Using swarm intelligence to optimize the energy consumption for distributed systems

Neil Bergmann; Chung, YY; Yang, X; Chen, Z; Yeh, WC; He, X; Jurdak, R

Using swarm intelligence to optimize the energy consumption for distributed systems

Neil Bergmann Chung, YY Yang, X Chen, Z Yeh, WC He, X

Jurdak, R

Permalink

Publication Type:: Journal Article
Citation:: Modern Applied Science, 2013, 7 (6), pp. 59 - 66
Issue Date:: 2013-08-21

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (232.56 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Neil Bergmann	en_US
dc.contributor.author	Chung, YY	en_US
dc.contributor.author	Yang, X	en_US
dc.contributor.author	Chen, Z	en_US
dc.contributor.author	Yeh, WC	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Jurdak, R	en_US
dc.date.issued	2013-08-21	en_US
dc.identifier.citation	Modern Applied Science, 2013, 7 (6), pp. 59 - 66	en_US
dc.identifier.issn	1913-1844	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26559
dc.description.abstract	Large, distributed, network-based computing systems (also known as Cloud Computing) have recently gained significant interest. We expect significantly more applications or web services will be relying on network-based servers, therefore reducing the energy consumption of these systems would be beneficial for companies to save their budgets on running their machines as well as cooling down their infrastructures. Dynamic Voltage Scaling can save significant energy for these systems, but it faces the challenge of efficient and balanced parallelization of tasks in order to maximize energy savings while maintaining desired performance levels. This paper proposes our Simplified Swarm Optimization (SSO) method to reduce the energy consumption for distributed systems with Dynamic Voltage Scaling. The results of SSO have been compared to the most popular evolutionary Particle Swarm Optimization (PSO) algorithm and have shown to be more efficient and effective, reducing both the execution time for scheduling and makespan.	en_US
dc.relation.ispartof	Modern Applied Science	en_US
dc.relation.isbasedon	10.5539/mas.v7n6p59	en_US
dc.title	Using swarm intelligence to optimize the energy consumption for distributed systems	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	7	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Large, distributed, network-based computing systems (also known as Cloud Computing) have recently gained significant interest. We expect significantly more applications or web services will be relying on network-based servers, therefore reducing the energy consumption of these systems would be beneficial for companies to save their budgets on running their machines as well as cooling down their infrastructures. Dynamic Voltage Scaling can save significant energy for these systems, but it faces the challenge of efficient and balanced parallelization of tasks in order to maximize energy savings while maintaining desired performance levels. This paper proposes our Simplified Swarm Optimization (SSO) method to reduce the energy consumption for distributed systems with Dynamic Voltage Scaling. The results of SSO have been compared to the most popular evolutionary Particle Swarm Optimization (PSO) algorithm and have shown to be more efficient and effective, reducing both the execution time for scheduling and makespan.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26559