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Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing

Shuyi Chen, Panagiotis Oikonomou, Zhengchang Hua, Nikos Tziritas, Karim Djemame, Nan Zhang, Georgios Theodoropoulos

TL;DR

The paper tackles the challenge of operating MEC systems powered exclusively by energy harvesting under dynamic user demand. It introduces an online heuristic that jointly schedules dependency-aware tasks and adapts energy use via Dynamic Voltage and Frequency Scaling (DVFS) and service migration based on real-time energy forecasts. The approach combines an initial CPM-guided placement with three energy-aware policies (Sufficient, Deficient, Mixed) and is validated through extensive simulations (7,200 rounds) using real-world DAG workloads, demonstrating improved latency and energy efficiency under EH constraints. This work enables sustainable, low-latency edge computing in grid-lacking environments and lays a framework for energy-aware, dependency-driven edge orchestration.

Abstract

Multi-access Edge Computing (MEC) delivers low-latency services by hosting applications near end-users. To promote sustainability, these systems are increasingly integrated with renewable Energy Harvesting (EH) technologies, enabling operation where grid electricity is unavailable. However, balancing the intermittent nature of harvested energy with dynamic user demand presents a significant resource allocation challenge. This work proposes an online strategy for an MEC system powered exclusively by EH to address this trade-off. Our strategy dynamically schedules computational tasks with dependencies and governs energy consumption through real-time decisions on server frequency scaling and service module migration. Experiments using real-world datasets demonstrate our algorithm's effectiveness in efficiently utilizing harvested energy while maintaining low service latency.

Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing

TL;DR

The paper tackles the challenge of operating MEC systems powered exclusively by energy harvesting under dynamic user demand. It introduces an online heuristic that jointly schedules dependency-aware tasks and adapts energy use via Dynamic Voltage and Frequency Scaling (DVFS) and service migration based on real-time energy forecasts. The approach combines an initial CPM-guided placement with three energy-aware policies (Sufficient, Deficient, Mixed) and is validated through extensive simulations (7,200 rounds) using real-world DAG workloads, demonstrating improved latency and energy efficiency under EH constraints. This work enables sustainable, low-latency edge computing in grid-lacking environments and lays a framework for energy-aware, dependency-driven edge orchestration.

Abstract

Multi-access Edge Computing (MEC) delivers low-latency services by hosting applications near end-users. To promote sustainability, these systems are increasingly integrated with renewable Energy Harvesting (EH) technologies, enabling operation where grid electricity is unavailable. However, balancing the intermittent nature of harvested energy with dynamic user demand presents a significant resource allocation challenge. This work proposes an online strategy for an MEC system powered exclusively by EH to address this trade-off. Our strategy dynamically schedules computational tasks with dependencies and governs energy consumption through real-time decisions on server frequency scaling and service module migration. Experiments using real-world datasets demonstrate our algorithm's effectiveness in efficiently utilizing harvested energy while maintaining low service latency.

Paper Structure

This paper contains 15 sections, 14 equations, 7 figures, 1 table, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Formulation
  4. EH-MEC network model
  5. Application Model
  6. Communication and Computation Model
  7. Energy and Battery Model
  8. Problem formulation
  9. Dynamic Scaling and Migration Algorithm
  10. Initial Assignment
  11. Dynamic Resource Scheduling
  12. Experimental Evaluation
  13. Performance Indicators & Setup
  14. Performance Assessment
  15. Conclusions and Future Work

Figures (7)

  • Figure 1: Examples of the EH-MEC system status, network topology and applications.
  • Figure 2: The workflow of the dynamic algorithm proposed.
  • Figure 3: The energy harvesting power and amount of incoming tasks over time.
  • Figure 4: Results on latency and utilisation
  • Figure 5: Figure caption
  • ...and 2 more figures