The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Towards energy-aware scheduling in data centers using machine learning
Open accessAbstract
As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered.
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“In particular, ML can potentially reduce global emissions (Figure 4) by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels [74,227,416,880].”
From Tackling climate change with machine learning (2023) · cites this paper as [74]
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“Berral et al. proposed a framework that demonstrates how such optimization algorithms might be combined with RL, digitized controls, and feedback systems to enable the autonomous control of industrial processes [74].”
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“Polar constitutes one of numerous approaches that address energy efficiency by adaptation at system level, for example in operating systems [3]–[6], data centre and cloud systems [7], [8], IoT systems [9], and compilers [10].”
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“This chapter is based on already published material (EEnergy10 [30], GreenBook12 [31]).”
MethodInfluentialSelf-citationAn Optimization Method with a Hybrid Objective Function for Scheduling a Flow Shop Problem
Zheng, J. H., Kobayashi, Y., Yanagida, T., Yasushi, K., & Takahashi, Y. (2020) · World Symposium on Artificial Intelligence (WSAI) · doi:10.1109/wsai49636.2020.9143311 · Cited by 1
“In order to avoid parameter tuning of the scheduling method and reduce workload of selecting schedules, machine learning models [4] were used in scheduling.”
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“In order to obtain an energy-efficient data center, J. L. Berral et al. [4] proposed an intelligent consolidation methodology using different techniques such as turning on/off machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while…”
MethodDeguchi, T., Taniguchi, Y., Hasegawa, G., Nakamura, Y., Ukita, N., Matsuda, K., & Matsuoka, M. (2016) · IEICE Transactions on Communications · doi:10.1587/transcom.2015ebp3267 · Cited by 1
“Until now, many researchers have proposed energy-efficient technologies focusing on individual data center units such as servers [3]–[5], air conditioners [6], network equipments [7] and so on.”
BackgroundAutomatic Consolidation of Virtual Machines in On-premises Cloud Platforms
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InfluentialSelf-citation
Metadata
- Authors
- Berral, J. L., Goiri, Í., Nou, R., Julià, F., Guitart, J., Gavaldà, R., & Torres, J.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Barcelona Supercomputing Center (ES)
- Venue
- Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
- Publisher
- ACM
- Published
- 2010-04-13
- Pages
- 215-224
- OpenAlex
- W2149472591
- Cited by
- 262
- References
- 28
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 5 · 2023: 10 · 2022: 11 · 2021: 10 · 2020: 25 · 2019: 16 · 2018: 21 · 2017: 14 · 2016: 23 · 2015: 22 · 2014: 26 · 2013: 24 · 2012: 20
- FWCI
- 15.28
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Energy-Efficient Computing and Networking (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by-nc-nd)
- Funders
- Federación Española de Enfermedades Raras (TIN2007-60625)
- Ministerio de Economía y Competitividad (AP2008-0264TIN2008-06582-C03TIN2008-06582-C03-01)
- Seventh Framework Programme (FP7-ICT-216886)
- Generalitat de Catalunya (2009-SGR-9802009-SGR-1428)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- energy-aware scheduling, data centers, green computing, machine learning, energy cost reduction, workload scheduling
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