Dynamic task offloading and resource allocation for energy-harvesting end–edge–cloud computing systems

Abstract

In end–edge–cloud (EEC) computing, end devices (EDs) offload compute-intensive tasks to nearby edge servers or the cloud server to alleviate processing burdens and enable a flexible computing architecture. However, resource constraints and dynamic environments pose significant challenges for EEC task offloading and resource allocation, including real-time requirements, unreliable task execution, and limited battery energy, especially in energy harvesting (EH) systems, in which battery energy remains unstable due to its inherent fluctuations. Existing task offloading and resource allocation approaches often fail to address these challenges holistically, leading to degraded performance and potential task execution failures. In this paper, we propose a novel task offloading and resource allocation method for EH EEC computing, aiming to optimize long-term performance by minimizing delay and energy consumption while ensuring task execution reliability and battery energy stability. Specifically, we formulate task offloading and resource allocation as a cost optimization problem under constraints such as ED capacity, task reliability, and energy consumption. To solve this problem, we first leverage Lyapunov optimization to decouple the original time-dependent problem. Then we derive optimal closed-form solutions for computation and transmission power resource allocation. Based on these solutions, we propose a multiple discrete particle swarm optimization algorithm to determine task offloading decision. Extensive experiments demonstrate the superiority of our method in balancing delay, execution reliability, and energy stability under varying conditions.