How can I distribute tasks efficiently in a multi-agent MEC system?
Distributed Offloading in Multi-Access Edge Computing Systems: A Mean-Field Perspective
This paper explores optimized task offloading in multi-access edge computing (MEC). Devices decide how much computation to perform locally versus offloading to a shared edge server, balancing power consumption and data freshness (age of information). To handle large numbers of devices, the researchers apply mean-field game theory, allowing for distributed decision-making based only on individual device information and statistical system properties, eliminating the need for complex inter-device communication. A priority-access scenario is also considered, using major-minor mean-field games to model the influence of a primary user. The mean-field approach provides scalable, decentralized computation offloading strategies relevant to large-scale multi-agent systems. The focus on individual agents reacting to global system statistics mirrors potential interactions in LLM-based multi-agent scenarios. The proposed distributed algorithms and consideration of resource contention offer valuable insights for developing similar systems with LLMs.