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Progressive Traffic-Oriented Resource Management for Reducing Network Congestion in Edge Computing.

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

Entropy (Basel, Switzerland)

|April 30, 2021

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 Things cloud computing edge computing fog computing heuristic algorithm network architecture network management software 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.