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Related Experiment Video

Updated: Nov 7, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Progressive Traffic-Oriented Resource Management for Reducing Network Congestion in Edge Computing.

Won-Suk Kim1

  • 1Department of Multimedia Engineering, Andong National University, Andong 36729, Korea.

Entropy (Basel, Switzerland)
|April 30, 2021
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Summary

Edge computing offers low latency by moving cloud services to the network edge. This study proposes a novel algorithm for efficient edge server deployment, balancing computing resources and network traffic for diverse services.

Keywords:
Internet of Thingscloud computingedge computingfog computingheuristic algorithmnetwork architecturenetwork managementsoftware defined networking

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Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Edge computing provides low-latency, real-time processing by distributing cloud services to the network periphery.
  • Resource management in edge computing is complex due to its hierarchical, distributed, and heterogeneous nature.
  • Diverse cloud-based services (e.g., crowd sensing, deep learning, cloud gaming) have unique traffic and computing demands.

Purpose of the Study:

  • To develop an effective resource management algorithm for edge computing environments.
  • To address the challenge of deploying edge servers while considering service diversity, user behavior, and network performance.
  • To optimize the user experience for various latency-sensitive edge services.

Main Methods:

  • Proposed a novel algorithm for simultaneous consideration of computing resources and network traffic load in edge server deployment.
  • Algorithm generates candidate deployments based on server count, location, and client mapping, tailored to service characteristics.
  • A partial vector bin packing scheme is employed to finalize deployment plans, integrating traffic and resource constraints.

Main Results:

  • The proposed algorithm effectively balances computing resources and network traffic load for edge service deployment.
  • Simulations demonstrated the algorithm's capability to handle diverse service requirements and network conditions.
  • Evaluations considered realistic network service and device characteristics for practical applicability.

Conclusions:

  • The developed algorithm provides a robust solution for resource management in edge computing.
  • Optimized server deployment enhances user experience by meeting the demands of diverse edge services.
  • This approach contributes to more efficient and scalable edge computing infrastructure.