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Edge Server Selection with Round-Robin-Based Task Processing in Multiserver Mobile Edge Computing.

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Summary
This summary is machine-generated.

Mobile edge computing (MEC) with round-robin scheduling significantly reduces task delays for applications like AR and telemedicine. This approach handles continuous, stochastic task arrivals more effectively than traditional methods.

Keywords:
computation delayedge computingedge server selectionprocessor sharinground robintask offloading

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Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Mobile edge computing (MEC) is crucial for next-generation networks (5G, 6G+) supporting demanding applications.
  • Increasing network density necessitates efficient task offloading strategies for multiple edge servers.
  • Existing research often simplifies task arrival and queueing models, limiting real-world applicability.

Purpose of the Study:

  • To introduce a novel framework for task offloading in mobile edge computing.
  • To investigate the performance of round-robin task scheduling with continuous, stochastic task arrivals.
  • To compare various server selection mechanisms in a multi-server MEC environment.

Main Methods:

  • Developed a task offloading framework for multiple edge servers using round-robin scheduling.
  • Modeled continuous and stochastic task arrivals from multiple users.
  • Conducted extensive simulations to evaluate system performance and compare server selection strategies.

Main Results:

  • Round-robin task scheduling significantly reduces task delay compared to traditional models.
  • The proposed framework demonstrates improved efficiency in handling realistic, dynamic workloads.
  • Comparative analysis of server selection mechanisms provides insights for practical deployment.

Conclusions:

  • Round-robin scheduling is a highly effective strategy for task offloading in mobile edge computing.
  • The study's realistic modeling advances understanding of MEC system performance.
  • Findings offer valuable guidance for optimizing edge computing resource management and task distribution.