Mobile computation offloading of computational intensive tasks from mobile devices to surrogate cloud servers has been recently envisaged as a promising technique to enhance the computational capacity of the mobile devices. Within this framework we consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio and computational resources in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications. To solve this non-convex problem we hinge on successive convex approximation techniques by showing that the original problem can be decomposed in parallel convex subproblems. Hence we devise an iterative algorithm which can be implemented in a distributed manner across the access points through dual/primal decomposition techniques requiring limited coordination/signaling with the cloud.

Distributed joint optimization of radio and computational resources for mobile cloud computing / Sardellitti, S.; Scutari, G.; Barbarossa, S.. - (2014), pp. 211-216. ((Intervento presentato al convegno 2014 3rd IEEE International Conference on Cloud Networking, CloudNet 2014 tenutosi a Luxembourg; Luxembourg [10.1109/CloudNet.2014.6968994].

Distributed joint optimization of radio and computational resources for mobile cloud computing

Sardellitti S.;Barbarossa S.
2014

Abstract

Mobile computation offloading of computational intensive tasks from mobile devices to surrogate cloud servers has been recently envisaged as a promising technique to enhance the computational capacity of the mobile devices. Within this framework we consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio and computational resources in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications. To solve this non-convex problem we hinge on successive convex approximation techniques by showing that the original problem can be decomposed in parallel convex subproblems. Hence we devise an iterative algorithm which can be implemented in a distributed manner across the access points through dual/primal decomposition techniques requiring limited coordination/signaling with the cloud.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1395440
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