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Fig. 1. Computing device sales comparisons. Data are from [1]. One method of reducing the computational burden of mobile devices is to set up an agent between the mobile devices and cloud computing resources to provide mobile devices access We propose an energy efficient cloud computing system that provides computational resources from distributed mobile de- vices to the users. The idea of applying distributed computing and cloud computing to mobile devices has been developed in recent years. However, limited by the traditionally low processing speed and small storage space, the focus of much of this research has been on reducing the computational burden of mobile devices.

Figure 1 Computing device sales comparisons. Data are from [1]. One method of reducing the computational burden of mobile devices is to set up an agent between the mobile devices and cloud computing resources to provide mobile devices access We propose an energy efficient cloud computing system that provides computational resources from distributed mobile de- vices to the users. The idea of applying distributed computing and cloud computing to mobile devices has been developed in recent years. However, limited by the traditionally low processing speed and small storage space, the focus of much of this research has been on reducing the computational burden of mobile devices.

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Fig. 2. The mobile computing system architecture.
Fig. 2. The mobile computing system architecture.
Fig. 3. The server-client protocol flow chart.
Fig. 3. The server-client protocol flow chart.
‘ig. 4. The prototype screen shot from an Android phone.
‘ig. 4. The prototype screen shot from an Android phone.
Fig. 6. Comparison of energy consumption.
Fig. 6. Comparison of energy consumption.
Fig. 5. Comparison of computing time.
Fig. 5. Comparison of computing time.
PERFORMANCE RESULTS OF THE E ASUS NEXUS 7 (1 CPU, 4 CorREs) TABLE V PERFORMANCE RESULTS OF THE WORKSTATION | (2 CPUS, 2 CORES)
PERFORMANCE RESULTS OF THE E ASUS NEXUS 7 (1 CPU, 4 CorREs) TABLE V PERFORMANCE RESULTS OF THE WORKSTATION | (2 CPUS, 2 CORES)
test, a WS3 takes 250.5s to complete the job and consumes 164, 906 in total. If we use the computing resources of 160 GS3, each processing 2 tasks, the total amount of time required to complete the job is 217.15 while the energy consumed is only 74,992J, 45.5% of that consumed when using WS3.
test, a WS3 takes 250.5s to complete the job and consumes 164, 906 in total. If we use the computing resources of 160 GS3, each processing 2 tasks, the total amount of time required to complete the job is 217.15 while the energy consumed is only 74,992J, 45.5% of that consumed when using WS3.
PERFORMANCE RESULTS OF THE WORKSTATION 3 (4 CPUS, 64 CORES)
PERFORMANCE RESULTS OF THE WORKSTATION 3 (4 CPUS, 64 CORES)
PERFORMANCE RESULTS OF THE WORKSTATION 2 (2 CPUS, 8 CORES)
PERFORMANCE RESULTS OF THE WORKSTATION 2 (2 CPUS, 8 CORES)
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Distributed ComputingVolunteer Computing
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