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GWS-A Collaborative Load-Balancing Algorithm for Internet-of-Things.

Hongyu Xiao1,2, Zhenjiang Zhang3,4, Zhangbing Zhou5

  • 1Department of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China. yuhongxiao@aliyun.com.

Sensors (Basel, Switzerland)
|August 2, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a collaborative load-balancing algorithm for fog computing nodes, replacing traditional methods with time-sharing. The new game-theory based work-stealing algorithm enhances overall performance and efficiency in fog computing environments.

Keywords:
Internet-of-ThingsNash bargaining solutionPareto optimalitycollaborativefog computingschedulingtime-sharing

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Game Theory

Background:

  • Fog computing nodes (FCNs) traditionally use first-come-first-service (FCFS), which can lead to inefficiencies.
  • Load balancing is critical for optimizing resource utilization and performance in distributed systems like fog computing.

Purpose of the Study:

  • To propose a novel collaborative load-balancing algorithm for FCNs using a time-sharing (TS) mechanism.
  • To enhance FCN performance by achieving Pareto optimality through cooperative strategies.

Main Methods:

  • Replaced the FCFS mechanism with the TS mechanism in FCNs.
  • Developed a collaborative load-balancing algorithm based on a work-stealing scheduling variant.
  • Applied the Nash bargaining solution (NBS) from cooperative game theory to coordinate FCNs.

Main Results:

  • The proposed game-theory based work-stealing algorithm (GWS) was simulated.
  • Simulation results demonstrated that GWS outperforms the classical work-stealing algorithm (CWS).
  • The collaborative approach improved the performance of individual FCNs.

Conclusions:

  • The novel GWS algorithm effectively balances load in FCNs using TS and game theory.
  • Cooperative strategies based on NBS lead to improved system-wide performance and Pareto optimality.
  • This approach offers a significant advancement over traditional load-balancing methods in fog computing.