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