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Conversely, their high growing energy consumption rate is the major problem to be addressed. Cloud providers are in a hunger to identify different solutions to tackle energy management and carbon emission. In this work, a multi-cloud environment is modeled as geographically distributed data centers with varying solar power generation corresponding to its location, electricity price, carbon emission, and carbon tax. The energy management of the workload allocation algorithm is strongly dependent on the nature of the application considered. The task deadline and brownout information is used to bring in variation in task types. The renewable energy-aware workload allocation algorithm adaptive to task nature is proposed with migration policy to explore its impact on carbon emission, total energy cost, brown and renewable power consumption.<\/jats:p>","DOI":"10.3233\/jifs-189887","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T12:56:43Z","timestamp":1618577803000},"page":"5677-5689","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Task aware optimized energy cost and carbon emission-based virtual machine placement in sustainable data centers"],"prefix":"10.1177","volume":"41","author":[{"given":"T.","family":"Renugadevi","sequence":"first","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India"}]},{"given":"K.","family":"Geetha","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India"}]}],"member":"179","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"AdnanM.A. SugiharaR. and GuptaR.K. 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