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dc.contributor.authorHussain, Omar
dc.contributor.authorDong, Hai
dc.contributor.authorSingh, Jaipal
dc.contributor.editorRobert Meersman, Tharam Dillon and Pilar Herrero
dc.date.accessioned2017-01-30T13:04:13Z
dc.date.available2017-01-30T13:04:13Z
dc.date.created2011-03-21T20:01:28Z
dc.date.issued2010
dc.identifier.citationHussain, Omar and Dong, Hai and Singh, Jaipal. 2010. Semantic Similarity Model for Risk Assessment in Forming Cloud Computing SLAs, in Meersman, R. and Dillon, T.S. and Herrero, P. (ed), Lecture Notes in Computer Science, Volume 6427: On the Move to Meaningful Internet Systems (OTM 2010). pp. 843-860. Germany: Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/28292
dc.description.abstract

Cloud computing has enabled users to access various resources and applications as a service and in return pay the provider only for the time for which they are used. Service Level Agreements (SLA) are formed between the user and provider to ensure that the required services and applications are delivered as expected. With the increase of public cloud providers, challenges such as availability, reliability, security, privacy and transactional risk demand detail assessment while forming SLAs. This paper focuses on one sub-category of transactional risk while forming SLAs; namely performance risk. We argue that performance risk assessment should be done by the user before entering into a SLA with a service provider. We propose to measure performance risk according to the specific context and assessment criteria with the aid of a semantic similarity model for the SLA requirement being negotiated in a cloud computing environment. We show through simulations that the performance risk analysis is more accurate using semantic similarity matching compared with risk analysis without semantic similarity matching.

dc.publisherSpringer
dc.subjectContext
dc.subjectAssessment Criteria
dc.subjectCloud Computing
dc.subjectSemantic Similarity Model
dc.subjectService Level Agreement
dc.subjectPerformance Risk
dc.titleSemantic Similarity Model for Risk Assessment in Forming Cloud Computing SLAs
dc.typeBook Chapter
dcterms.source.startPage843
dcterms.source.endPage860
dcterms.source.titleLecture notes in computer science, volume 6427: on the move to meaningful internet systems (OTM 2010)
dcterms.source.isbn9783642169489
dcterms.source.placeHeidelberg
dcterms.source.chapter36
curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available


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