Application-Agnostic Power Monitoring in Virtualized Environments

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

doi:10.1109/ccgrid.2017.100

Cited by 8 publications

(39 citation statements)

References 18 publications

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“…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%

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

Depasquale

Salam

Davoli

2021

Preprint

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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.

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

confidence: 99%

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

Depasquale

Salam

Davoli

2021

Preprint

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“…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)

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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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“…In contrast, coarse-grained performance profiling aims to characterize system-wide performance dynamics, such as the macro stream framework [19]. Moreover, a power meter for virtualized environments was presented in [20]. CPU event counters and the Performance Programming Interface Library were used to estimate the power usage on a per-thread basis.…”

Section: Related Workmentioning

confidence: 99%

Characterizing Power and Energy Efficiency of Legion Data-Centric Runtime and Applications on Heterogeneous High-Performance Computing Systems

Huang¹,

Fu²,

Pakin³

et al. 2019

High Performance Parallel Computing

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The traditional parallel programming models require programmers to explicitly specify parallelism and data movement of the underlying parallel mechanisms. Different from the traditional computation-centric programming, Legion provides a data-centric programming model for extracting parallelism and data movement. In this chapter, we aim to characterize the power and energy consumption of running HPC applications on Legion. We run benchmark applications on compute nodes equipped with both CPU and GPU, and measure the execution time, power consumption and CPU/GPU utilization. Additionally, we test the message passing interface (MPI) version of these applications and compare the performance and power consumption of high-performance computing (HPC) applications using the computation-centric and data-centric programming models. Experimental results indicate Legion applications outperforms MPI applications on both performance and energy efficiency, i.e., Legion applications can be 9.17 times as fast as MPI applications and use only 9.2% energy. Legion effectively explores the heterogeneous architecture and runs applications tasks on GPU. As far as we know, this is the first study to understand the power and energy consumption of Legion programming and runtime infrastructure. Our findings will enable HPC system designers and operators to develop and tune the performance of data-centric HPC applications with constraints on power and energy consumption.

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Application-Agnostic Power Monitoring in Virtualized Environments

Cited by 8 publications

References 18 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

Characterizing Power and Energy Efficiency of Legion Data-Centric Runtime and Applications on Heterogeneous High-Performance Computing Systems

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