Topology-Aware Task Offloading and Resource Allocation in Edge Cloud Computing Enabled IIoT With Agentic AI
Abstract
Edge cloud computing in the Industrial Internet of Things (IIoT) enables latency-sensitive tasks from IIoT terminals to be offloaded to distributed edge data centers (EDCs). This paper proposes an agentic artificial intelligence (AI)-assisted Stackelberg game framework to address the task offloading and resource allocation (TORA) problem. Our goal is to minimize the total processing delay of tasks while guaranteeing their delay thresholds. In this framework, LAN bandwidth and EDC computing capabilities are treated as commodities. Tasks act as non-cooperative Stackelberg followers, utilizing a Lagrange multiplier-based algorithm to achieve closed-form theoretically optimal bidding strategies under given commodity prices. Meanwhile, an agentic Stackelberg leader adopts a twin delayed deep deterministic policy gradient (TD3) algorithm to dynamically adjust these unit-prices for optimal system performance. Experimental results demonstrate that the proposed framework features extremely low computational complexity for online TORA problems, minimizes total processing delays, and significantly reduces the occurrence probability of events that at least one security-related task exceeds its delay threshold.