Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema, Adel N. Toosi

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.

Multi-Objective Load Balancing for Heterogeneous Edge-Based Object Detection 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.
Paper Structure (21 sections, 8 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 21 sections, 8 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation
  3. System Design and Implementation
  4. Problem Formulation
  5. Proposed Solution
  6. Stage 1: Accuracy-Based Filtering
  7. Stage 2: Multi-Objective Scoring via Weighted-Sum Method
  8. Performance evaluation
  9. Experimental Setup
  10. Dataset
  11. Experimental Testbed
  12. Experimental Procedure
  13. Experimental Results
  14. Proposed Load Balancer against Baseline Strategies
  15. Results Across Different $\gamma$ Values
  16. ...and 6 more sections

Figures (5)

  • Figure 1: Contrasting pedestrians scenarios: (a) High density scene; (b) Low density scene.
  • Figure 2: Accuracy, energy consumption and inference time for different group of images across object detection models.
  • Figure 3: The overall system architecture comprises vision-enabled IoT devices, a gateway, and heterogeneous edge computing resources.
  • Figure 4: Comparison of load balancing policies across different metrics under varying request concurrency. The shaded region around MO_gamma_50 indicates the range between MO_gamma_0 and MO_gamma_1.
  • Figure 5: Comparison of proposed load balancer with changing gamma value across different metrics under varying request concurrency.