Distributed Job Dispatching in Edge Computing Networks With Random Transmission Latency: A Low-Complexity POMDP Approach

Job dispatching is a fundamental problem in edge computing for load balancing among multiple edge servers. When implementing an edge computing system with distributed job dispatchers in a sizable network, such as a metropolitan area network (MAN), the highly dynamic transmission latency is nonnegligible, which could lead to outdated information being shared. Moreover, the fully observed system state is beyond reach as the reception of any broadcast is time consuming. In this article, we investigate the online distributed job dispatching problem in edge computing, where multiple access points (APs) collect jobs and then dispatch each job to an edge server. The distributed dispatcher on each AP would receive partially and outdated information exchanged via periodic broadcast. Hence, we formulate the distributed job dispatching problem by leveraging the partially observable Markov decision process (POMDP) and propose a novel approximate Markov decision process (MDP) solution framework, called
DecMDP
, that bypasses the huge time complexity of conventional POMDP solutions. Both analytical and semi-analytical performance lower bounds are derived for the approximate MDP solution. Furthermore, we extend
DecMDP
to handle a more general scenario where
a priori
knowledge of the system is absent. Finally, extensive simulations based on the Google Cluster traces show that our policy can achieve the best performance when compared with heuristic baselines, e.g., achieving 20.67% reduction in average job response time, and consistently performs well under various parameter settings.