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Optimizing Service Placement in Edge-to-Cloud AR/VR Systems using a Multi-Objective Genetic Algorithm

Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed, Calin Curescu

TL;DR

A Multi-Objective Genetic Algorithm to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments and can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods.

Abstract

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67\% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97\% infrastructure (hardware) and 95\% services (software).

Optimizing Service Placement in Edge-to-Cloud AR/VR Systems using a Multi-Objective Genetic Algorithm

TL;DR

A Multi-Objective Genetic Algorithm to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments and can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods.

Abstract

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67\% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97\% infrastructure (hardware) and 95\% services (software).
Paper Structure (27 sections, 25 equations, 7 figures, 11 tables)

This paper contains 27 sections, 25 equations, 7 figures, 11 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. SYSTEM MODEL
  4. User and helper model
  5. Service model
  6. Infrastructure and network model
  7. OPTIMIZATION MODEL
  8. Data transmission delay model
  9. Execution time model
  10. Response time model
  11. Reliability model
  12. Objective function
  13. PROPOSED SOLUTION (MOGA)
  14. Chromosome encoding
  15. Fitness function
  16. ...and 12 more sections

Figures (7)

  • Figure 1: An example DAG for an AR/VR application
  • Figure 2: Multi-tier edge-to-cloud infrastructure
  • Figure 3: Chromosome encoding
  • Figure 4: Selecting the best configuration for MOGA in different scales by the Pareto Front-based method
  • Figure 5: Convergence process of MOGA in different scales
  • ...and 2 more figures