Providing RS Participation for Geo-Distributed Data Centers Using Deep Learning-Based Power Prediction

Somayyeh Taheri, Maziar Goudarzi, Osamu Yoshie

研究成果: Conference contribution

抜粋

Nowadays, geo-distributed Data Centers (DCs) are very common, because of providing more energy efficiency, higher system availability as well as flexibility. In a geo-distributed cloud, each local DC responds to the specific portion of the incoming load which distributed based on different Geographically Load Balancing (GLB) policies. As a large yet flexible power consumer, the local DC has a great impact on the local power grid. From this point of view, a local DC is a good candidate to participate in the emerging power market such as Regulation Service (RS) opportunity, that brings monetary benefits both for the DC as well as the grid. However, a fruitful collaboration requires the DC to have the capability of forecasting its future power consumption. While, given the different GLB policies, the amount of delivered load toward each local DC is a function of the whole system’s conditions, rather than the local situation. Thereby, the problem of RS participation for local DCs in a geo-distributed cloud is challenging. Motivated by this fact, this paper benefits from deep learning to predict the local DCs’ power consumption. We consider two main GLB policies, including Power-aware as well as Cost-aware, to acquire training data and construct a prediction model accordingly. Afterward, we leverage the prediction results to provide the opportunity of RS participation for geo-distributed DCs. Results show that the proposed approach reduces the energy cost by 22% on average in compared with well-known GLB policies.

元の言語English
ホスト出版物のタイトルHigh-Performance Computing and Big Data Analysis- 2nd International Congress, TopHPC 2019, Revised Selected Papers
編集者Lucio Grandinetti, Reza Shahbazian, Seyedeh Leili Mirtaheri
出版者Springer
ページ3-17
ページ数15
ISBN(印刷物)9783030334949
DOI
出版物ステータスPublished - 2019
イベント2nd International Congress on High-Performance Computing and Big Data Analysis, TopHPC 2019 - Tehran, Iran, Islamic Republic of
継続期間: 2019 4 232019 4 25

出版物シリーズ

名前Communications in Computer and Information Science
891
ISSN(印刷物)1865-0929
ISSN(電子版)1865-0937

Conference

Conference2nd International Congress on High-Performance Computing and Big Data Analysis, TopHPC 2019
Iran, Islamic Republic of
Tehran
期間19/4/2319/4/25

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • これを引用

    Taheri, S., Goudarzi, M., & Yoshie, O. (2019). Providing RS Participation for Geo-Distributed Data Centers Using Deep Learning-Based Power Prediction. : L. Grandinetti, R. Shahbazian, & S. L. Mirtaheri (版), High-Performance Computing and Big Data Analysis- 2nd International Congress, TopHPC 2019, Revised Selected Papers (pp. 3-17). (Communications in Computer and Information Science; 巻数 891). Springer. https://doi.org/10.1007/978-3-030-33495-6_1