We consider a scenario composed by multiple mobile users asking for computation offloading of their applications to a set of cloud servers. A set of radio access points, small cell base stations possibly coexisting with macro base stations, are available to provide radio proximity access to fixed computational resources. Our objective is to find the optimal assignment of each mobile user to a cloud server through the most convenient base station and, contextually, the optimal MIMO precoding matrices and computational rates (virtual machines) to each user, under latency constraints dictated by the users Quality of Experience (QoE). The radio resources assigned to users belonging to the same cell are orthogonal to each other, whereas users of different cells might interfere against each other. The latency constraint imposes a strict relationship between the time spent for transferring the program execution from the mobile device to the fixed server (and viceversa) and the time needed to execute the computation. To properly exploit this relationship, we formulate the computation offloading problem as a joint optimization of the radio and computational resources, with the objective of minimizing the overall energy consumption, at the mobile terminal side, while meeting the latency constraints. The resulting optimization problem is nonconvex in both the objective function and in the constraints. Nevertheless, by hinging on successive convex approximation techniques, we propose an iterative algorithm able to converge to a local optimal solution of the original nonconvex problem.

Distributed mobile cloud computing. joint optimization of radio and computational resources / Sardellitti, S.; Barbarossa, S.; Scutari, G.. - (2014), pp. 1505-1510. (Intervento presentato al convegno 2014 IEEE Globecom Workshops, GC Wkshps 2014 tenutosi a Austin; United States) [10.1109/GLOCOMW.2014.7063647].

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

Sardellitti S.;Barbarossa S.;
2014

Abstract

We consider a scenario composed by multiple mobile users asking for computation offloading of their applications to a set of cloud servers. A set of radio access points, small cell base stations possibly coexisting with macro base stations, are available to provide radio proximity access to fixed computational resources. Our objective is to find the optimal assignment of each mobile user to a cloud server through the most convenient base station and, contextually, the optimal MIMO precoding matrices and computational rates (virtual machines) to each user, under latency constraints dictated by the users Quality of Experience (QoE). The radio resources assigned to users belonging to the same cell are orthogonal to each other, whereas users of different cells might interfere against each other. The latency constraint imposes a strict relationship between the time spent for transferring the program execution from the mobile device to the fixed server (and viceversa) and the time needed to execute the computation. To properly exploit this relationship, we formulate the computation offloading problem as a joint optimization of the radio and computational resources, with the objective of minimizing the overall energy consumption, at the mobile terminal side, while meeting the latency constraints. The resulting optimization problem is nonconvex in both the objective function and in the constraints. Nevertheless, by hinging on successive convex approximation techniques, we propose an iterative algorithm able to converge to a local optimal solution of the original nonconvex problem.
2014
2014 IEEE Globecom Workshops, GC Wkshps 2014
cloud computing; computation offloading; distributed resource allocation; successive convex approximation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Distributed mobile cloud computing. joint optimization of radio and computational resources / Sardellitti, S.; Barbarossa, S.; Scutari, G.. - (2014), pp. 1505-1510. (Intervento presentato al convegno 2014 IEEE Globecom Workshops, GC Wkshps 2014 tenutosi a Austin; United States) [10.1109/GLOCOMW.2014.7063647].
File allegati a questo prodotto
File Dimensione Formato  
Sardellitti_Ditributed-mobile_2014.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 418.19 kB
Formato Adobe PDF
418.19 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1395635
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 35
  • ???jsp.display-item.citation.isi??? 29
social impact