تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,501 |
تعداد مشاهده مقاله | 124,104,878 |
تعداد دریافت فایل اصل مقاله | 97,210,576 |
Reducing the Break-even Time by Smart Power Managing in Data Center with Renewable Energy | ||
Journal of Information Technology Management | ||
مقاله 6، دوره 7، شماره 4، دی 2015، صفحه 805-824 اصل مقاله (682.43 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jitm.2015.54270 | ||
نویسندگان | ||
Somayyeh Taheri؛ Maziar Goudarzi* | ||
چکیده | ||
Due to the increasing cost and environmental impacts of fossil fuel consumption, using the renewable energies in data center has become an important and essential priority. Although using renewable energies has less environmental harmful effects, but the high investment needed for their installations as well as their fluctuating power production characteristic, restricts their usage such that most of data centers' managers prefer to use brown energy instead. In this paper, the power management and cost reduction techniques, with the aim of reducing break-even time, like using the electricity pricing models' opportunity and distributed Uninterruptable Power Supply (UPS) has been considered. In each time slot, the proposed Smart Power Manager Unit (SPMU) specifies the optimized distribution of power between available power supplies based on available solar energy, the price of electricity and the status of distributed UPS batteries; it also manages batteries charging and discharging. Results show that proposed method reduces the break-even time of investment cost on solar power installation to 0/8- 1/2 years that is 36% shorter than conventional method on average. | ||
کلیدواژهها | ||
Data Center؛ Distributed UPS؛ Electricity Pricing Model؛ Renewable Energy source | ||
عنوان مقاله [English] | ||
کاهش زمان بازگشت سرمایه از طریق مدیریت هوشمند توان مصرفی در مرکز دادۀ دارای انرژی خورشیدی | ||
نویسندگان [English] | ||
سمیه طاهری؛ مازیار گودرزی | ||
چکیده [English] | ||
توجه به مسئلۀ رشد هزینه و پیامدهای منفی مصرف سوختهای فسیلی، استفاده از منابع تجدیدپذیر انرژی را به اولویتی اساسی در مراکز داده تبدیل کرده است. اگرچه استفاده از این منابع موجب کاهش آثار مخرب زیستمحیطی مصرف سوختهای فسیلی میشود، هزینۀ زیاد راهاندازی نیروگاه و طبیعت نوسانی این منابع، استفاده از آن را محدود میکند؛ بهگونهای که بسیاری از گردانندگان مراکز داده ترجیح میدهند از منابع برق شهری استفاده کنند. در این مقاله روشهای مدیریت توان مصرفی و کاهش هزینه با هدف کاهش زمان بازگشت سرمایه، مانند بهرهمندی از فرصتهای برآمده از الگوی محاسبۀ هزینۀ انرژی و مدل توزیعشدۀ برق اضطراری (UPS)، بررسی شده است. واحد مدیریت هوشمند توان مصرفی (SPMU) پیشنهادشده، در هر فاصلۀ زمانی با توجه به میزان انرژی خورشیدی در دسترس، قیمت برق شهری و وضعیت باتریهای توزیعشدۀ UPS، توزیع بهینهای از توان ذخیرهشدۀ هر منبع ارائه میدهد و مدیریت شارژ و تخلیۀ باتریها را برعهده میگیرد. بر اساس نتایج، این روش زمان بازگشت سرمایه را بین 2/1 - 8/0 سال کاهش میدهد که بهطور متوسط 36 درصد کمتر از روش مرسوم است. | ||
کلیدواژهها [English] | ||
الگوی محاسبۀ هزینۀ انرژی, مدل توزیعشدۀ برق اضطراری, مرکز داده, منبع انرژی تجدیدپذیر | ||
مراجع | ||
Ardagna, D., Panicucci, B., Trubian, M. & Zhang, L. (2012). Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environment, IEEE Transactions on Services Computing, 5(1): 2-19. Arlitt, M., Bash, C., Blagodurov, S., Chen Y. & Christian, T. (2012). Towards the design and operation of net-zero energy data centers. 13th IEEE Intersociety Conference on Thermal and Thermo mechanical Phenomena in Electronic Systems (ITherm), 552 (561), May 2012. Barroso, L. A., Clidaras, J. & Hoelzle, U. (2013). The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, (2 Ed), Madison: Morgan and Claypool Publishers. Barroso, L. A. & Holzle, U. (2007). The Case for Energy-Proportional Computing. Journal of Computer, 40(12): 33-37. Chang, F., Ren J. & Viswanathan, R. (2010). Optimal Resource Allocation in Clouds, Proceedings of the 1th International Conference on Cloud Computing, Miami July 2010. Facebook Hacking Conventional Computing Infrastructure. (2011). Available at: http://opencompute.org/. Fan, X., Weber, W. & Barroso, L. A. (2007). Power Provisioning for a Warehouse-Sized Computer. Proceedings of the 1th International Symposium on Computer Architecture, June 2007. Goiri, I. (2012). Green Hadoop: Leveraging Green Energy in Data-Processing Frameworks. 