Energy-efficient and multifaceted resource management for profit-driven virtualized data centers

Goiri, Íñigo; Berral, Josep Ll.; Fitó, J. Oriol; Julií, Ferran; Nou, Ramón; Guitart, Jordi; Gavaldí, Ricard; Torres, Jordi

doi:10.1016/j.future.2011.12.002

Cited by 73 publications

(34 citation statements)

References 26 publications

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“…Multi-objective optimization problems can be solved via scalarization, that is, converting the original problem with multiple objectives into a singleobjective optimization problem, for instance, by considering their impact on the data center profit [34]. The problem can be also solved as a single-objective problem when all but one of the objectives have a target value, as a constraint can be placed on those objectives.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Toward sustainable data centers: a comprehensive energy management strategy

Guitart

2016

Computing

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Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers. In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Toward sustainable data centers: a comprehensive energy management strategy

Guitart

2016

Computing

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“…In addition, a decentralized resource management middleware that considers multi-optimization is designed. In [32], authors proposed an innovative methodology to manage virtualized data centers according to multiple facets (energy efficiency, virtualization overheads, and SLA violation penalties) when placing VMs in data center nodes and maximizing the provider's profit. The experiments demonstrated that the proposed model is capable of increasing the provider's revenue by 30% and can handle certain issues including resource heterogeneity.…”

Section: Java Based Simulatormentioning

confidence: 99%

Resource Scheduling in Cloud Environmet: A Survey

Mangla¹,

Singh²,

Rana³

2016

Adv. Sci. Technol. Res. J.

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Cloud Computing offers the avant-garde services at a stretch that are too attractive for any cloud user to ignore. With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under different administrative domains. This perspective brings out certain vital factors for efficient scheduling of resources providing a wide genre of characteristics, diversity in context of level of service agreements and that too with user-contingent elasticity. In this paper, a comprehensive survey of research related to various aspects of cloud resource scheduling is provided. A comparative analysis of various resource scheduling techniques focusing on key performance parameters like Energy efficiency, Virtual Machine allocation and migration, Cost-effectiveness and Service-Level Agreement is also presented.

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“…Similar approaches could be done by considering the potential ecological efficiency as the target to maximize, instead. The policy in [30] maximizes the provider's benefit by considering multiple facets to decide the placement of VMs: energy efficiency, virtualization overheads and SLA violation penalties. It also supports the outsourcing of VMs to external providers.…”

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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Energy-efficient and multifaceted resource management for profit-driven virtualized data centers

Cited by 73 publications

References 26 publications

Toward sustainable data centers: a comprehensive energy management strategy

Toward sustainable data centers: a comprehensive energy management strategy

Resource Scheduling in Cloud Environmet: A Survey

Estimation and forecasting of ecological efficiency of virtual machines

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