Lightweight Power Monitoring Framework for Virtualized Computing Environments

Phung, James; Lee, Young Choon; Zomaya, Albert Y.

doi:10.1109/tc.2019.2936018

Cited by 12 publications

(34 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3.1.6 P6: Impact of temperature and/or frequency on models that predict VEs' power consumption [73,93,94]. This category regards the challenge of inclusion of processor package temperature in models of power consumption.…”

Section: P5: Dependency Of Ve's Power Consumption and Model On Ve's Resource Configuration (Heterogeneity)mentioning

confidence: 99%

“…PARSEC is a benchmark which incorporates workloads suited to several different computer classes (e.g. servers, workstations) [93,94,182]. NBP-MP is derived from NASA real fluid computational applications.…”

Section: Workload Generators/benchmarksmentioning

confidence: 99%

“…Works in this category investigate low-power-idle (LPI) and adaptive rate (AR) operation of a processor as a means of reducing power consumption. The challenge is broad enough to permit a sub-categorization into (a) those works that investigate the influence of frequency as a power-model parameter [93,94] and (b) other works that address improved, real-time governance of LPI and/or AR [64,95,105] to minimize the power consumed to process a load.…”

Section: P7: Loading the Ve's Resources And Measuring Resource Usementioning

confidence: 99%

See 2 more Smart Citations

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

Depasquale

Salam

Davoli

2021

Preprint

View full text Add to dashboard Cite

This article surveys the literature, over the period 2010-2020, on measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. Hardware power meters are incapable of measuring power consumption of individual virtual entities co-hosted on a physical machine. Thus, software power meters are inevitable, yet their development is difficult. Indeed, there is no direct approach to measurement and, therefore, modeling through proxies of power consumption must be used. In this survey, we present trends, fallacies and pitfalls. Notably, we identify limitations of the widely-used linear models and the progression towards Artificial Intelligence / Machine Learning techniques as a means of dealing with the seven major dimensions of variability: workload type; computer virtualization agents; system architecture and resources; concurrent, co-hosted virtualized entities; approaches towards attribution of power consumption to virtual entities; frequency; and temperature.

show abstract

Section: P5: Dependency Of Ve's Power Consumption and Model On Ve's Resource Configuration (Heterogeneity)mentioning

confidence: 99%

Section: Workload Generators/benchmarksmentioning

confidence: 99%

Section: P7: Loading the Ve's Resources And Measuring Resource Usementioning

confidence: 99%

See 1 more Smart Citation

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

Depasquale

Salam

Davoli

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Lightweight power models, such as cWatts+ [17] and cWatts++ [18] are developed for containers. cWatts++ is a virtual power model that has two components: a client back-end and a server front-end.…”

Section: Related Workmentioning

confidence: 99%

“…It is valuable to gather the power consumption of individual containers running in a system to schedule them in a power-aware manner. However, software-based methods, such as cWatts [17], cWatts++ [18] and SmartWatts [7] are either CPU architecture specific, do not capture all system components that contribute to container power, and are intrusive methods. This paper develops WattsApp underpinned by a six step software-based (hardwarebased are expensive and require hardware level changes), architecture agnostic and a non-intrusive power-aware container scheduling method.…”

mentioning

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)

View full text Add to dashboard Cite

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.

show abstract

Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

2022

View full text Add to dashboard Cite

In this article, we address a critical concern of the growth of computer servers number and the resulting power expenditures in data centers by analyzing the statistical metrics related to the workload executed in each physical node. The aim is to build a stochastic model for power consumption estimation based on historical data. Relying on in-depth investigation and experimental testing of the power consumption features and performance of the various workload datasets, we propose a model that considers the workload and the power consumed to be executed by a server as random variables. Based on the properties of the probabilistic distribution function of each random variable, we establish the correlation relationship between the workload and the power consumption using a non-parametric approach. Our use of a non-parametric method to learn a given probability model is challenging because it requires estimating the full distribution from the available data samples. The accuracy of our approach is demonstrated by estimating the energy consumption of various workloads. The experimental and simulation results show that our model outperforms many existing approaches in terms of accuracy, and it can be applied to a wide variety of workloads.

show abstract

Lightweight Power Monitoring Framework for Virtualized Computing Environments

Cited by 12 publications

References 29 publications

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

WattsApp: Power-Aware Container Scheduling

Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

Contact Info

Product

Resources

About