7th ACM European Conference on Computer System, pp. 57-70, New York, April 2012. Goiri I., Katsak W., Ley K., Nguyen D. & Bianchini R. (2013). Parasol and Green Switch: Managing Datacenters Powered by Renewable Energy.18th International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 51-64. Google Report (2014). Available at: http://www.google.com/transparencyreport/ traffic/explorer. Google Summit (2009). Available at: http://www.google.Com/corporate /datacenter /events/dc-summit-2009.html. Goudarzi, H., Ghasemazar, M. & Pedram, M. (2012). SLA-based Optimization of Power and Migration Cost in Cloud Computing, 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 172(179): 13-16. Goudarzi H., Pedram M. (2011). Multi-dimensional SLA-Based Resource Allocation for Multi-tier Cloud Computing Systems, Proceedings of the IEEE International Conference on Cloud Computing (CLOUD), pp: 324 – 331. Govindan, S., Wang, D., Sivasubramaniam, A. & Urgaonkar, B. (2013). Aggressive Datacenter Power Provisioning with batteries. ACM Transaction on Computer Science, 31(1), February 2013. Greenpeace International. (2008). Avaialable at http://www.greenpeace.org/ international/en/press/releases/Greenpeace-likes-Facebooks- new- datacenter- but- wants- a- greener friendship. Kontorinis, V., Sampson, J., Zhang, L., Aksanli, B., Homayoun, H., Rosing, T. & Tullsen, D. (2012). Battery Provisioning and Associative Costs for Data Center Power Capping. UCSD Technical Report CS, July 2012. Koomey, G. (2008). Estimating Total Power Consumption by Servers in the U.S. and the World, A report by the Lawrence Berkeley National Laboratory Public Law, 109-431. Lefurgy, C., Wang, X. & Ware, M. (2007). Server-Level Power Control, Proceedings of the 4th International Conference on Autonomic Computing (ICAC), pp: 4-4. Liu, L., Wang, H., Liu, X., Jin, X., He, W., Wang, Q. & Chen, Y. (2009). Green Cloud: A new Architecture for Green Datacenter. Proceedings of the 1th International Conference on Industry Session on Autonomic Computing and Communications, ICAC-INDST'09, June 2009. Meisner, D., Gold, B. T. & Wenisch, T. F. (2009). PowerNap: Eliminating Server Idle Power. Proceedings of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems, pp: 205-216. Report to Congress on Server and Datacenter Energy Efficiency (2006). Public Law -U.S. Environmental Protection Agency. Avaialable at: http://hightech.lbl. gov/documents /data_centers/epadatacenters.pdf. Sun Electronics Company (2014). Available at: http://sunelec.com/solar-panels. Tolia N., Wang Z., Marwah M., C. Bash, P. Ranganathan, and Zhu X. (2008), Delivering Energy Proportionality with Non-Energy Proportional Systems – Optimizing the Ensemble, Proceedings of the 2008 conference on Power aware computing and systems, pp: 2-2. U.S. Energy Information Administration. (2014). http://www.eia.gov/electricity. Urgaonkar, R., Urgaonkar, B., Neely M. & Sivasubramaniam, A. (2011). Optimal Power Cost Management using stored Energy in data centers, ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp: 221-232. Wang, H., Huang, J., Lin X. & Mohsenian-Rad, H. (2013). Exploring Smart Grid and Data Center Interactions for Electric Power Load Balancing. ACM SIGMETRICS Performance Evaluation Review, 41(3): 89-94. World Weather Forecast: Available at: http://www.accuweather.com /en/ir/iran-weather. | ||
آمار تعداد مشاهده مقاله: 2,935 تعداد دریافت فایل اصل مقاله: 1,374 |