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

2026-03-16

AI Summary

This work addresses the challenge of achieving effective load balancing for object detection across heterogeneous edge devices, where hardware disparities and dynamically varying workloads and scene complexities make it difficult to jointly optimize accuracy, latency, and energy consumption. To this end, the authors propose a multi-objective load balancing approach that employs a two-stage decision mechanism for coordinated optimization. In the first stage, candidate devices are filtered based on accuracy awareness; in the second stage, a weighted scoring function incorporating expected latency and energy consumption enables real-time scheduling. Experimental results demonstrate that, compared to baseline methods, the proposed scheme reduces end-to-end latency by 80% and energy consumption by 50%, while incurring no more than a 10% loss in accuracy, thereby significantly enhancing overall system efficiency.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Object Detection & Categorization
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• load balancing
• edge computing
• object detection
• heterogeneous systems
• multi-objective optimization

Innovation

• multi-objective load balancing
• heterogeneous edge computing
• object detection
• accuracy-aware filtering
• latency-energy tradeoff