RARE: Renewable Energy Aware Resource Management in Datacenters

Venkataswamy, Vanamala; Grigsby, Jake; Grimshaw, Andrew S.; Qi, Yanjun

doi:10.1007/978-3-031-22698-4_6

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…L. Grange et al propose an approach for scheduling batch jobs with due date constraints taking into account the renewable energy's availability to reduce the use of grid-imported energy [9]. Some more recent contributions use machine learning techniques to automatically learns effective job scheduling policies by continually adapting them to the data centers' complex dynamic environment (computing and renewable energy resources) [10], while others extend the scheduling policy exploration to service tasks so as to consume more on-site renewable energy [11].…”

Section: Flexibility Of the It Infrastructuresmentioning

confidence: 99%

Renewable Energy in Data Centers: The Dilemma of Electrical Grid Dependency and Autonomy Costs

Gnibga,

Blavette,

Orgerie

2024

IEEE Trans. Sustain. Comput.

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Integrating larger shares of renewables in data centers' electrical mix is mandatory to reduce their carbon footprint. However, as they are intermittent and fluctuating, renewable energies alone cannot provide a 24/7 supply and should be combined with a secondary source. Finding the optimal infrastructure configuration for both renewable production and financial costs remains difficult. In this paper, we examine three scenarios with on-site renewable energy sources combined respectively with the electrical grid, batteries alone and batteries with hydrogen storage systems. The objectives are first, to size optimally the electric infrastructure using combinations of standard microgrids approaches, secondly to quantify the level of grid utilization when data centers consume/ export electricity from/to the grid, to determine the level of effort required from the grid operator, and finally to analyze the cost of 100% autonomy provided by the battery-based configurations and to discuss their economical viability. Our results show that in the grid-dependent mode, 63.1% of the generated electricity has to be injected into the grid and retrieved later. In the autonomous configurations, the cheapest one including hydrogen storage leads to a unit cost significantly more expensive than the electricity supplied from a national power system in many countries.

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Section: Flexibility Of the It Infrastructuresmentioning

confidence: 99%

Renewable Energy in Data Centers: The Dilemma of Electrical Grid Dependency and Autonomy Costs

Gnibga,

Blavette,

Orgerie

2024

IEEE Trans. Sustain. Comput.

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“…Considering the challenge of reducing the impact of ICT on GHG emissions, several works propose power data centers using RES. In [7], the authors propose RARE, a Renewable energy Aware Resource management. This manager uses Deep Reinforcement Learning (DRL) to define the job to run, considering the job's demanded resources.…”

Section: Related Workmentioning

confidence: 99%

“…It is possible to notice that the majority of the works consider only online [7], [8], [11], [12], [15] or offline decisions [9], [10], [13], [16]. Therefore, to the best of our knowledge, only work [17] proposes a mix between offline and online decisions, for a renewable-only data center and considering battery awareness.…”

Section: Related Workmentioning

confidence: 99%

BEASY: Making EASY Backfilling Renewable-Only

de Nardin,

Stolf,

Caux

2023

2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Reducing greenhouse gas (GHG) emissions from Information and Communication Technology (ICT) has become a hot topic since the Paris Agreement. Data centers are one of the most impactful ICT energy consumers since they are built to run 24 hours / 7 days. An emerging discussion is switching their power supply from brown to green energy, using Renewable Energy Sources (RES). However, this change introduces uncertainties linked to production intermittence. This work is part of the Datazero2 project. This project designs a data center powered only by renewable production, adding storage elements to reduce the impact of the intermittence. A clean-by-design data center requires several decisions at different levels of management. To do so, it uses predictions to plan the actions for the next few days. However, it also needs to react to the actual events that can vary from the forecast. This work presents the BEASY heuristic. BEASY mixes power and scheduling decisions in a renewableonly data center, seeking to reduce the number of killed jobs and wasted energy. The results demonstrate that BEASY reduced wasted energy by up to 35.33% in critical cases. Considering the killed jobs, it also kills fewer jobs than the state of art algorithms in all executions.

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