scholarly journals Reduction of wasted energy in a volunteer computing system through Reinforcement Learning

2014 ◽  
Vol 4 (4) ◽  
pp. 262-275 ◽  
Author(s):  
A. Stephen McGough ◽  
Matthew Forshaw
Keyword(s):  
Reinforcement Learning ◽  
Computing System ◽  
Volunteer Computing ◽  
Wasted Energy

scholarly journals DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)

2021 ◽  
Vol 3 (1) ◽  
pp. 69-98
Author(s):  
Paul Gautier ◽  
Johann Laurent
Keyword(s):  
Reinforcement Learning ◽  
Task Allocation ◽  
Computing System ◽  
Processing Load ◽  
Robot System ◽  
Research Challenges ◽  
Local Solutions ◽  
Overall Efficiency ◽  
Robot Task ◽  
Multi Robot

Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.

Optimizing Both the User Requirements and the Load Balancing in the Volunteer Computing System by using Markov Chain Model

2018 ◽  
Vol 14 (1) ◽  
pp. 35-62 ◽  
Author(s):  
Abdeldjalil Ledmi ◽  
Hakim Bendjenna ◽  
Hemam Sofiane Mounine
Keyword(s):  
Stochastic Process ◽  
Cloud Computing ◽  
Markov Chain ◽  
System Performance ◽  
Markov Chain Model ◽  
Computing System ◽  
Volunteer Computing ◽  
User Requirements ◽  
Chain Model ◽  
Computing Systems

This article describes how in volunteer cloud computing systems, some resources are volunteered by the hosts. These systems became more powerful and attractive because they provide a highest power computing. However, to satisfy the user requirements and the system performance in this kind of the system is a crucial challenge. In this article, the authors propose a new architecture for the volunteer cloud computing systems to allow balancing the load between volunteer clouds in a decentralized manner, and between resources inside a volunteer cloud in centralized manner. Moreover, their proposal shows more advantages: First, selecting a resource according to the user requirements and to the system performance. Second, estimating the volunteer resource failure probability by using the stochastic process Markov chain model. Experimental results using the PeerSim Simulator is established to verify the efficacy of the proposed system and promising results are obtained.

Virtual EZ Grid

2012 ◽  
Vol 3 (1) ◽  
pp. 74-85 ◽  
Author(s):  
Mohamed Ben Belgacem ◽  
Nabil Abdennadher ◽  
Marko Niinimaki
Keyword(s):  
Distributed Computing ◽  
Computing System ◽  
Medical Applications ◽  
Volunteer Computing ◽  
Computing Platform

This paper presents the Virtual EZ Grid project, based on the XtremWeb-CH (XWCH) volunteer computing platform. The goal of the project is to introduce a flexible distributed computing system, with (i) an infrastructure with a non-trivial amount of computing resources from various institutes, (ii) a stable platform that manages these computing resources and provides advanced interfaces for applications, and (iii) a set of applications that take benefit of the platform. This paper concentrates on the application support of the new version of XWCH, and describes how two medical applications, MedGIFT and NeuroWeb, utilise it.

Sign in / Sign up

Export Citation Format

Share Document