Browsing by Author "Natolana Ganapathy, Divya"
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Item A Semantically Rich Framework to Automate Cloud Service Level Agreements(IEEE, 2022-01-11) Natolana Ganapathy, Divya; Joshi, KarunaConsumers evaluate and choose cloud-based services based on the Service Level Agreements (SLA). These agreements list the service terms and metrics to be agreed upon by the service providers and the customers. Current cloud SLAs are text 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 compare different provider SLAs. 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 knowledge graph helping the user to make a calculated decision in choosing a provider. Our framework captures the key terms, measures, and deontic rules, in the form of obligations and permissions present in the cloud SLAs. In this paper, we discuss our framework, technique, and challenges in automating the cloud services agreement. We also describe our results and their validation against well-established standards.Item Semantically Rich Framework to Automate KnowledgeExtraction from Cloud Service Level Agreement(2020-01-01) Natolana Ganapathy, Divya; Joshi, Karuna Pande; Computer Science and Electrical Engineering; Computer ScienceConsumers 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.