How does docker affect energy consumption? Evaluating workloads in and out of Docker containers

Santos, Eddie Antonio; McLean, Carson; Solinas, Christopher; Hindle, Abram

doi:10.1016/j.jss.2018.07.077

Cited by 21 publications

(12 citation statements)

References 12 publications

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“…Figure 3 reports that this is not the case, as the energy consumption variation does not get noticeably affected by Docker while running a same compiled version of the benchmarks at 5 %, 50 % and 100 % workloads. In fact, while Docker increases the energy consumption due to the extra layer it implements [9], it does not noticeably affect the energy variation. The standard deviation (STD) is even slightly smaller (ST D Docker = 192mJ ,ST D Binar y = 207mJ ), taking into account the measurements errors and the OS activity.…”

Section: Measurement Tools and Methodologymentioning

confidence: 96%

Taming Energy Consumption Variations In Systems Benchmarking

Ournani

Belgaid

Rouvoy

et al. 2020

Proceedings of the ACM/SPEC International Conference on Performance Engineering

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The past decade witnessed the inclusion of power measurements to evaluate the energy efficiency of software systems, thus making energy a prime indicator along with performance. Nevertheless, measuring the energy consumption of a software system remains a tedious task for practitioners. In particular, the energy measurement process may be subject to a lot of variations that hinder the relevance of potential comparisons. While the state of the art mostly acknowledged the impact of hardware factors (chip printing process, CPU temperature), this paper investigates the impact of controllable factors on these variations. More specifically, we conduct an empirical study of multiple controllable parameters that one can easily tune to tame the energy consumption variations when benchmarking software systems. To better understand the causes of such variations, we ran more than a 1, 000 experiments on more than 100 nodes with different workloads and configurations. The main factors we studied encompass: experimental protocol, CPU features (C-states, Turbo Boost, core pinning) and generations, as well as the operating system. Our experiments showed that, for some workloads, it is possible to tighten the energy variation by up to 30×. Finally, we summarize our results as guidelines to tame energy consumption variations. We argue that the guidelines we deliver are the minimal requirements to be considered prior to any energy efficiency evaluation. CCS CONCEPTS • Hardware → Power and energy; Power estimation and optimization; Platform power issue; Enterprise level and data centers power issues.

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Section: Measurement Tools and Methodologymentioning

confidence: 96%

Taming Energy Consumption Variations In Systems Benchmarking

Ournani

Belgaid

Rouvoy

et al. 2020

Proceedings of the ACM/SPEC International Conference on Performance Engineering

View full text Add to dashboard Cite

show abstract

“…In some papers, such as [15,16], the topics are only briefly raised. Most of the papers use other approaches to deal with the subject matter.…”

Section: Experiments Backgrounds Prospects and Related Workmentioning

confidence: 99%

“…Most of the papers use other approaches to deal with the subject matter. For example, in [16], entire systems are compared natively and inside Docker containers, whereas we are more concerned with single containers connected to a network, without referring to overall systems, but taking scalability into account. Other papers are concerned with solving well-known problems; for example, the creation of a container broker system for monitoring containers and their consumption [17] or the consolidation of VMs if containers can be moved to other systems [18].…”

Section: Experiments Backgrounds Prospects and Related Workmentioning

confidence: 99%

An Analysis of the Energy Consumption Behavior of Scaled, Containerized Web Apps

2018

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Containerization is one of the most important topics for modern data centers and web developers. Since the number of containers on one-and multi-node systems is growing, knowledge about the energy consumption behavior of single web-service containers is essential in order to save energy and, of course, money. In this article, we are going to show how the energy consumption behavior of single containerized web services/web apps changes while creating replicas of the service in order to scale and balance the web service.

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“…Barresi et al [38] introduced MicroCloud, which is a container-based solution for managing cloud resource efficiently. Santos et al [39] evalu- ated the energy consumption of different applications executed in Docker and bare metal. However, the energy efficient scheduling is not considered in these container-based work.…”

Section: Related Workmentioning

confidence: 99%

BrownoutCon: A software system based on brownout and containers for energy-efficient cloud computing

Buyya

2019

Journal of Systems and Software

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VM consolidation and Dynamic Voltage Frequency Scaling approaches have been proved to be efficient to reduce energy consumption in cloud data centers. However, the existing approaches cannot function efficiently when the whole data center is overloaded. An approach called brownout has been proposed to solve the limitation, which dynamically deactivates or activates optional microservices or containers. In this paper, we propose a brownoutbased software system for container-based clouds to handle overloads and reduce power consumption. We present its design and implementation based on Docker Swarm containers. The proposed system is integrated with existing Docker Swarm without the modification of their configurations. To demonstrate the potential of BrownoutCon software in offering energy-efficient services in brownout situation, we implemented several policies to manage containers and conducted experiments on French Grid'5000 cloud infrastructure. The results show the currently implemented policies in our software system can save about 10% to 40% energy than the existing baselines while ensuring quality of services.

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How does docker affect energy consumption? Evaluating workloads in and out of Docker containers

Cited by 21 publications

References 12 publications

Taming Energy Consumption Variations In Systems Benchmarking

Taming Energy Consumption Variations In Systems Benchmarking

An Analysis of the Energy Consumption Behavior of Scaled, Containerized Web Apps

BrownoutCon: A software system based on brownout and containers for energy-efficient cloud computing

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