Enhanced LSTM Model for Data Center Energy Consumption Forecast
|© 2022 by IJCTT Journal|
|Year of Publication : 2022|
|Authors : Chidiebere Enyinnah, Olawale. J. Omotosho, Samson O. Ogunlere|
|DOI : 10.14445/22312803/IJCTT-V70I4P105|
How to Cite?
Chidiebere Enyinnah, Olawale. J. Omotosho, Samson O. Ogunlere, "Enhanced LSTM Model for Data Center Energy Consumption Forecast," International Journal of Computer Trends and Technology, vol. 70, no. 4, pp. 29-33, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I4P105
High-energy consumption is a major challenge most sectors face, including data centers. the data center sector accounts for about 3% of the world`s total energy consumption, which has been predicted to keep increasing. Most data centers are run for profit-making, and the high-energy usage makes them expensive to operate. This high-energy consumption also causes environmental pollution due to the emission of greenhouse gases. Forecasting energy consumption for data centers is important in decision-making for effective energy saving. This study considered statistical, machine learning, and deep learning algorithms. Dataset was obtained from the EnergyPlus simulation platform. the simulation was informed by the information gathered from one of the leading data centers in Lagos, Nigeria. the algorithms considered were ARIMA, SVR, and LSTM. These algorithms were compared to determine an optimal algorithm using five (5) performance evaluation metrics: MSE, RMSE, MAE, MAPE, and Accuracy. the optimal algorithm was modified and utilized to develop a model. This is a step towards producing an accurate energy consumption forecast tool for data centers.
Algorithms, Data Centre, Energy, Machine Learning, Model.
 Abbas., A. Huzayyin, T. Mouneer, T, and S. Nada, S. Effect of Data Center Servers’ Power Density on the Decision of Using in-Row Cooling or Perimeter Cooling. Alexandria Engineering Journal, 60(4) (2021) 3855-3867
 (2019). A Complete Guide to Understanding Long Short-Term Memory (Lstm) Networks [Online]. Available: Http://Www.Sefidian.Com/2019/08/15/A-Complete-Guide-to-Understand-Long-Short-Term-Memory-Lstm-Networks/
 J. Ángel, G. Ordiano, , A. Bartschat,, N. Ludwig, E. Braun, S. Waczowicz,, N Renkamp, N. Peter, C. Düpmeier, R. Mikut, and V. Hagenmeyer, V. Concept and Benchmark Results for Big Data Energy Forecasting Based on Apache Spark. Journal of Big Data, 1–1 (2018).
 Beloglazov, R. Buyya., C. Lee, and A. Zomaya. A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems Anton. Advances in Computers, 82 (2011) 57–111.
 J.L Berral., R. Gavaldà, and J. Torres. Adaptive Scheduling on Power-Aware Managed Data Centers Using Machine Learning, in Proc - 12th Ieee/Acm International Conference on Grid Computing, Grid, (2011) 66–73.
 J.L Berral, I Goiri, R. Nou, F. Julià, J. Guitart, R. Gavaldà, and J. Torres, J. Towards Energy-Aware Scheduling in Data Centers Using Machine Learning, in Proc. of the E-Energy’10- 1st Int’l Conf. on Energy-Efficient Computing and Networking, 2 (2010) 215–224.
 (2017). A Gentle Introduction to Long Short-Term Memory Networks By the Experts.[Online]. Available: Https://Machinelearningmastery.Com/Gentle-Introduction-Long-Short-Term-Memory-Networks-Experts/
 Y.W Foo, C. Goh, H. C. Lim, Z.H Zhan and Y. Li, Y. Evolutionary Neural Network-Based Energy Consumption Forecast for Cloud Computing, in Proc. Icccri,15 (2015) 53–64.
 Machine Learning for Data Center Optimization. (2014). [Online]. Available: Https://Static.Googleusercontent.Com/Media/Research.Google.Com /En//Pubs/Archive/42542.Pdf,
 S. Georgios, E. Mohamed, L. Athanasios and I. Ilias, I.. on the Energy Consumption Forecasting of Data Centers Based on Weather Conditions, in Proc. Csndsp’18.
