Replication of “Tail” Computations in a Desktop Grid Project

2020 ◽  
pp. 611-621
Author(s):  
Evgeny Ivashko ◽  
Natalia Nikitina
Keyword(s):  
Desktop Grid

A Survey of Desktop Grid Scheduling

2018 ◽  
Vol 29 (12) ◽  
pp. 2882-2895 ◽  
Author(s):  
Evgeny Ivashko ◽  
Ilya Chernov ◽  
Natalia Nikitina
Keyword(s):  
Grid Scheduling ◽  
Desktop Grid

Scalable and effective peer-to-peer desktop grid system

2014 ◽  
Vol 17 (4) ◽  
pp. 1185-1201 ◽  
Author(s):  
Jik-Soo Kim ◽  
Beomseok Nam ◽  
Alan Sussman
Keyword(s):  
Peer To Peer ◽  
Grid System ◽  
Desktop Grid

Distributed Rendering in an Open Self-Organised Trusted Desktop Grid

2015 ◽  
Author(s):  
Jan Kantert ◽  
Henning Spiegelberg ◽  
Sven Tomforde ◽  
Jorg Hahner ◽  
Christian Muller-Schloer
Keyword(s):  
Desktop Grid ◽  
Distributed Rendering

How to Build an Open Source Render Farm Based on Desktop Grid Computing

2008 ◽  
pp. 268-278 ◽  
Author(s):  
Zeeshan Patoli ◽  
Michael Gkion ◽  
Abdullah Al-Barakati ◽  
Wei Zhang ◽  
Paul Newbury ◽  
...  
Keyword(s):  
Grid Computing ◽  
Open Source ◽  
Desktop Grid

Trust, Virtual Teams, and Grid Technology

2011 ◽  
pp. 1396-1412
Author(s):  
Genoveffa Jeni Giambona ◽  
Nicholas L.J. Silburn ◽  
David W. Birchall
Keyword(s):  
Decision Making ◽  
Competitive Advantage ◽  
Virtual Teams ◽  
Return On Investment ◽  
Source Code ◽  
Simulation Software ◽  
Grid Technology ◽  
Desktop Grid ◽  
Grid Middleware ◽  
Support Decision Making

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization are possible in the same amount of time) and management (as it can potentially increase the return on investment on existing resources).

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