RILNET: A Reinforcement Learning Based Load Balancing Approach for Datacenter Networks

2019 ◽  
pp. 44-55
Author(s):  
Qinliang Lin ◽  
Zhibo Gong ◽  
Qiaoling Wang ◽  
Jinlong Li
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Datacenter Networks

Cloud Load Balancing and Reinforcement Learning

2018 ◽  
pp. 266-291
Author(s):  
Abdelghafour Harraz ◽  
Mostapha Zbakh
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Load Balancing ◽  
Decision Process ◽  
Cloud System ◽  
Human Intervention ◽  
Q Learning ◽  
State Action ◽  
Learning Techniques ◽  
Markov Decision

Artificial Intelligence allows to create engines that are able to explore, learn environments and therefore create policies that permit to control them in real time with no human intervention. It can be applied, through its Reinforcement Learning techniques component, using frameworks such as temporal differences, State-Action-Reward-State-Action (SARSA), Q Learning to name a few, to systems that are be perceived as a Markov Decision Process, this opens door in front of applying Reinforcement Learning to Cloud Load Balancing to be able to dispatch load dynamically to a given Cloud System. The authors will describe different techniques that can used to implement a Reinforcement Learning based engine in a cloud system.

MARVEL: Enabling controller load balancing in software-defined networks with multi-agent reinforcement learning

2020 ◽  
Vol 177 ◽  
pp. 107230
Author(s):  
Penghao Sun ◽  
Zehua Guo ◽  
Gang Wang ◽  
Julong Lan ◽  
Yuxiang Hu
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Software Defined Networks ◽  
Multi Agent

VMS: Load Balancing Based on the Virtual Switch Layer in Datacenter Networks

2020 ◽  
Vol 38 (6) ◽  
pp. 1176-1190 ◽  
Author(s):  
Yiran Zhang ◽  
Jun Bi ◽  
Zhaogeng Li ◽  
Yu Zhou ◽  
Yangyang Wang
Keyword(s):  
Load Balancing ◽  
Datacenter Networks

scholarly journals Adaptive Load Balancing: A Study in Multi-Agent Learning

1995 ◽  
Vol 2 ◽  
pp. 475-500 ◽  
Author(s):  
A. Schaerf ◽  
Y. Shoham ◽  
M. Tennenholtz
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Distributed System ◽  
Adaptive Behavior ◽  
Local Information ◽  
System Efficiency ◽  
Agent Learning ◽  
Multi Agent ◽  
Explicit Communication

We study the process of multi-agent reinforcement learning in the context ofload balancing in a distributed system, without use of either centralcoordination or explicit communication. We first define a precise frameworkin which to study adaptive load balancing, important features of which are itsstochastic nature and the purely local information available to individualagents. Given this framework, we show illuminating results on the interplaybetween basic adaptive behavior parameters and their effect on systemefficiency. We then investigate the properties of adaptive load balancing inheterogeneous populations, and address the issue of exploration vs.exploitation in that context. Finally, we show that naive use ofcommunication may not improve, and might even harm system efficiency.

Sign in / Sign up

Export Citation Format

Share Document