Accelerating Skycube Computation with Partial and Parallel Processing for Service Selection

Dong, F; Luo, J; Jin, J; Shi, J; Yang, Y; Shen, J

Accelerating Skycube Computation with Partial and Parallel Processing for Service Selection

Dong, F Luo, J Jin, J Shi, J Yang, Y Shen, J

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Services Computing, 2020, 13, (6), pp. 969-984
Issue Date:: 2020-11-01

Closed Access

	Filename	Description	Size
	08067535.pdf	Published version	1.92 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Dong, F
dc.contributor.author	Luo, J
dc.contributor.author	Jin, J
dc.contributor.author	Shi, J
dc.contributor.author	Yang, Y
dc.contributor.author	Shen, J
dc.date.accessioned	2021-03-31T08:56:54Z
dc.date.available	2021-03-31T08:56:54Z
dc.date.issued	2020-11-01
dc.identifier.citation	IEEE Transactions on Services Computing, 2020, 13, (6), pp. 969-984
dc.identifier.issn	1939-1374
dc.identifier.issn	1939-1374
dc.identifier.uri	http://hdl.handle.net/10453/147729
dc.description.abstract	© 2008-2012 IEEE. Recently researchers use skyline techniques to optimize service selection procedure, where they can filter those low-quality web services from the large amount of candidates and return a much smaller high-quality service set. The skycube concept is adopted for quickly responding to the skyline queries with different combinations of Quality of Web Service (QoWS) parameters. As the skycube computation is quite time-consuming, it is a compelling challenge to accelerate this procedure. However, the current solutions usually have a number of redundant computations which will significantly affect the efficiency. To address such drawbacks, after an in-depth analysis of skycube computation procedure, we introduce a partial skycube, which only consists of the skylines with frequently used combinations of QoWS. Then the computational relationships between the skyline on one subspace and its parent-space are studied. Based on the relationships, we develop {\sf {ParCube}}ParCube algorithm to speedup partial skycube computation by reusing the intermediate comparison results. Meanwhile, at the execution phase, {\sf {ParCube}}ParCube can be further optimized with parallel execution mode and optimized scheduling strategy. Finally, we evaluate the efficiency and scalability of {\sf {ParCube}}ParCube on both single machine and cluster environment. The results show that {\sf {ParCube}}ParCube can efficiently compute partial skycube and scale well in cluster environment.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Services Computing
dc.relation.isbasedon	10.1109/TSC.2017.2762681
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.title	Accelerating Skycube Computation with Partial and Parallel Processing for Service Selection
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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 Information, Systems and Modelling
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-31T08:56:51Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	6

Abstract:

© 2008-2012 IEEE. Recently researchers use skyline techniques to optimize service selection procedure, where they can filter those low-quality web services from the large amount of candidates and return a much smaller high-quality service set. The skycube concept is adopted for quickly responding to the skyline queries with different combinations of Quality of Web Service (QoWS) parameters. As the skycube computation is quite time-consuming, it is a compelling challenge to accelerate this procedure. However, the current solutions usually have a number of redundant computations which will significantly affect the efficiency. To address such drawbacks, after an in-depth analysis of skycube computation procedure, we introduce a partial skycube, which only consists of the skylines with frequently used combinations of QoWS. Then the computational relationships between the skyline on one subspace and its parent-space are studied. Based on the relationships, we develop {\sf {ParCube}}ParCube algorithm to speedup partial skycube computation by reusing the intermediate comparison results. Meanwhile, at the execution phase, {\sf {ParCube}}ParCube can be further optimized with parallel execution mode and optimized scheduling strategy. Finally, we evaluate the efficiency and scalability of {\sf {ParCube}}ParCube on both single machine and cluster environment. The results show that {\sf {ParCube}}ParCube can efficiently compute partial skycube and scale well in cluster environment.

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

http://hdl.handle.net/10453/147729