Online Distributed Job Dispatching with Outdated and Partially-Observable Information
Yuncong Hong, Bojie Lv, Rui Wang, Haisheng Tan, Zhenhua Han, Hao Zhou,, Francis C.M. Lau

TL;DR
This paper presents a low-complexity, distributed job dispatching strategy for edge computing in MANs, effectively handling outdated and partial information to reduce job response times.
Contribution
It introduces a novel policy iteration framework for POMDP-based job dispatching, with proven performance bounds and practical effectiveness demonstrated through simulations.
Findings
Achieves up to 20.67% reduction in average job response time.
Provides a scalable solution for distributed dispatching with outdated information.
Performs well across various network parameter settings.
Abstract
In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Age of Information Optimization · Cloud Computing and Resource Management
