DocumentCode :
265918
Title :
MCACC: New approach for augmenting the computing capabilities of mobile devices with Cloud Computing
Author :
Elgendy, Mohammed A. ; Shawish, Ahmed ; Moussa, M.I.
Author_Institution :
Fac. of Comput. & Inf., Comput. Sci. Dept., Benha Univ., Benha, Egypt
fYear :
2014
fDate :
27-29 Aug. 2014
Firstpage :
79
Lastpage :
86
Abstract :
Smartphones are becoming increasingly popular with a wide range of capabilities for the purpose of handling heavy applications like gaming, video editing, and face recognition etc. These kinds of applications continuously require intensive computational power, memory, and battery. Many of the early techniques solve this problem by offloading these applications to run on the Cloud due to its famous resources availability. Later, enhanced techniques choosed to offload part of the applications while leaving the rest to be processed on the smartphone based on one or two metrics like power and CPU consumption without any consideration to the communication and network overhead. With the notable development of the smartphone´s hardware, it becomes crucial to develop a smarter offloading framework that is able to efficiently utilize the available smartphone´s resources and only offload when necessary based on real-time decision metrics. This paper proposed such framework, which we called Mobile Capabilities Augmentation using Cloud Computing (MCACC). In this framework, any mobile application is divided into a group of services, and then each of them is either executed locally on the mobile or remotely on the Cloud based a novel dynamic offloading decision model. Here, the decision is based on five realtime metrics: total execution time, energy consumption, remaining battery, memory and security. The extensive simulation studies show that both heavy and light applications can benefit from our proposed model while saving energy and improving performance compare to previous techniques. The proposed MCACC turns the smartphones to be more smarter as the offloading decision is taken without any user interaction.
Keywords :
cloud computing; face recognition; smart phones; CPU consumption; MCACC; battery; cloud computing; dynamic offloading decision model; energy consumption; face recognition; gaming; intensive computational power; memory; mobile capabilities augmentation; mobile devices; network overhead; notable development; offloading framework; real-time decision metrics; realtime metrics; smart phone hardware; smart phone resources; total execution time; user interaction; video editing; Androids; Batteries; Humanoid robots; Java; Measurement; Mobile communication; Smart phones; Android; battery; mobile Cloud computing; offloading; security; smartphones;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Science and Information Conference (SAI), 2014
Conference_Location :
London
Print_ISBN :
978-0-9893-1933-1
Type :
conf
DOI :
10.1109/SAI.2014.6918175
Filename :
6918175
Link To Document :
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