Semantically Rich Framework to Automate KnowledgeExtraction from Cloud Service Level Agreement
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Date
2020-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Abstract
Consumers evaluate the performance of their cloud-based services by monitoring the Service Level Agreements (SLA) that list the service terms and metrics agreed with the service providers. Current Cloud SLAs are documents that require significant manual effort to parse and determine if providers meet the SLAs. Moreover, due to the lack of standardization, providers differ in the way they define the terms and metrics, making it more difficult to ensure continuous SLA monitoring. We have developed a novel framework to significantly automate the process of extracting knowledge embedded in cloud SLAs and representing it in a semantically rich Ontology. Our framework captures the key terms, standards,remedies for noncompliance and roles and responsibilities, in the form of deontic statements and their actors from cloud SLAs. Its mostly built on major cloud SLAs, but could be adapted to other domains as well. In this theses `Semantically rich framework to automate knowledge extraction from cloud SLA' , we discuss the challenges in automating cloud services management and how we address these with our framework.