 Energy Demand Forecasting in A Rapidly Changing Landscape. (2017).[Onlie].. Available: Https://Www.Ge.Com/Power/Transform/Article.Transform.Articles .2017.Dec.Energy-Demand-Forecasting-in-A#:~:Text=Forecasting%20energy%20demand%20is%20a,Organiz ations%20involved%20in%20the%20business.&Text=Mid%2dterm %20forecasting%20(One%20month,and%20analyzing%20the%20d istribution%20network
 T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang and H. Zareipour. Energy Forecasting: A Review and Outlook. Ieee Open Access Journal of Power and Energy, 7 (2020) 376–388.
 N. Kansara, N. Neural Network Modeling, and Control of Data Center. Proc. Asme’15, (2015)
 Datacenter Load Forecast u sing a Dependent Mixture Model. (2016). [Online]. Available: Https://Openprairie.Sdstate.Edu/Etd/1120
 S. Kim. Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach. Ieee Access, (2021).
 M. Koot and F. Wijnhoven. Usage Impact on Data Center Electricity Needs: A System Dynamic Forecasting Model. Applied Energy, 291 (2021).1-13.
 P.T. Krein). Datacenter Challenges and their Power Electronics. Cpss Transactions on Power Electronics and Applications, 2(1) (2017) 39–46.
 (2022). Top 8 Tips to Optimize Your Data Center’s Hvac and Energy use for (2022). [Online]. Available: Https://Galooli.Com/Blog/Top-8-Tips-to-Optimize-Your-Data-Centers-Hvac-and-Energy-use-for-2022/
 A. Mozo, B. Ordozgoiti and S Gómez-Canaval. Forecasting Short-Term Data Center Network Traffic Load with Convolutional Neural Networks. in Plos One, 13(2) (2018).
 Essentials of Deep Learning: Introduction to Long Short Term Memory. (2017). [Online]. Available: Https://Www.Analyticsvidhya.Com/Blog/2017/12/Fundamentals-of-Deep-Learning-Introduction-to-Lstm/ Accessed on 6th January 2022.
 V. Ramachandra. Forecasting the Effect of Heat Stress Index and Climate Change on Cloud Data Center Energy Consumption. (2019) ,Arxiv, Doi: 10.13140/Rg.2.2.18802.86724.
 E. Sharma. Energy Forecasting Is Based on Predictive Data Mining Techniques in Smart Energy Grids. Energy Informatics, 1(1) (2018) 17.
 J. Shuja, K. Bilal, S.A Madani, M. Othman, R. Ranjan, P. Balaji and S.U Khan. Survey of Techniques and Architectures for Designing Energy-Efficient Data Centers. Ieee Systems Journal, 10(2) (2016) 507–519.
 Z. Song, X. Zhang and C. Eriksson. Datacenter Energy and Cost Saving Evaluation. Energy Procedia, 75 (2015) 1255–1260.
 S.K Uzaman, A.U Khan., J. Shuja, T. Maqsood, F. Rehman, and S. Mustafa. A Systems Overview of Commercial Data Centers: Initial Energy and Cost Analysis. International Journal of Information Technology and Web Engineering, 14(1) (2019) 42–65.
 A.V. Vesa, T. Cioara, I. Anghel, M. Antal, C. Pop, B. Iancu, I. Salomie and V.T. Dadarlat. Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs. Sustainability, 12(4) (2020) 1-23.
 H. Maroua, J. Nicod., Y.B. Mainassara, Z. Masry, M. Al, Péra,, M. Haddad, J. Nicod, Y.B. Mainassara, L. Rabehasaina and A.Masry. Wind and Solar Forecasting for Renewable Energy System Using Sarima-Based Model, 1-14 (2019).
 (2021). Energy-Efficient Data Center Market Seeing Significant Growth. [Online]. Available: Https://Www.Environmentalleader.Com/2021/11/Energy-Efficient-Data-Center-Market-Seeing-Significant-Growth/.
 J. Yuan, X. Miao, L. Li and X. Jiang. An Online Energy-Saving Resource Optimization Methodology for the Data Center. Journal of Software, 8(8) (2013) 1875-1880.