Multi-Objective Load Balancing for Heterogeneous Edge-Based Object Detection Systems

TL;DR

This paper introduces a multi-objective load balancing approach for heterogeneous edge-based object detection, optimizing for latency and energy while maintaining acceptable accuracy in dynamic IoT environments.

Contribution

It presents a novel two-stage decision mechanism that dynamically balances accuracy, latency, and energy in heterogeneous edge computing for object detection.

Findings

Halves energy consumption compared to baseline.

Reduces end-to-end latency by 80%.

Maintains detection accuracy within 10% of accuracy-centric methods.

Abstract

The rapid proliferation of the Internet of Things (IoT) and smart applications has led to a surge in data generated by distributed sensing devices. Edge computing is a mainstream approach to managing this data by pushing computation closer to the data source, typically onto resource-constrained devices such as single-board computers (SBCs). In such environments, the unavoidable heterogeneity of hardware and software makes effective load balancing particularly challenging. In this paper, we propose a multi-objective load balancing method tailored to heterogeneous, edge-based object detection systems. We study a setting in which multiple device-model pairs expose distinct accuracy, latency, and energy profiles, while both request intensity and scene complexity fluctuate over time. To handle this dynamically varying environment, our approach uses a two-stage decision mechanism: it first performs accuracy-aware filtering to identify suitable device-model candidates that provide accuracy within the acceptable range, and then applies a weighted-sum scoring function over expected latency and energy consumption to select the final execution target. We evaluate the proposed load balancer through extensive experiments on real-world datasets, comparing against widely used baseline strategies. The results indicate that the proposed multi-objective load balancing method halves energy consumption and achieves an 80% reduction in end-to-end latency, while incurring only a modest, up to 10%, decrease in detection accuracy relative to an accuracy-centric baseline.