Efficient Distributed Edge Computing for Dependent Delay-Sensitive Tasks in Multi-Operator Multi-Access Networks
Alia Asheralieva, Dusit Tao Niyato, Xuetao Wei · IEEE Transactions on Parallel and Distributed Systems · 2024
We study the problem of distributed computing in themulti-operator multi-access edge computing(MEC) network fordependent tasks. Every task comprises severalsub-taskswhich are executed based on logical precedence modelled as adirected acyclic graph. In the graph, each vertex is a sub-task, each edge – precedence constraint, such that a sub-task can only be started after all its preceding sub-tasks are completed. Tasks are executed by MEC servers with the assistance of nearby edge devices, so that the MEC network can be viewed as adistributed“primary-secondary node” system where each MEC server acts as aprimary node(PN) deciding on sub-tasks assigned to itssecondary nodes(SNs), i.e., nearby edge devices. The PN's decision problem is complex, as its SNs can be associated with otherneighboringPNs. In this case, the available processing resources of SNs depend on the sub-task assignment decisions of all neighboring PNs. Since PNs are controlled by different operators, they do not coordinate their decisions, and each PN is uncertain about the sub-task assignments of its neighbors (and, thus, the available resources of its SNs). To address this problem, we propose a novel framework based on agraphical Bayesian game, where PNs play under uncertainty about their neighbors’ decisions. We prove that the game has aperfect Bayesian equilibrium(PBE) yieldingunique optimal values, and formulate newBayesian reinforcement learningandBayesian deep reinforcement learningalgorithms enabling each PN to reach the PBE autonomously (without communicating with other PNs).