SITOff: Enabling Size-Insensitive Task Offloading in D2D-Assisted Mobile Edge Computing

Mobile edge computing (MEC), along with device-to-device (D2D) assisted MEC (D-MEC), are promising technologies that could improve the quality-of-experience for mobile devices (MDs) by offloading their tasks to edge servers or nearby idle MDs. There is a popular trend to develop distributed task offloading algorithms using multi-agent reinforcement learning (MARL), whose adoption of central critics during training makes the offloading still size-sensitive. Therefore, this paper proposes a Size-Insensitive Task Offloading (SITOff) algorithm for D-MEC based on fully-distributed offloading without maintaining any central venue. Specifically, taking advantage of the inherent graph-like structure of D-MEC, SITOff adopts graphs to represent MDs' states and relationships and form each MD's local knowledge about D-MEC through graph computation. Furthermore, considering the limitation of local knowledge in performing whole performance-oriented offloading, each MD utilizes D2D-transmitting to exchange knowledge with its neighbors and form a comprehensive knowledge about D-MEC to enhance the coordination of distributed offloading. Additionally, regarding the different impacts of neighbors' knowledge, each MD leverages attention mechanisms to selectively learn its neighbors' knowledge during knowledge-exchange. Extensive experimental results show the superiority of SITOff over state-of-the-art MARL-based offloading algorithms in D-MEC with various MDs, and the easy collaboration of SITOff with curriculum-learning for large-scale D-MEC offloading.