapunov-based optimization method, a reliabilityaware task scheduling algorithm, and an efficient heuristic-based task offloading algorithm. Extensive simulations and experiments based on empirical data from a physical MEC testbed validate the efficacy of our approach.