Watts Up? Pro AC Power Meter for Automated Energy Recording

Hirst, Jason M.; Miller, Jonathan R.; Kaplan, Brent A.; Reed, Derek D.

doi:10.1007/bf03391795

Cited by 23 publications

(12 citation statements)

References 15 publications

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“…Real time system power consumption data can be obtained by connecting it through a USB interface. It is reported that the accuracy of this hardware meter is +/-1.5%+0.3W 2 [9]. The readings provided are also considered to be generally reliable although a high error rate is observed for readings below 1 Watt [9].…”

Section: The Wattsapp Methodsmentioning

confidence: 89%

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WattsApp: Power-Aware Container Scheduling

Mehta

Harvey

Rana

et al. 2020

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)

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Containers are popular for deploying workloads. However, there are limited software-based methods (hardware-based methods are expensive) for obtaining the power consumed by containers to facilitate power-aware container scheduling. This paper presents WattsApp, a tool underpinned by a six step software-based method for poweraware container scheduling to minimize power cap violations on a server. The proposed method relies on a neural network-based power estimation model and a power capped container scheduling technique. Experimental studies are pursued in a lab-based environment on 10 benchmarks on Intel and ARM processors. The results highlight that power estimation has negligible overheads -nearly 90% of all data samples can be estimated with less than a 10% error, and the Mean Absolute Percentage Error (MAPE) is less than 6%. The power-aware scheduling of WattsApp is more effective than Intel's Running Power Average Limit (RAPL) based power capping as it does not degrade the performance of all running containers.

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Section: The Wattsapp Methodsmentioning

confidence: 89%

“…It is reported that the accuracy of this hardware meter is +/-1.5%+0.3W 2 [9]. The readings provided are also considered to be generally reliable although a high error rate is observed for readings below 1 Watt [9]. A series of power meters from Watts Up are used in research reported in the literature [15]…”

Section: The Wattsapp Methodsmentioning

confidence: 99%

WattsApp: Power-Aware Container Scheduling

Mehta

Harvey

Rana

et al. 2020

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)

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“…Apart from evaluating the time required by our modules to return a solution, we also planned to measure their energy consumption associated using hardware energy meters [18,20]. Nonetheless, it has not been possible as evaluation was done during the teleworking restrictions imposed by the COVID-19 global pandemic.…”

Section: G2mentioning

confidence: 99%

Supporting the evolution of applications deployed on edge-based infrastructures using multi-layer feature models

Cañete

Amor

Fuentes

2020

Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B

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The proliferation of cyber-physical systems has encouraged the emergence of new technologies and paradigms to improve the performance of IoT-based applications. Edge Computing proposes using the nearby devices in the frontier/Edge of the access network for deploying application tasks. However, the functionality of cyberphysical systems, which is usually distributed in several devices and computers, imposes specific requirements on the infrastructure to run properly. The evolution of an application to meet new user requirements and the high diversity of hardware and software technologies in the edge can complicate the deployment of evolved applications. The aim of our approach is to apply Multi Layer Feature Models, which capture the variability of applications and the infrastructure, to support the deployment in edge-based environments of cyber-physical systems applications. This separation can support the evolution of application and infrastructure. Considering that IoT/Edge/Cloud infrastructures are usually shared by many applications, the SPL deployment process has to assure that there will be enough resources for all of them, informing developers about the alternatives of deployment. Prior to its deployment and leaning on the infrastructure feature models, the developer can calculate what is the configuration of minimal set of devices supporting application requirements of the evolved application. In addition, the developer can find which is the application configuration that can be hosted in the current evolved infrastructure. CCS CONCEPTS • Software and its engineering → Software product lines; • Computer systems organization → Distributed architectures; Embedded and cyber-physical systems.

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“…PRO meters were chosen based on a previous paper that validated the device's reliability. 11 Because data were collected using an automated device with established reliability, the data were not at risk for reliability concerns. Procedural fidelity data were collected on the power management settings on the lab computers.…”

Section: Research and Solutionsmentioning

confidence: 99%

“…The utility and reliability of these meters was confirmed in a previous study. 11 The meters were capable of recording data at preset intervals and storing the data using internal memory. The data were retrievable using a cable and program supplied by the manufacturer.…”

Section: Apparatus and Dependent Measuresmentioning

confidence: 99%