TY - GEN
T1 - MECCA offloading cloud model over wireless interfaces for optimal power reduction and processing time
AU - Aldmour, Rakan
AU - Yousef, Sufian
AU - Yaghi, Mohammad
AU - Kapogiannis, Georgios
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - In this paper, the power consumption and the processing time of smartphones are estimated locally and then compared with the power consumption and processing time when the smartphone executes heavy tasks by offloading on WLAN, 3G, and 4G mobile systems. Different scenarios were tested for different file sizes and wireless network interfaces. The main parameter of the quality of service is the time needed to process the file on the cloud versus the time needed to execute the file locally on the smartphone, as tested by the MECCA (Mobile Energy Cloud Computing algorithm) model. The optimal saving in energy consumption of the smartphone has reached around 90% over the 4G system, while maintaining an approximately similar range of time consumption for similar file sizes. The most important issue is to save time while serving the file. However, it is important, especially for the small nodes, to decrease the power consumption during serving big files, which is normally very high. The cost of the power consumption on smartphone, processing time, and file size for the core cloud and local node, are calculated to extract an immediate input to the processing decision. The Wi-Fi results showed very short processing times comparatively but resulted in very high energy consumption.
AB - In this paper, the power consumption and the processing time of smartphones are estimated locally and then compared with the power consumption and processing time when the smartphone executes heavy tasks by offloading on WLAN, 3G, and 4G mobile systems. Different scenarios were tested for different file sizes and wireless network interfaces. The main parameter of the quality of service is the time needed to process the file on the cloud versus the time needed to execute the file locally on the smartphone, as tested by the MECCA (Mobile Energy Cloud Computing algorithm) model. The optimal saving in energy consumption of the smartphone has reached around 90% over the 4G system, while maintaining an approximately similar range of time consumption for similar file sizes. The most important issue is to save time while serving the file. However, it is important, especially for the small nodes, to decrease the power consumption during serving big files, which is normally very high. The cost of the power consumption on smartphone, processing time, and file size for the core cloud and local node, are calculated to extract an immediate input to the processing decision. The Wi-Fi results showed very short processing times comparatively but resulted in very high energy consumption.
KW - Cloud Computing
KW - Energy Consumption
KW - Offloading
KW - Processing time
KW - Smartphones
UR - http://www.scopus.com/inward/record.url?scp=85050201745&partnerID=8YFLogxK
U2 - 10.1109/UIC-ATC.2017.8397639
DO - 10.1109/UIC-ATC.2017.8397639
M3 - Conference contribution
AN - SCOPUS:85050201745
T3 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
SP - 1
EP - 8
BT - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017
Y2 - 4 April 2017 through 8 April 2017
ER -
