{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T10:30:18Z","timestamp":1762338618129,"version":"build-2065373602"},"reference-count":0,"publisher":"European Alliance for Innovation n.o.","issue":"10","license":[{"start":{"date-parts":[[2016,1,4]],"date-time":"2016-01-04T00:00:00Z","timestamp":1451865600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EAI Endorsed Trans Energy Web"],"abstract":"<jats:p>Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all possible system states. Approaches to determine performance and power for a state not included in the measured subset use simple interpolation, such as nearest neighbor interpolation, or define state switching rules. This leads to a loss in accuracy, as unmeasured system states are not considered. In this paper, we compare different interpolation functions and automatically configure and select functions for a given domain or measurement set. We evaluate our approach by comparing interpolation of measurement data subsets against power and performance measurements on a commodity server. We show that for non-extrapolating models interpolation is significantly more accurate than regression, with our automatically configured interpolation function improving modeling accuracy up to 43.6%.<\/jats:p>","DOI":"10.4108\/eai.14-12-2015.2262579","type":"journal-article","created":{"date-parts":[[2016,1,4]],"date-time":"2016-01-04T09:03:04Z","timestamp":1451898184000},"page":"e4","source":"Crossref","is-referenced-by-count":4,"title":["Univariate Interpolation-based Modeling of Power and Performance"],"prefix":"10.4108","volume":"3","author":[{"given":"J\u00f3akim von","family":"Kistowski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Kounev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2587","published-online":{"date-parts":[[2016,1,4]]},"container-title":["EAI Endorsed Transactions on Energy Web"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/1045\/886","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/1045\/886","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T10:22:30Z","timestamp":1762338150000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/view\/1045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,4]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5,18]]}},"URL":"https:\/\/doi.org\/10.4108\/eai.14-12-2015.2262579","relation":{},"ISSN":["2032-944X"],"issn-type":[{"type":"electronic","value":"2032-944X"}],"subject":[],"published":{"date-parts":[[2016,1,4]]}}}