{"corpus_id":257809615,"paper_sha":"f6371e8d05f7f13910ac0d631e1c9c1492e4d4f6","doi":"10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00184","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":null,"dblp_id":"conf/hpcc/TianEE22","acl_id":null,"title":"Predicting Cloud Performance Using Real-time VM-level Metrics","year":2022,"publication_date":"2022-12-01","venue":"2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)","journal":{"name":"2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)","pages":"1165-1172","volume":null},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle"],"pubmed_pub_types":null,"s2_fields_of_study":["Computer Science"],"reference_count":34,"citation_count":1,"influential_citation_count":0,"is_open_access":true,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":"https://eprints.gla.ac.uk/295748/2/295748.pdf","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/f6371e8d05f7f13910ac0d631e1c9c1492e4d4f6","s2_open_access_license":"other-oa","s2_open_access_status":"GREEN","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"The vast range of cloud service offerings can easily overwhelm users and cause them to select ones that are unsuitable for their needs. As such, the literature has a number of proposals to predict application performance based on a history of executing a certain application or benchmark. However, this requires significant cost to pre-run the application on different service levels before identifying the most suitable one. We propose a machine learning model that enables a cloud user to select the optimal cloud service based on real-time execution without the need to do an exhaustive search. We develop and test this model using a popular benchmark suite on Microsoft Azure, a leading cloud provider. The key insight of this work is that fluctuations in rather than the absolute amount of utilization levels of CPU and memory can be strongly indicative of how well an application is executing.","claims":[{"public_id":"cl_a246bac1c507d58aa2bd97ff07909e18","status":"active","text":"A machine learning model can select the optimal cloud service based on real-time execution without exhaustive search.","confidence":0.96,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_a246bac1c507d58aa2bd97ff07909e18"},{"public_id":"cl_ad90737487bcc5a351dd0d2902bd076c","status":"active","text":"Fluctuations in CPU and memory utilization are strongly indicative of how well an application is executing, whereas absolute utilization levels are less informative.","confidence":0.92,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous 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