Improving IoT Analytics through Selective Edge Execution

TL;DR

This paper proposes an adaptive algorithm that enables IoT devices to perform analytics locally or outsource to edge servers based on predicted performance gains, improving efficiency and response times.

Contribution

It introduces a novel dual subgradient-based algorithm that dynamically decides where to execute IoT analytics, optimizing resource use at the edge.

Findings

Algorithm effectively balances local and edge execution.

Adaptive approach improves response time and resource efficiency.

Minimal assumptions required for system parameters.

Abstract

A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes network resources. However, due to power and computing limitations, the devices often cannot support such resource-intensive routines and fail to accurately execute the analytics. In this work, we propose to improve the performance of analytics by leveraging edge infrastructure. We devise an algorithm that enables the IoT devices to execute their routines locally; and then outsource them to cloudlet servers, only if they predict they will gain a significant performance improvement. It uses an approximate dual subgradient method, making minimal assumptions about the statistical properties of the system's parameters. Our analysis demonstrates that our proposed algorithm can intelligently leverage the cloudlet, adapting to the service requirements.