{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:21:21Z","timestamp":1750306881608,"version":"3.41.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2012,12,18]],"date-time":"2012-12-18T00:00:00Z","timestamp":1355788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGOPS Oper. Syst. Rev."],"published-print":{"date-parts":[[2012,12,18]]},"abstract":"<jats:p>Increasingly, storage vendors are finding it difficult to leverage existing white-box and black-box modeling techniques to build robust system models that can predict system behavior in the emerging dynamic and multi-tenant data centers. White-box models are becoming brittle because the model builders are not able to keep up with the innovations in the storage system stack, and black-box models are becoming brittle because it is increasingly difficult to a priori train the model for the dynamic and multi-tenant data center environment. Thus, there is a need for innovation in system model building area.<\/jats:p>\n          <jats:p>In this paper we present a machine learning based blackbox modeling algorithm called M-LISP that can predict system behavior in untrained region for these emerging multitenant and dynamic data center environments. We have implemented and analyzed M-LISP in real environments and the initial results look very promising. We also provide a survey of some common machine learning algorithms and how they fare with respect to satisfying the modeling needs of the new data center environments.<\/jats:p>","DOI":"10.1145\/2421648.2421653","type":"journal-article","created":{"date-parts":[[2013,1,2]],"date-time":"2013-01-02T13:23:15Z","timestamp":1357132995000},"page":"20-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Model building for dynamic multi-tenant provider environments"],"prefix":"10.1145","volume":"46","author":[{"given":"Jayanta","family":"Basak","sequence":"first","affiliation":[{"name":"NetApp India Private Ltd., Advanced Technology Group, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kushal","family":"Wadhwani","sequence":"additional","affiliation":[{"name":"NetApp India Private Ltd., Advanced Technology Group, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaladhar","family":"Voruganti","sequence":"additional","affiliation":[{"name":"NetApp Inc., Advanced Technology Group, Sunnyvale, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srinivasan","family":"Narayanamurthy","sequence":"additional","affiliation":[{"name":"NetApp India Private Ltd., Advanced Technology Group, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vipul","family":"Mathur","sequence":"additional","affiliation":[{"name":"NetApp India Private Ltd., Advanced Technology Group, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siddhartha","family":"Nandi","sequence":"additional","affiliation":[{"name":"NetApp India Private Ltd., Advanced Technology Group, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,12,18]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1162\/neco.2006.18.9.2062"},{"key":"e_1_2_1_2_1","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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Petsche, editors, Advances in Neural Information Processing Systems 9 , pages 281 -- 287 . MIT Press , Cambridge, MA, USA , 1997 . V. Vapnik, S. Golowich, and A. Smola. Support vector method for function approximation, regression estimation, and signal processing. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 281--287. 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