A Semantically Rich Framework to Automate Cloud Service Level Agreements
| dc.contributor.author | Natolana Ganapathy, Divya | |
| dc.contributor.author | Joshi, Karuna | |
| dc.date.accessioned | 2022-02-07T15:43:52Z | |
| dc.date.available | 2022-02-07T15:43:52Z | |
| dc.date.issued | 2022-01-11 | |
| dc.description.abstract | Consumers 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. | en_US |
| dc.description.sponsorship | This research was partially supported by a DoD supplement to the NSF award 1747724, Phase I IUCRC UMBC: Center for Accelerated Real time Analytics (CARTA). | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/abstract/document/9678067 | en_US |
| dc.format.extent | 12 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2dzez-rkts | |
| dc.identifier.citation | D. Natolana Ganapathy and K. P. Joshi, "A Semantically Rich Framework to Automate Cloud Service Level Agreements," in IEEE Transactions on Services Computing, doi: 10.1109/TSC.2022.3140585. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/TSC.2022.3140585 | |
| dc.identifier.uri | http://hdl.handle.net/11603/24128 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | © 2022 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 | UMBC Ebiquity Research Group | |
| dc.title | A Semantically Rich Framework to Automate Cloud Service Level Agreements | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-6354-1686 | en_US |