EdgeMatrix: A Resources Redefined Edge-Cloud System for Prioritized Services
Yuanming Ren, Shihao Shen, Yanli Ju, Xiaofei Wang, Wenyu Wang, Victor, C.M. Leung

TL;DR
EdgeMatrix introduces a novel edge-cloud resource management framework that leverages multi-agent reinforcement learning, clustering, and multi-task mechanisms to optimize throughput, reduce SLA violations, and improve runtime efficiency in complex networked environments.
Contribution
The paper presents a new framework, EdgeMatrix, which redefines resource management in edge-cloud systems using advanced algorithms for better SLA guarantees and system performance.
Findings
Improves system throughput in complex environments.
Reduces SLA violations significantly.
Cuts down runtime compared to traditional methods.
Abstract
The edge-cloud system has the potential to combine the advantages of heterogeneous devices and truly realize ubiquitous computing. However, for service providers to guarantee the Service-Level-Agreement (SLA) priorities, the complex networked environment brings inherent challenges such as multi-resource heterogeneity, resource competition, and networked system dynamics. In this paper, we design a framework for the edge-cloud system, namely EdgeMatrix, to maximize the throughput while guaranteeing various SLA priorities. First, EdgeMatrix introduces Networked Multi-agent Actor-Critic (NMAC) algorithm to redefine physical resources as logically isolated resource combinations, i.e., resource cells. Then, we use a clustering algorithm to group the cells with similar characteristics into various sets, i.e., resource channels, for different channels can offer different SLA guarantees.…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Cloud Computing and Resource Management · Software-Defined Networks and 5G
