SEQUENCE-TO-SEQUENCE DEEP LEARNING FOR DEPENDENCY-AWARE TASK OFFLOADING IN MULTI-ACCESS EDGE COMPUTING

Abstract

The proliferation of Internet of Things (IoT) devices and resource-intensive applications has placed growing pressure on computation, storage, and network resources. Cloud computing helps, but its centralized nature adds latency that delay-sensitive services cannot tolerate, whereas Multi-Access Edge Computing (MEC), boosted by 5G, moves computation closer to the data. Offloading remains hard, however, because applications consist of interdependent tasks while wireless conditions and edge load fluctuate continuously. This paper formulates dependency-aware task offloading as a Sequence-toSequence (Seq2Seq) learning problem: the application task graph is read as an input sequence, and pertask placements are produced as an output sequence. The contributions are threefold: (i) a Seq2Seq encoder-decoder model that couples a graph-aware encoder with multi-scale (global, local, and criticalpath) attention; (ii) an offline curriculum-based training procedure that removes the online exploration cost of reinforcement learning; and (iii) an extensive evaluation on a discrete-event MEC simulator. The proposed model reduces application latency by up to 38% compared with Round-Robin and by about 26% compared with HEFT, with the widest margin in the 7–20 Mbps band typical of 4G networks; it scales near-linearly at about 1.7 ms per task up to 100 tasks; and it keeps the task-failure rate near 5% under overload, against 12% for Round-Robin, at a modest 18% device-energy overhead.