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Joint offloading decision and resource allocation for multi-user multi-task mobile cloud. In IEEE ICC, pages 1--6, 2016."},{"key":"e_1_3_2_2_22_1","volume-title":"Accessed","year":"2021","unstructured":"scikit learn. cluster KMeans. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.KMeans.html , Accessed 2021 . scikit learn. cluster KMeans. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.KMeans.html, Accessed 2021."},{"key":"e_1_3_2_2_23_1","volume-title":"Accessed","year":"2021","unstructured":"pyclustering. cluster kmedoids. https:\/\/pyclustering.github.io\/docs\/0.9.2\/html\/de\/dfd\/namespacepyclustering_1_1cluster_1_1kmedoids.html , Accessed 2021 . pyclustering. cluster kmedoids. https:\/\/pyclustering.github.io\/docs\/0.9.2\/html\/de\/dfd\/namespacepyclustering_1_1cluster_1_1kmedoids.html, Accessed 2021."},{"key":"e_1_3_2_2_24_1","volume-title":"Accessed","author":"Neighbors Nearest","year":"2021","unstructured":"scikit learn. 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