Reliable Task Offloading in Sustainable Edge Computing with Imperfect Channel State Information

As a promising paradigm, edge computing enhances service provisioning by offloading tasks to powerful servers at the network edge. Meanwhile, Non-Orthogonal Multiple Access (NOMA) and renewable energy sources are increasingly adopted for spectral efficiency and carbon footprint reduction. However, these new techniques inevitably introduce reliability risks to the edge system generally because of i) imperfect Channel State Information (CSI), which can misguide offloading decisions and cause transmission outages, and ii) unstable renewable energy supply, which complicates device availability. To tackle these issues, we first establish a system model that measures service reliability based on probabilistic principles for the NOMA-based edge system. As a solution, a Reliable Offloading method with Multi-Agent deep reinforcement learning (ROMA) is proposed. In ROMA, we first reformulate the reliability-critical constraint into an long-term optimization problem via Lyapunov optimization. We discretize the hybrid action space and convert the resource allocation on edge servers into a 0-1 knapsack problem. The optimization problem is then formulated as a Partially Observable Markov Decision Process (POMDP) and addressed by multi-agent proximal policy optimization (PPO). Experimental evaluations demonstrate the superiority of ROMA over existing methods in reducing grid energy costs and enhancing system reliability, achieving Pareto-optimal performance under various settings.