A Methodology for Redistributing the Operating Virtual Machines among the Servers in a Data Center

  • Александр [Aleksandr] Анатольевич [A.] Ларин [Larin]
  • Леонид [Leonid] Иванович [I.] Абросимов [Abrosimov]
Keywords: data center energy consumption, resources of servers and virtual machines, virtual machines redistribution procedure, data center

Abstract

The need for centralized processing and storage of data is growing every year. Data centers of still greater capacity and computing power are put into operation for this purpose. To reduce their expenses the owners of data centers pay special attention to optimizing the energy consumed by the data center infrastructure. An approach for improving the data center’s overall energy efficiency through redistributing its load with subsequently shutting down the idle servers is considered. A mechanism governing the migration of virtual machines for optimizing power consumption by the servers is outlined. The existing methods used to redistribute virtual machines either perform a full search which is impossible in the case of real dimensions or disregard the power consumption criterion. A procedure is proposed that excludes the need to perform a full search and places focus on improving the efficiency according to the criterion of total energy consumption by the data center servers due to redistribution of the operating virtual machines among the data center servers. The proposed method is based on a heuristic procedure of selecting the destination servers and consists of a static part, which collects information about the available resources, and a dynamic part in which the virtual machines are distributed among the servers depending on the initial data and the preset limiting parameters. A model example demonstrating the operability of the method is given which outlines the steps in applying the method and shows how the destination servers are selected for migrations.

Information about authors

Александр [Aleksandr] Анатольевич [A.] Ларин [Larin]

Workplace

Computing Machines, Systems and Networks Dept., NRU MPEI

Occupation

Ph.D.-student

Леонид [Leonid] Иванович [I.] Абросимов [Abrosimov]

Science degree:

Dr.Sci. (Techn.)

Workplace

Computing Machines, Systems and Networks Dept., NRU MPEI

Occupation

Professor

References

1. Облачный провайдинг: экономика, стратегии, бизнес-модели [Электрон. ресурс] URL: http://www. iks-consulting.ru/raitings-220.html (дата обращения 03.01.2017)

2. Обзор: облачные сервисы 2014 [Электрон. ресурс] URL: http://www.cnews.ru/reviews/cloud_2014/ review_table/655b2c19b5e2ed63f1792efd5a3e786121fd6 9d2/ (дата обращения 23.12.2016)

3. Google's DeepMind Trains AI to Cut its Energy Bills by 40% [Электрон. ресурс] URL: http://www.wired. co.uk/article/google-deepmind-data-centres-efficiency (дата обращения 25.11.2017)

4. Pedram M., Hwang I. Power and Performance Modeling in a Virtualized Server System [Электрон. ресурс] URL: http://www.mpedram.com/Papers/Virtualsystem-modeling-greencom10.pdf (дата обращения 20.02.2017)

5. Аверьянихин А.Е., Котельницкий А.В., Муравьев К.А. Методика расчета оптимального числа узлов кластера виртуализации частного облака виртуальных рабочих столов по критерию эффективности // Международный научно-исследовательский журнал. 2016. № 5 (47). С. 6—13.

6. Соловьев В.П., Удовиченко А.О. Метод планирования размещения группы виртуальных машин с перераспределением ресурсов // Программные продукты и системы. 2012. № 1. С. 134—137.

7. Ворожцов А.С., Тутова Н.В., Тутов А.В. Методика оптимального распределения виртуальных серверов в центрах обработки данных // T-Comm – Телекоммуникации и транспорт. 2015. № 7. C. 5—10.

8. Абросимов Л. И., Крамаренко М. Д., Гончаренко О. С. Лабораторные работы по исследованию вероятностно-временных характеристик прокси-сервера [Электрон. ресурс] URL:http://network-journal.mpei. ac.ru/cgi-bin/main.pl?l=ru&n=27&pa=15&ar=1 (дата обращения 28.03.2017)
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Для цитирования: Ларин А.А, Абросимов Л.И. Методика перераспределения функционирующих виртуальных машин по серверам в дата-центре // Вестник МЭИ. 2018. № 1. С. 98—105. DOI: 10.24160/1993-6982-2018-1-98-105.
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1. Oblachnyy Provayding: Ekonomika, Strategii, Biznes-modeli [Elektron. Resurs] URL: http://www.iks- consulting.ru/raitings-220.html (Data Obrashcheniya 03.01.2017) (in Russian).

2. Obzor: Oblachnye Servisy 2014 [Elektron. Resurs] URL: http://www.cnews.ru/reviews/cloud_2014/review_ table/655b2c19b5e2ed63f1792efd5a3e786121fd69d2/ (Data Obrashcheniya 23.12.2016) (in Russian).

3. Google's DeepMind trains AI to cut its energy bills by 40% [Elektron. resurs] URL: http://www.wired.co.uk/ article/google-deepmind-data-centres-efficiency (Data Obrashcheniya 25.11.2017)

4. Pedram M., Hwang I. Power and Performance Modeling in a Virtualized Server System [Elektron. Resurs] URL: http://www.mpedram.com/Papers/Virtual system-modeling-greencom10.pdf (Data Obrashcheniya 20.02.2017)

5. Aver'yanihin A.E., Kotel'nitskiy A.V., Murav'- ev K.A. Metodika Rascheta Optimal'nogo Chisla Uzlov Klastera Virtualizatsii Chastnogo Oblaka Virtual'nyh Rabochih Stolov po Kriteriyu Effektivnosti. Mezhdu- narodnyy Nauchno-issledovatel'skiy Zhurnal. 2016;5 (47):6—13. (in Russian).

6. Solov'ev V.P., Udovichenko A.O. Metod Planirovaniya Razmeshcheniya Gruppy Virtual'nyh Mashin s Pereraspredeleniem Resursov. Programmnye Produkty i Sistemy. 2012;1:134—137. (in Russian).

7. Vorozhtsov A.S., Tutova N.V., Tutov A.V. Metodika Optimal'nogo Raspredeleniya Virtual'nyh Serverov v Tsentrah Obrabotki Dannyh. T-Comm – Telekommunikatsii i Transport. 2015;7:5—10. (in Russian).

8. Abrosimov L. I., Kramarenko M. D., Goncharenko O. S. Laboratornye Raboty po Issledovaniyu veroyatnostno-vremennyh Harakteristik Proksi-servera [Elektron. Resurs] URL:http://network-journal.mpei.ac.ru/ cgi-bin/main.pl?l=ru&n=27&pa=15&ar=1 (Data Obra- shcheniya 28.03.2017) (in Russian).
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For citation: Larin A.A., Abrosimov L.I. A Methodology for Redistributing the Operating Virtual Machines among the Servers in a Data Center. MPEI Vestnik. 2018;1:98—105. (in Russian). DOI: 10.24160/1993-6982-2018-1-98-105.
Published
2019-01-29
Section
Informatics, computer engineering and control (05.13.00)