The smartphones have evolved a lot during recent years. However, they are still limited in their battery time, computational power and storage space. Mobile Cloud Computing (MCC) has emerged as a promising solution that aims to augment smartphone's capabilities by providing a vast pool of computational power and storage space at cloud data center. In parallel to this, cooperation based computing is a recent concept in MCC that augments smartphone's capabilities by accumulating the computational resources of nearby devices to run a task. In this paper, we discuss different scenarios of computational offloading for a User Equipment (UE) and find an optimal option in terms of its energy consumption and task completion time. In particular, we compare the energy consumption and task completion time of a mobile application for local processing, offloading to a remote cloud and exploiting the cooperation based computing in the local Mobile Cloud (MC).We mark an offloading threshold for different offloading scenarios, so a UE can decide among offloading to a local MC or to a remote cloud, depending upon the size of the task it is offloading.

Remote Cloud vs Local Mobile Cloud: A Quantitative Analysis / Usman, M.; Akhtar, A.; Qaraqe, M.; Granelli, F.. - (2018), pp. 1-6. (Intervento presentato al convegno 2018 IEEE Global Communications Conference, GLOBECOM 2018 tenutosi a Abu Dhabi National Exhibition Centre (ADNEC), are nel 2018) [10.1109/GLOCOM.2018.8648069].

Remote Cloud vs Local Mobile Cloud: A Quantitative Analysis

Usman M.;Granelli F.
2018-01-01

Abstract

The smartphones have evolved a lot during recent years. However, they are still limited in their battery time, computational power and storage space. Mobile Cloud Computing (MCC) has emerged as a promising solution that aims to augment smartphone's capabilities by providing a vast pool of computational power and storage space at cloud data center. In parallel to this, cooperation based computing is a recent concept in MCC that augments smartphone's capabilities by accumulating the computational resources of nearby devices to run a task. In this paper, we discuss different scenarios of computational offloading for a User Equipment (UE) and find an optimal option in terms of its energy consumption and task completion time. In particular, we compare the energy consumption and task completion time of a mobile application for local processing, offloading to a remote cloud and exploiting the cooperation based computing in the local Mobile Cloud (MC).We mark an offloading threshold for different offloading scenarios, so a UE can decide among offloading to a local MC or to a remote cloud, depending upon the size of the task it is offloading.
2018
2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
USA
Institute of Electrical and Electronics Engineers Inc.
978-1-5386-4727-1
Usman, M.; Akhtar, A.; Qaraqe, M.; Granelli, F.
Remote Cloud vs Local Mobile Cloud: A Quantitative Analysis / Usman, M.; Akhtar, A.; Qaraqe, M.; Granelli, F.. - (2018), pp. 1-6. (Intervento presentato al convegno 2018 IEEE Global Communications Conference, GLOBECOM 2018 tenutosi a Abu Dhabi National Exhibition Centre (ADNEC), are nel 2018) [10.1109/GLOCOM.2018.8648069].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11572/320105
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact