Distributed Edge Computing Task Allocation with Network Effects

TL;DR

This paper presents a distributed optimization approach for task allocation in edge computing networks, maximizing QoS amid heterogeneous hardware constraints and dynamic network conditions.

Contribution

It introduces a dual-descent based distributed algorithm for task allocation that adapts to changing QoS and hardware constraints in edge networks.

Findings

The proposed method effectively adapts to dynamic network conditions.

Simulation results show improved QoS in real-world sensor network data.

The algorithm is easily implementable within network communication constraints.

Abstract

Field-deployable edge computing nodes form a network and are used to complete scientific tasks for remote sensing and monitoring. The networked nodes collectively decide which scientific applications to run while they are constrained by various factors, such as differing hardware constraints from heterogeneous nodes and time-varying quality of service (QoS) requirements. We model the problem of task allocation as an optimization problem that maximizes the QoS, subject to the constraints. We solve the optimization problem using a dual-descent method, which can be easily implemented in a distributed way subject to the communication constraints of the network. Using a simulation that uses real-world data collected from Sage, a distributed sensor network, we analyze our policy's performance in dynamic situations where the required QoS and the nodes' capabilities change, and verify that it can adapt and return a feasible solution while accounting for those changes.