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Ubiquitous green computing techniques for high demand applications in Smart environments.

Zapater M, Sanchez C, Ayala JL, Moya JM, Risco-Martín JL - Sensors (Basel) (2012)

Bottom Line: Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users.This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure.These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

View Article: PubMed Central - PubMed

Affiliation: CEI Campus Moncloa, UCM-UPM, Madrid 28040, Spain. marina@die.upm.es

ABSTRACT
Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

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Energy profiling of the tasks in the Intel processor.
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f7-sensors-12-10659: Energy profiling of the tasks in the Intel processor.

Mentions: Profiling of the tasks of the first job set in the IntelXeon machine: as explained in Section 3.2, we use the first job set of the workload to profile the tasks in the HPC facility. The profiling step gathers information about the following features for each task: average CPU usage, memory used, time needed to complete execution and energy. On Figure 7 the results for the energy profiling of the tasks are shown. The Y-axis represents the energy variation (in kWh) when allocating a certain task in a certain processor—that is, the values of etp for p being an Intel processor. This Figure lets us deduce intuitively the three different types of tasks: the low-demand tasks consume very little energy, the medium-demand tasks consume a little more, while there are other tasks that comparatively consume a lot of energy. However, making this assumption only with the energy results and without paying attention to other characteristics such as the CPU-usage would be a naive approximation. Therefore, in the next step a clustering that takes into account all the features is performed.


Ubiquitous green computing techniques for high demand applications in Smart environments.

Zapater M, Sanchez C, Ayala JL, Moya JM, Risco-Martín JL - Sensors (Basel) (2012)

Energy profiling of the tasks in the Intel processor.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472849&req=5

f7-sensors-12-10659: Energy profiling of the tasks in the Intel processor.
Mentions: Profiling of the tasks of the first job set in the IntelXeon machine: as explained in Section 3.2, we use the first job set of the workload to profile the tasks in the HPC facility. The profiling step gathers information about the following features for each task: average CPU usage, memory used, time needed to complete execution and energy. On Figure 7 the results for the energy profiling of the tasks are shown. The Y-axis represents the energy variation (in kWh) when allocating a certain task in a certain processor—that is, the values of etp for p being an Intel processor. This Figure lets us deduce intuitively the three different types of tasks: the low-demand tasks consume very little energy, the medium-demand tasks consume a little more, while there are other tasks that comparatively consume a lot of energy. However, making this assumption only with the energy results and without paying attention to other characteristics such as the CPU-usage would be a naive approximation. Therefore, in the next step a clustering that takes into account all the features is performed.

Bottom Line: Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users.This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure.These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

View Article: PubMed Central - PubMed

Affiliation: CEI Campus Moncloa, UCM-UPM, Madrid 28040, Spain. marina@die.upm.es

ABSTRACT
Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

Show MeSH