Remote cloud vs local mobile cloud: a quantitative analysis

Muhammad Usman; Ameera Akhtar; Marwa Qaraqe; Fabrizio Granelli

doi:10.1109/GLOCOM.2018.8648069

Remote cloud vs local mobile cloud: a quantitative analysis

Muhammad Usman, Ameera Akhtar, Marwa Qaraqe, Fabrizio Granelli

Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	2018 IEEE Global Communications Conference (GLOBECOM)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9781538647271
ISBN (Print)	9781538647288
DOIs	https://doi.org/10.1109/GLOCOM.2018.8648069
Publication status	Published - 21 Feb 2019
Event	2018 IEEE Global Communications Conference - Abu Dhabi, United Arab Emirates Duration: 9 Dec 2018 → 13 Dec 2018 https://globecom2018.ieee-globecom.org/index.html (Link to conference website)

Publication series

Name
ISSN (Print)	1930-529X
ISSN (Electronic)	2576-6813

Conference

Conference	2018 IEEE Global Communications Conference
Abbreviated title	IEEE GLOBECOM 2018
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	9/12/18 → 13/12/18
Internet address	https://globecom2018.ieee-globecom.org/index.html (Link to conference website)

Keywords

mobile cloud computing
mobile clouds
D2D communication
5G networks
computational offloading
cloud services
energy efficiency
spectrum efficiency
cooperative networks
cooperation based computing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1109/GLOCOM.2018.8648069

Cite this

@inproceedings{573ebc8348d547c6a60513cb807ab4ed,

title = "Remote cloud vs local mobile cloud: a quantitative analysis",

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.",

keywords = "mobile cloud computing, mobile clouds, D2D communication, 5G networks, computational offloading, cloud services, energy efficiency, spectrum efficiency, cooperative networks, cooperation based computing",

author = "Muhammad Usman and Ameera Akhtar and Marwa Qaraqe and Fabrizio Granelli",

year = "2019",

month = feb,

day = "21",

doi = "10.1109/GLOCOM.2018.8648069",

language = "English",

isbn = "9781538647288",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE Global Communications Conference (GLOBECOM)",

address = "United States",

note = " 2018 IEEE Global Communications Conference, IEEE GLOBECOM 2018 ; Conference date: 09-12-2018 Through 13-12-2018",

url = "https://globecom2018.ieee-globecom.org/index.html",

}

TY - GEN

T1 - Remote cloud vs local mobile cloud: a quantitative analysis

AU - Usman, Muhammad

AU - Akhtar, Ameera

AU - Qaraqe, Marwa

AU - Granelli, Fabrizio

PY - 2019/2/21

Y1 - 2019/2/21

N2 - 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.

AB - 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.

KW - mobile cloud computing

KW - mobile clouds

KW - D2D communication

KW - 5G networks

KW - computational offloading

KW - cloud services

KW - energy efficiency

KW - spectrum efficiency

KW - cooperative networks

KW - cooperation based computing

U2 - 10.1109/GLOCOM.2018.8648069

DO - 10.1109/GLOCOM.2018.8648069

M3 - Conference contribution

SN - 9781538647288

BT - 2018 IEEE Global Communications Conference (GLOBECOM)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Global Communications Conference

Y2 - 9 December 2018 through 13 December 2018

ER -

Remote cloud vs local mobile cloud: a quantitative analysis

Abstract

Publication series

Conference

Keywords

UN SDGs

Documents and Links

Fingerprint

Cite this