Balancing the Use of Batteries and Opportunistic Scheduling Policies for Maximizing Renewable Energy Consumption in a Cloud Data Center

Li, Yunbo; Orgerie, Anne‐Cécile; Menaud, Jean-Marc

doi:10.1109/pdp.2017.24

Cited by 22 publications

(25 citation statements)

References 17 publications

(20 reference statements)

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“…However, there are not many studies that provide a detailed consideration of joint IT and energy management in green datacenters. Some studies that have certain levels of that consideration are [19][20][21][22][23][24][25]. Among them, the articles [19] and [20] are from a same research work; also, the articles [23,24], and [25] are from a same research work.…”

Section: Related Workmentioning

confidence: 99%

“…The research in [21,22,27] and [28] also considers joint IT and energy management, though the energy management is limited. Li et al [22] presented two methods to maximize the utilization of renewable energy in a small/medium-sized datacenter. The first method is an opportunistic scheduling, which suggests to run more jobs when renewable energy is available.…”

Section: Related Workmentioning

confidence: 99%

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Negotiation game for joint IT and energy management in green datacenters

Thi

Pierson

Costa

et al. 2020

Future Generation Computer Systems

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As the power demand of datacenters is increasing sharply, a promising solution is to power datacenters locally by renewable energies. However, one of the main challenges when operating such green datacenters is to conciliate the intermittent power supply and the power demand. To deal with this problem, we view the green datacenter as two sub-systems, namely, Information Technology (IT) sub-system which consumes energy, and electrical sub-system which supplies energy. The objective is to find an efficient trade-off between the power demand and power supply, respecting the operational requirements of both sub-systems (i.e., the requirements on utility, or monetary gain, which includes monetary revenue and monetary cost). First, we analyze the problem by a black-box approach. In this approach, the models of the two sub-systems are unknown to each other, and the two sub-systems negotiate by exchanging their power preferences. However, we found that the black-box approach cannot guarantee stable solutions in term of execution time and generational distance (which is the distance between a solution and the Pareto front). Then we introduce a semi black-box approach, in which the two subsystems are modeled as the buyer and the supplier in a buyer-supplier negotiation game. We propose an algorithm that allows the buyer and supplier to negotiate, seeking for an efficient trade-off between the power demand and power supply. The analytical results show that the semi black-box algorithm converges to equilibrium, and these results are then confirmed by experimental results. We conduct the experiments by implementing a middleware of a datacenter powered by renewable energies. The experimental results show that the semi black-box algorithm improves significantly the stability, quality of service (QoS) and utility of the datacenter, compared to other algorithms. In term of stability, compared to the black-box algorithm, the semi black-box algorithm reduces the standard deviation of execution time and generational distance by 23 and 27 times, respectively. In term of QoS and utility, the semi black-box algorithm outperforms the algorithms that do not consider joint IT-energy management, as well as the algorithms that do not utilize a semi black-box design.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Negotiation game for joint IT and energy management in green datacenters

Thi

Pierson

Costa

et al. 2020

Future Generation Computer Systems

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“…This effect does not allow reliable control of the grid/sub-system nor the optimization of the installed storage, which has to be larger in order to accommodate such uncertainties. Most of the research performed on storage optimization (Megel et al, 2015), data-center scheduling based on green energy (Aksanli et al, 2011, Goiri et al, 2015, Li et al, 2017 and smart grid fine-control (Golshannavaz et al, 2014), is based on the assumption of perfect (or close to) detailed energy yield forecasts in order to enable the rest of the findings. Any increase in error margins reduces cost efficiency and therefore makes the solutions unattractive.…”

Section: Related Workmentioning

confidence: 99%

A method for detailed, short-term energy yield forecasting of photovoltaic installations

Anagnostos

Schmidt²,

Cavadias

et al. 2019

Renewable Energy

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The global shift towards renewable energy production suggests a promising future on reducing carbon emissions and avoiding the detrimental effects of global warming, but also creates additional challenges on all levels of energy production and distribution. The expected penetration of electric cars, increasing energy usage of cloud computing centers and the transformation of the electricity grid itself towards the "Smart Grid" requires novel solutions on all levels of energy production and management. Forecasting of energy production especially will become a major component for design and operation in all temporal and spatial scales, creating opportunities for optimized control of energy storage, local energy exchange etc. To this end, a method for the creation of detailed and accurate energy yield forecasts for PV installations is presented. Based on sky-imager information and using tailored neural networks, highly detailed energy yield forecasts are produced for a monitored test installation, for horizons up to 15 minutes and with a resolution of 1 second. The described method manages to outperform state of the art models by up to 39% in forecast skill, while at the same time retaining temporal resolutions that enable control schemes and energy exchange in a local scale.

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“…Again, the production variability of most renewable sources pushes DCs to only partially rely on them and to depend on the regular electrical grid as a backup or main supplier [6]. Nevertheless, even when supplying the entire DC energy source with renewable energy as the case of actual Google DCs, it is crucial to support this with a regular energy-efficient scheduling of the entire inter-and intra-DCs cloud infrastructure and platform utilization.…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Balancing the Use of Batteries and Opportunistic Scheduling Policies for Maximizing Renewable Energy Consumption in a Cloud Data Center

Cited by 22 publications

References 17 publications

Negotiation game for joint IT and energy management in green datacenters

Negotiation game for joint IT and energy management in green datacenters

A method for detailed, short-term energy yield forecasting of photovoltaic installations

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

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