Impact of Workload and Renewable Prediction on the Value of Geographical Workload Management

Abbasi, Zahra; Pore, Madhurima; Gupta, Sandeep

doi:10.1007/978-3-642-55149-9_1

Cited by 12 publications

(12 citation statements)

References 15 publications

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“…In particular, the prediction error has a very harmful impact on the peak power cost as observed by the related work [13]. The reason is that the optimal approach is to utilize the data centers with low electricity cost as much as possible without increasing their peak power in that time frame T .…”

Section: (B) This Means That Optimizing the Energy Cost Does Notmentioning

confidence: 99%

Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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Energy consumption of the data centers worldwide is rapidly growing fueled by everincreasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. In order to achieve these objectives, this dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level.This dissertation devises optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and the cooling power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.

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Section: (B) This Means That Optimizing the Energy Cost Does Notmentioning

confidence: 99%

Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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“…GLB also allows exploiting data centers powered by renewable sources to reduce carbon emissions, as we will discuss in Section 6. In addition, GLB can be integrated with energy buffering management in order to shave peak power draw from the grid [10].…”

Section: Data Center Ecosystemsmentioning

confidence: 99%

“…Whereas data center ecosystems offer new opportunities for energy-driven management, they also encompass new challenges that must be considered [44,56,10], such as the distant geographic locations of data centers, which have an impact in the migration of VMs (increasing the cost significantly (and the consumed energy) and frequently causing service level degradations for the affected customers) and in the data exchange among VMs located in different data centers (increasing the communication latency among them); the independent administrative domains involved in the ecosystem, which have frequently conflicting goals and do not generally disclose information about their energy consumption and energy mix, thus increasing the need for third parties to independently assess energy data of data centers and share this information within the ecosystem; and the importance of the prediction accuracy of the input data (e.g. workload, energy price, renewable energy), which depends on the predictability of each data source and the prediction window length, and can be a downgrading factor on the efficiency of the management algorithms.…”

Section: Data Center Ecosystemsmentioning

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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“…An example is discussed in Ref. 63 proposing to use Geographical Load Balancing (GLB) to shift workloads and avoid peak power demands. The incoming workload and peak demand need to be predicted and this is achieved using the SARIMA prediction method and the RHC technique.…”

Section: Cloud Computing and Networkmentioning

confidence: 99%

A Survey on Energy Efficiency in Information Systems

Vitali

Pernici

2014

Int. J. Coop. Info. Syst.

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Received (Day Month Year) Revised (Day Month Year)Concerns about energy and sustainability are growing everyday involving a wide range of fields. Even Information Systems are being influenced by the issue of reducing pollution and energy consumption and new fields are rising dealing with this topic. One of these fields is Green IT, which deals with energy efficiency with a focus on IT. Researchers have faced this problem according to several points of view. The purpose of this paper is to understand the trends and the future development of Green IT by analyzing the state of the art and classifying existing approaches to understand which are the components that have an impact on energy efficiency in Information Systems and how this impact can be reduced. At first, we explore some guidelines that can help to understand the efficiency level of an organization and of an Information System. Then we discuss measurement and estimation of energy efficiency and identify which are the components that mainly contribute to energy waste and how it is possible to improve energy efficiency, both at the hardware and at the software level.

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Impact of Workload and Renewable Prediction on the Value of Geographical Workload Management

Cited by 12 publications

References 15 publications

Sustainable Cloud Computing

Sustainable Cloud Computing

Toward sustainable data centers: a comprehensive energy management strategy

A Survey on Energy Efficiency in Information Systems

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