Network Load Balancing Methods: Experimental Comparisons and Improvement

10/18/2017
by   Shafinaz Islam, et al.
0

Load balancing algorithms play critical roles in systems where the workload has to be distributed across multiple resources, such as cores in multiprocessor system, computers in distributed computing, and network links. In this paper, we study and evaluate four load balancing methods: random, round robin, shortest queue, and shortest queue with stale load information. We build a simulation model and compare mean delay of the systems for the load balancing methods. We also provide a method to improve shortest queue with stale load information load balancing. A performance analysis for the improvement is also presented in this paper.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/17/2020

Performance Analysis of Load Balancing Policies with Memory

Joining the shortest or least loaded queue among d randomly selected que...
research
04/11/2019

Survey of Major Load Balancing Algorithms in Distributed System

The classification of the most used load balancing algorithms in distrib...
research
01/27/2022

Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center

This paper presents the network load balancing problem, a challenging re...
research
01/14/2018

Distributed dynamic load balancing for task parallel programming

In this paper, we derive and investigate approaches to dynamically load ...
research
08/09/2022

Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems

Recent years have seen a great increase in the capacity and parallel pro...
research
06/26/2020

Dynamic Constraint-based Influence Framework and its Application in Stochastic Modeling of Load Balancing

Components connected over a network influence each other and interact in...
research
10/11/2011

Multiple ant-bee colony optimization for load balancing in packet-switched networks

One of the important issues in computer networks is "Load Balancing" whi...

Please sign up or login with your details

Forgot password? Click here to reset