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ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge.

Marios Avgeris1, Dimitrios Spatharakis1, Dimitrios Dechouniotis1

  • 1Department of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.

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Summary
This summary is machine-generated.

This study introduces an optimized resource allocation framework for mobile task offloading to Edge Computing. The proposed method balances energy consumption, delay, and load balancing, outperforming existing solutions.

Keywords:
Markov Random Fieldsedge computingenergy optimizationresource allocationtask offloading

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Edge Computing

Background:

  • Mobile applications demand increasing computational power, making traditional cloud offloading inefficient due to latency.
  • Edge Computing offers a solution but faces resource limitations, necessitating efficient task allocation.

Purpose of the Study:

  • To propose an optimal resource allocation framework for mobile task offloading to Edge Computing.
  • To balance energy consumption, end-to-end delay, and Edge load balancing.

Main Methods:

  • Developed an optimal resource allocation framework utilizing aggregated edge resources.
  • Introduced a Markov Random Field mechanism for excess workload distribution.
  • Incorporated a prediction mechanism for physical resource orchestration.

Main Results:

  • The proposed framework effectively manages task offloading in realistic scenarios with diverse applications and dynamic conditions.
  • Evaluated via modeling and simulation, the approach demonstrated superior performance compared to existing solutions.
  • Successfully balanced energy consumption, delay requirements, and Edge load.

Conclusions:

  • The proposed framework provides an efficient solution for mobile task offloading in Edge Computing environments.
  • The amalgamation of edge resources and intelligent workload distribution significantly improves performance metrics.
  • This research contributes to optimizing mobile application performance through advanced Edge Computing strategies.