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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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].”
From Tackling climate change with machine learning (2023) · cites this paper as [74]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Witten, I. H., & Frank, E. (2002) · ACM SIGMOD Record · doi:10.1145/507338.507355 · Cited by 5,176
The Case for Energy-Proportional Computing
Barroso, L. A., & Hölzle, U. (2007) · Computer · doi:10.1109/mc.2007.443 · Cited by 2,548
Managing energy and server resources in hosting centers
Chase, J. S., Anderson, D. C., Thakar, P. N., Vahdat, A. M., & Doyle, R. P. (2001) · doi:10.1145/502034.502045 · Cited by 1,285
Managing server energy and operational costs in hosting centers
Chen, L. Y., Das, A., Qin, W., Sivasubramaniam, A., Wang, Q., & Gautam, N. (2005) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1064212.1064253 · Cited by 558
Load balancing and unbalancing for power and performance in cluster-based systems
Bianchini, R., Carrera, E. V., Heath, T., & Pinheiro, E. (2001) · doi:10.7282/t3-agfw-yt73 · Cited by 480
Energy management for commercial servers
Lefurgy, C. R., Rajamani, K., Rawson, F. L., Felter, W., Kistler, M., & Keller, T. (2003) · Computer · doi:10.1109/mc.2003.1250880 · Cited by 473
Power and energy management for server systems
Bianchini, R., & Rajamony, R. (2004) · Computer · doi:10.1109/mc.2004.217 · Cited by 455
Server workload analysis for power minimization using consolidation
Verma, A., Dasgupta, G., Nayak, T. K., De, P., & Kothari, R. (2009) · Cited by 381
Liu, L., Wang, H., Liu, X., Jin, X., He, W. B., Wang, Q. B., & Chen, Y. (2009) · doi:10.1145/1555312.1555319 · Cited by 371
Iosup, A., Li, H., Jan, M., Anoep, S., Dumitrescu, C. L., Wolters, L., & Epema, D. H. J. (2008) · Future Generation Computer Systems · doi:10.1016/j.future.2008.02.003 · Cited by 344
Oceano-SLA based management of a computing utility
Appleby, K., Fakhouri, S., Fong, L., Goldszmidt, G. S., Kalantar, M., Krishnakumar, S., Pazel, D. P., Pershing, J., & Rochwerger, B. (2002) · IFIP/IEEE International Symposium on Integrated Network Management (IM) · doi:10.1109/inm.2001.918085 · Cited by 323
Evaluation of Job-Scheduling Strategies for Grid Computing
Hamscher, V., Uwe Schwiegelshohn, A., Streit, A., & Yahyapour, R. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44444-0_18 · Cited by 271
Rivoire, S., Shah, M. A., Ranganathan, P., & Kozyrakis, C. (2007) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1247480.1247522 · Cited by 231
Power-aware dynamic placement of HPC applications
Verma, A., Ahuja, P., & Neogi, A. (2008) · International Conference on Supercomputing · doi:10.1145/1375527.1375555 · Cited by 201
Managing server energy and operational costs in hosting centers
Chen, L. Y., Das, A., Qin, W., Sivasubramaniam, A., Wang, Q., & Gautam, N. (2005) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1071690.1064253 · Cited by 188
QoS-driven server migration for Internet data centers
Ranjan, S., Rolia, J. A., Fu, H., & Knightly, E. W. (2002) · International Workshop on Quality of Service · doi:10.1109/iwqos.2002.1006569 · Cited by 167
Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs
Kephart, J. O., Chan, H., Das, R., Levine, D. W., Tesauro, G. J., Rawson, F. L., & Lefurgy, C. R. (2007) · International Conference on Autonomic Computing · doi:10.1109/icac.2007.12 · Cited by 159
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
Tesauro, G. (2007) · IEEE Internet Computing · doi:10.1109/mic.2007.21 · Cited by 143
Integrated resource management for cluster-based Internet services
Shen, K., Tang, H., Yang, T., & Chu, L. (2002) · ACM SIGOPS Operating Systems Review · doi:10.1145/844128.844150 · Cited by 133
An energy case for hybrid datacenters
Chun, B.-G., Iannaccone, G., Iannaccone, G., Katz, R. H., Lee, G., & Niccolini, L. (2010) · ACM SIGOPS Operating Systems Review · doi:10.1145/1740390.1740408 · Cited by 127
Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning
Tesauro, G. J., Das, R., Chan, H., Kephart, J. O., Levine, D., Rawson, F. L., & Lefurgy, C. R. (2007) · Cited by 118
Autonomic multi-agent management of power and performance in data centers
Das, R., Kephart, J. O., Lefurgy, C. R., Tesauro, G. J., Levine, D., & Chan, H. (2008) · doi:10.5555/1402795.1402816 · Cited by 111
A reinforcement learning approach to dynamic resource allocation
Vengerov, D. (2006) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.06.019 · Cited by 95
Autonomic power and performance management for computing systems
Khargharia, B., Hariri, S., & Yousif, M. S. (2007) · Cluster Computing · doi:10.1007/s10586-007-0043-6 · Cited by 84
Dynamic Data Center Power Management Trends, Issues, and Solutions
Filani, D. (2008) · Intel Technology Journal · doi:10.1535/itj.1201.06 · Cited by 66
Autonomic QoS control in enterprise Grid environments using online simulation
Nou, R., Kounev, S., Julià, F., & Torres, J. (2008) · Journal of Systems and Software · doi:10.1016/j.jss.2008.07.048 · Cited by 40
Self-citationXtreemOS: A Vision for a Grid Operating System
Cortés, T., Franke, C., Jégou, Y., Kielmann, T., Laforenza, D., Matthews, B. W., Morin, C. A., Prieto, L. P., & Reinefeld, A. (2008) · Cited by 32
Elastic Management of Tasks in Virtualized Environments
Presa, Í. G., Guitart, J., & Torres, J. (2009) · Cited by 15
Self-citation
Research works printed but not linked: 3
- T. Cortes and R. Nou . AEM prototype , D3.3.6, XtreemOS deliverable , 2008 . T. Cortes and R. Nou. AEM prototype, D3.3.6, XtreemOS deliverable, 2008.
- V. Petrucci , O. Loques , and D. Mossé . A dynamic configuration model for power-efficient virtualized server clusters . In 11th Brazillian Workshop on Real-Time and Embedded Systems (WTR) , 2009 . V. Petrucci, O. Loques, and D. Mossé. A dynamic configuration model for power-efficient virtualized server clusters. In 11th Brazillian Workshop on Real-Time and Embedded Systems (WTR), 2009.
- V. Petrucci , O. Loques , B. Niteroi , and D. Mossé . Dynamic configuration support for power-aware virtualized server clusters . In WiP Session of the 21th Euromicro Conference on Real-Time Systems . Dublin, Ireland , 2009 . V. Petrucci, O. Loques, B. Niteroi, and D. Mossé. Dynamic configuration support for power-aware virtualized server clusters. In WiP Session of the 21th Euromicro Conference on Real-Time Systems. Dublin, Ireland, 2009.
Cited by
Sorted by citations
Beloglazov, A., & Buyya, R. (2011) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.1867 · Cited by 1,869
“Berral et al [27] have studied the problem of dynamic consolidation of VMs running applications with deadlines that are set in the SLAs.”
BackgroundData Center Energy Consumption Modeling: A Survey
Dayarathna, M., Wen, Y., & Fan, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2481183 · Cited by 1,165
“Then high-level information predictors are learned to drive decision-making algorithms for virtualized service schedulers, without much expert knowledge or real-time supervision [370][371].”
BackgroundA Survey on Machine Learning Techniques for Cyber Security in the Last Decade
Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., & Xu, M. (2020) · IEEE Access · doi:10.1109/access.2020.3041951 · Cited by 607
“ML techniques have applications in the energy sector in which power-aware strategies were designed to reduce the power consumption for data centers and companies [652].”
Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100841 · Cited by 396
Mastelić, T., Oleksiak, A., Claußen, H., Brandić, I., Pierson, J., & Vasilakos, A. V. (2014) · ACM Computing Surveys · doi:10.1145/2656204 · Cited by 278
“The listed goals are taken as a basis for the literature review in our search to find current as well as future research directions that focus on improving the energy efficiency of cloud computing infrastructure.”
Background
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
