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Full title—Distributed Task Scheduling for MEC-Assisted Virtual Reality: A Fully-Cooperative Multi-Agent Perspective
This article proposes a fully-cooperative task scheduling scheme for virtual reality, taking into account parallel multitasking, the randomness of task arrival and leaving, as well as network load balance. By sharing a common objective among distributed schedulers, tasks can be offloaded to proper edge servers in a cooperative way.
We first formulate an optimization problem to maximize the number of delay-satisfied tasks in the entire edge network. Next, we treat the edge network as a fully-cooperative multi-agent system and transform the problem into a decentralized partially observed Markov decision process (Dec-POMDP). Finally, we propose a cooperative task scheduling scheme based on multi-agent proximal policy optimization to solve the Dec-POMDP.
Simulation results show that the cooperation among edge nodes brings the network improvement in terms of offloading success rate, delay compliance rate, and load balance.
Full Article: IEEE Transactions on Vehicular Technology, Early Access | 
