Energy Efficient Resource Management in Virtualized Cloud Data Centers

Beloglazov, Anton; Buyya, Rajkumar

doi:10.1109/ccgrid.2010.46

Cited by 616 publications

(330 citation statements)

References 15 publications

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“…For instance, [28] presents two task consolidation algorithms that decide the destination node for a batch application based on resource utilizations and power consumptions. In [29], the presented solution also migrates and consolidates VMs while maintaining the QoS as high as possible, in order to minimize the energy consumption. Similar approaches could be done by considering the potential ecological efficiency as the target to maximize, instead.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Estimation and forecasting of ecological efficiency of virtual machines

Katsaros

Stichler

Subirats

et al. 2016

Future Generation Computer Systems

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The massive development of the cloud marketplace is leading to an increase in the number of the Data Centers (DCs) globally and eventually to an increase of the CO 2 related footprint. The calculation of the impact of Virtual Machines (VMs) on the environment is a challenging task, not only due to the technical difficulties but also due to the lack of information from the energy providers. The ecological efficiency of a system captures the relationship between the performance of the system with its environmental footprint. In this paper we present a methodology for the estimation and prediction of the ecological efficiency of VMs in private cloud infrastructures. We specifically focus on the information management starting from the energy resources in a region, the energy consumption and the performance of the resources and finally the calculation of ecological efficiency of a VM. To this end, we have designed and implemented a framework through which the ecological efficiency of a running VM can be assessed and the ecological efficiency of a VM to be deployed can be forecasted. The presented framework is being evaluated through several private cloud scenarios with VM deployments in hosts located in Germany.

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Section: State Of the Art And Related Workmentioning

confidence: 99%

Estimation and forecasting of ecological efficiency of virtual machines

Katsaros

Stichler

Subirats

et al. 2016

Future Generation Computer Systems

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“…There are some works that propose the use of migration for reducing energy consumption [7,11]. Other articles focus on reducing the amount of migration, which generates greater savings than using the migration without any control [9,10,12].…”

Section: Introductionmentioning

confidence: 99%

“…For this there are several techniques, such as using neural networks [1], or use live migration of virtual machines [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Green Cloud Meta-Scheduling

et al. 2015

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This article aims to evaluate the flexibility of GreenMACC (Metascheduling Green Architecture to Provide Quality of Service in Cloud Computing). The GreenMACC has a module called LRAM (Local Resource Allocation Manager) to automate the execution of all scheduling policies implemented in the architecture. This module enables the Meta-scheduler automatically adjust for each type of service requested by the user of a private cloud. Due to this function, can be ensure the most appropriate behavior to the principles of GreenIT while worrying about the quality of service. In this paper is shown the importance of the LRAM on GreenMACC. This article is also shown how to include a new policy in GreenMACC in a way that identifies the LRAM and automatically use. Through the performance evaluation of the new policy included it could be concluded that the GreenMACC is a flexible, reliable architecture and the LRAM module enables the automation of choosing the best scheduling mechanism in a private cloud.

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“…Therefore, it must not be degraded significantly [2] in order to support business processes effectively. Negative consequences of performance failures may include damaged customer relations, lost income, increased maintenance costs, delayed project schedules, and project failures [3].…”

Section: Introductionmentioning

confidence: 99%

Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2017

CSIMQ

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Abstract. Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed.

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Energy Efficient Resource Management in Virtualized Cloud Data Centers

Cited by 616 publications

References 15 publications

Estimation and forecasting of ecological efficiency of virtual machines

Estimation and forecasting of ecological efficiency of virtual machines

Green Cloud Meta-Scheduling

Capacity Management as a Service for Enterprise Standard Software

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