Learning-Based Queue-Aware Task Offloading and Resource Allocation for Air-Ground Integrated PIoT

Air-Ground Integrated Power Internet of Things (AGI-PIoT) is a key enabler to meet the stringent communication and computing requirements of PIoT devices. In AGI-PIoT, the computation-intensive and delay-sensitive tasks can be either offloaded to edge servers through unmanned aerial vehicles (UAVs) or offloaded to cloud servers through ground base stations (GBSs), while the computational resources of edge servers and cloud servers should be jointly allocated. However, the joint optimization of task offloading and resource allocation faces several challenges such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this paper, we propose a learning-based QUeue-AwaRe Task offloading and rEsouRce allocation algorithm (QUARTER). Specifically, by exploiting Lyapunov optimization, the joint optimization problem is decomposed into task offloading and server-side resource allocation. For the first subproblem, we propose a Queue-aware Actor-Critic-based task offloading algorithm named QAC to cope with dimensionality curse. A low-complexity heuristic algorithm is developed to solve the second subproblem. Compared with existing task offloading and resource allocation algorithms, simulation results demonstrate that QUARTER has superior performances in throughput, queuing delay, and convergence.