Energy consumption estimation of virtual machines

Waßmann, Ingolf; Versick, Daniel; Tavangarian, Djamshid

doi:10.1145/2480362.2480579

Cited by 12 publications

(44 citation statements)

References 8 publications

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“…The literature usually assumes power consumption as linear function of load [7]. In [14] we show that a linear model is not sufficient in current energy-efficient server architectures as the implemented energy-reduction mechanisms often result in a non-linear dependency of energy consumption by load. Exemplarily we showed that for a 6 core AMD Phenom II X6 1090T and an 8 core Intel Xeon CPU E5620 [14].…”

Section: ) Consumption Estimationmentioning

confidence: 95%

“…In [14] we show that a linear model is not sufficient in current energy-efficient server architectures as the implemented energy-reduction mechanisms often result in a non-linear dependency of energy consumption by load. Exemplarily we showed that for a 6 core AMD Phenom II X6 1090T and an 8 core Intel Xeon CPU E5620 [14]. Thus, our energy model approximates the energy consumption by polynomial regression (method of least squares) with varying degrees of the polynomial function.…”

Section: ) Consumption Estimationmentioning

confidence: 97%

“…In fact we acquired measurements which amongst others show the influence of colocated VMs in a Xen hypervisor system [15]. Further details about our power estimation algorithm can be found in [14].…”

Section: ) Consumption Estimationmentioning

confidence: 99%

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The CÆSARA architecture for power and thermal-aware placement of virtual machines

Versick

Tavangarian

2013

2013 International Green Computing Conference Proceedings

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Energy consumption of data centers increased continously during the last decades. As current techniques for improving their energy efficiency are usually limited to one type of data center components, it is difficult to employ them for a holistic optimization which may utilize synergies between all components. This paper shortly presents architecture and working principles of the novel energy management system CAESARA that applies a comprehensive view for evaluating an energy-efficient virtual machine placement in current virtualization environments for minimizing the number of running energy-consuming physical machines. In contrast to existing solutions, our placement algorithm does not only aggregate load to a minimum number of servers -it also discovers which servers in conjunction with their infrastructure act as most energy-efficient migration targets. Thus, the CAESARA architecture unifies two worlds which are usually separated when finding an energy-efficient configuration: the inner computer world which can be characterized by load values and the infrastructure world which is characterized by physical parameters. This paper presents the architecture of CAESARA, defines properties for an energy-efficient virtual machine placement algorithm, and sketches how the integration of infrastructural energy consumption can be employed by using a thermodynamic approach. Exemplarily, this is realized by the example of server cooling equipment.

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Section: ) Consumption Estimationmentioning

confidence: 95%

Section: ) Consumption Estimationmentioning

confidence: 97%

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The CÆSARA architecture for power and thermal-aware placement of virtual machines

Versick

Tavangarian

2013

2013 International Green Computing Conference Proceedings

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“…We use a power consumption model based on the parameters introduced in [16] and [24] to model the consumption of stand-alone BBUs placed with a RRH on a Distributed RAN (DRAN) node, cloud and fog nodes that consolidates vBBU processing, vBBUs instantiated on processing nodes and LC used to terminate traffic from a VPON. The power consumed to maintain a stand alone BBU at the RRH is equal to 600 W, and this consumption is related to the BBU itself.…”

Section: Power Consumption Modelmentioning

confidence: 99%

Low-Latency and Energy-Efficient BBU Placement and VPON Formation in Virtualized Cloud-Fog RAN

Tinini

Batista

Figueiredo

et al. 2019

J. Opt. Commun. Netw.

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Cloud Radio Access Networks (CRAN) allow to reduce power consumption in future 5G networks by decoupling BaseBand Units (BBU) from cell sites and centralizing the baseband processing from Remote Radio-Heads (RRH) in BBUs pools in a cloud. Although this centralization can enable power savings, it imposes much higher traffic on the optical transport network used to connect RRHs to the BBU pool, i.e., the fronthaul. In this paper we propose a hybrid Cloud-Fog RAN (CF-RAN) architecture that resorts to fog computing and to Network Functions Virtualization (NFV) to replicate the processing capacity of CRAN in local fog nodes closer to the RRHs that can be activated on demand to process surplus fronthaul/cloud traffic. We devise an ILP formulation and graph-based heuristics to decide when to activate fog nodes and how to dimension wavelengths on a Timeand-Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) to support the fronthaul. Our results show that our architecture can consume up to 96% less energy than a traditional Distributed RAN (DRAN), providing a maximum transmission latency of about 20µs between RRHs and BBUs even in large traffic scenarios. Moreover, we demonstrate that our graph-based heuristics can achieve same optimal solutions of the ILP formulation but with a reduction of 99.86% in the execution time.

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“…Krishnan in [30] used not only CPU utilization, but also memory consumption. Versick in [35] proposes a polynomial model. In this model network interface card (NIC) power consumption and hard disk power consumption is measured.…”

Section: Vm Power Consumptionmentioning

confidence: 99%

Security Supportive Energy Aware Scheduling And Scaling For Cloud Environments

Jakóbik¹,

Grzonka²,

Kołodziej³

2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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Energy consumption is one of the most important problems in the era of Computational Clouds (CC). CC infrastructures must be elastic and scalable for being accessible by huge population of users in different geographical locations. It means also that CC energy utilization systems must be modern and dynamic in order to reduce the cost of using the cloud services and resources.In this paper, we develop the novel energy saving strategies for resource allocation and task scheduling in computational clouds. We present the new energyaware scheduling policies and methods of scaling the virtual resources. The idea of the proposed models is based on Dynamic Voltage and Frequency Scaling (DVFS) techniques of modulation of the power of microprocessors. Additionally, the proposed model enables the monitoring of the energy consumption, which is necessary for providing the scheduling under the security criterion. The efficiency of the proposed models has been justified in the simple empirical analysis. The obtained results show the need to maintain a balance between energy consumption and task schedule execution.

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Energy consumption estimation of virtual machines

Cited by 12 publications

References 8 publications

The CÆSARA architecture for power and thermal-aware placement of virtual machines

The CÆSARA architecture for power and thermal-aware placement of virtual machines

Low-Latency and Energy-Efficient BBU Placement and VPON Formation in Virtualized Cloud-Fog RAN

Security Supportive Energy Aware Scheduling And Scaling For Cloud Environments

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Energy consumption estimation of virtual machines

Cited by 12 publications

References 8 publications

The C&#x00C6;SARA architecture for power and thermal-aware placement of virtual machines

The C&#x00C6;SARA architecture for power and thermal-aware placement of virtual machines

Low-Latency and Energy-Efficient BBU Placement and VPON Formation in Virtualized Cloud-Fog RAN

Security Supportive Energy Aware Scheduling And Scaling For Cloud Environments

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The CÆSARA architecture for power and thermal-aware placement of virtual machines

The CÆSARA architecture for power and thermal-aware placement of virtual machines