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However, they should decide what computation\u2010intensive parts are appropriate for offloading to be beneficial instead of local execution on the mobile device. Moreover, in the real world, different types of clouds\/servers with heterogeneous processing speeds are available that should be considered for offloading. Because making offloading decision in multisite context is an NP\u2010complete, obtaining an optimal solution is time consuming. Hence, we use a near optimal decision algorithm to find the best\u2010possible partitioning for offloading to multisite clouds\/servers. We use a genetic algorithm and adjust it for multisite offloading problem. Also, genetic operators are modified to reduce the ineffective solutions and hence obtain the best\u2010possible solutions in a reasonable time. We evaluated the efficiency of the proposed method using graphs of real mobile applications in simulation experiments. The evaluation results demonstrate that our proposal outperforms other counterparts in terms of energy consumption, execution time, and weighted cost model.<\/jats:p>","DOI":"10.1002\/dac.3241","type":"journal-article","created":{"date-parts":[[2016,12,9]],"date-time":"2016-12-09T14:04:28Z","timestamp":1481292268000},"source":"Crossref","is-referenced-by-count":25,"title":["A genetic\u2010based decision algorithm for multisite computation offloading in mobile cloud computing"],"prefix":"10.1002","volume":"30","author":[{"given":"Mohammad","family":"Goudarzi","sequence":"first","affiliation":[{"name":"Computer Engineering School Iran University of Science and Technology (IUST) Tehran Iran"}]},{"given":"Mehran","family":"Zamani","sequence":"additional","affiliation":[{"name":"Computer Engineering School Iran University of Science and Technology (IUST) Tehran Iran"}]},{"given":"Abolfazl","family":"Toroghi\u00a0Haghighat","sequence":"additional","affiliation":[{"name":"Department of 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