-مدیـریت آگاه به انرژی جهت زمان بـندی وظایف بـا استفاده از تکنیک مقیاس بندی پویای ولتاژ و فرکانس در مراکز داده ابری – مطالعه موردی سازمان بنادر و دریانوردی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد فناوری اطلاعات، دانشکده فنی و مهندسی، موسسه آموزش عالی روزبهان، ساری، ایران. رایانامه: hamed.ghorbani67@gmail.com

2 استادیار گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد بابل، بابل، ایران. رایانامه: barzegar@baboliau.ac.ir

3 استادیار گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، موسسه آموزش عالی روزبهان، ساری، ایران. رایانامه: nazari.mousa@gmail.com

چکیده

مسئله مدیریت انرژی، به عنوان یکی از نگرانی های اصلی در سیستم رایانش ابری برای پشتیبانی از رشد سریع مراکز داده و محاسباتی محسوب می شود. استراتژی مقیاس بندی پویای ولتاژ و فرکانس بر روی کاهش مصرف انرژی و نیز بهینه سازی پارامتر های کارایی متمرکز شده اند. در این مقاله الگوریتم زمان بندی سه مرحله ای آگاه به انرژی و قابلیت اطمینان به نام ERADVFS با استفاده ازتکنیک DVFS بر روی پردازنده مراکز ابری با قابلیت مقیاس بندی پویای ولتاژ و فرکانس ارائه می دهیم. هدف این الگوریتم مصرف کمترین میزان انرژی و در عین حال تضمین پایایی و محدودیت زمان‌بندی وظایف است. در مرحله اول بدترین زمان اجرای هر وظیفه در هر ماشین مجازی از آن نوع روی هر سطح فرکانس را تعیین می کنیم. سپس در مرحله بعدی مهلت انجام کار بین وظایف را تقسیم می کنیم. در مرحله آخر توسط تکنیک DVFS، وظایف را به بهترین ماشین مجازی‌ اختصاص می دهیم. ایدۀ اصلی سود بردن از مقدار طولانی‌شدۀ مدت زمان اجرا، برای کاهش فرکانس ماشین مجازی است.

کلیدواژه‌ها


عنوان مقاله [English]

Energy Aware Management for Tasks Scheduling Using Dynamic Voltage and Frequency Scaling Techniques in Cloud Data Centers - A Case Study in the Ports and Maritime Organization

نویسندگان [English]

  • hamed ghorbani 1
  • behnam barzegar 2
  • mousa nazari 3
1 University College of Rouzbahan, Sari, Iran. Email: hamed.ghorbani67@gmail.com
2 Faculty of Computer Engineering, Islamic Azad University, Babol Branch, Babol, Iran. Email: barzegar@baboliau.ac.ir
3 Faculty of Computer Engineering, University College of Rouzbahan, Sari, Iran. Email: nazari.mousa@gmail.com
چکیده [English]

The issue of energy management is considered as one of the main concerns in cloud computing systems to support the rapid growth of data centers and computing Becomes. Dynamic voltage and frequency scaling strategies focus on reducing energy consumption as well as optimizing efficiency parameters. In this paper, we present an energy-conscious and reliable three-step scheduling algorithm called ERADVFS using the DVFS technique on a cloud center processor with dynamic voltage and frequency scaling capability. The goal of this algorithm is to consume the least amount of energy while ensuring reliability and limited task scheduling. In the first step, we determine the worst execution time of each task in each virtual machine of that type on each frequency level. Then in the next step, we divide the deadline for doing the work between the tasks. Finally, by assigning the DVFS technique, we assign tasks to the best virtual machine. The main idea in selecting a virtual machine is to take advantage of the extended amount of runtime to reduce the frequency of the virtual machine.

کلیدواژه‌ها [English]

  • Green Cloud Computing
  • Task Scheduling
  • Ports and Maritime Organization Data Center
  • Energy Consumption
Asghari, A., & Sohrabi, M. K. (2021). Combined use of coral reefs optimization and multi-agent deep Q-network for energy-aware resource provisioning in cloud data centers using DVFS technique. Cluster Computing. https://doi.org/10.1007/s10586-021-03368-3   
Barzegar, B., Motameni, H., & Movaghar, A. (2019). EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters. Journal of Intelligent & Fuzzy Systems, 36(6), 5135–5152. https://doi.org/10.3233/JIFS-171927
Di, S., Robert, Y., Vivien, F., Vivien, F., ENS Lyon and INRIA, F., Profile, V., Kondo, D., Wang, C.-L., & Cappello, F. (2013). Optimization of cloud task processing with checkpoint-restart mechanism. Proceedings of the International Conference on High Performance Computing. https://doi.org/http://dx.doi.org/10.1145/2503210.2503217
Fan, M., Han, Q., & Yang, X. (2017). Energy minimization for on-line real-time scheduling with reliability awareness. Journal of Systems and Software, 127, 168–176 .
Fatehi, S., Motameni, H., Barzegar, B., & Golsorkhtabaramiri, M. (2020). Energy Aware Multi Objective Algorithm for Task Scheduling on DVFS-Enabled Cloud Datacenters using Fuzzy NSGA-II. International Journal of Nonlinear Analysis and Applications. https://dx.doi.org/10.22075/ijnaa.2020.21625.2283
Garg, S. K., Yeo, C. S., Anandasivam, A., & Buyya, R. (2011). Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. Journal of Parallel and Distributed Computing, 71(6), 732–749.
Gendler, A., Knoll, E., & Sazeides, Y. (2021). I-DVFS: Instantaneous Frequency Switch during Dynamic Voltage and Frequency Scaling. IEEE Micro, 1–1. https://doi.org/10.1109/MM.2021.3096655
Goga, K., Parodi, A., Ruiu, P., & Terzo, O. (2018). Performance Analysis of WRF Simulations in a Public Cloud and HPC Environment (pp. 384–396). https://doi.org/10.1007/978-3-319-61566-035
Guérout, T., Monteil, T., Costa, G. Da, Calheiros, R., Buyya, A., & Alexandru, M. (2013). Energy-aware simulation with DVFS. Simulation Modelling Practice and Theory. https://www.sciencedirect.com/science/article/abs/pii/S1569190X13000786
Hassan, H. A., Salem, S. A., & Saad, E. M. (2020). A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment. Future Generation Computer Systems, 112, 431–448. https://doi.org/10.1016/j.future.2020.05.040
Ismail, L., & Fardoun, A. (2016). Eats: Energy-aware tasks scheduling in cloud computing systems. Procedia Computer Science, 83, 870–877.
Ismayilov, G., & Topcuoglu, H. R. (2020). Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Future Generation Computer Systems, 102, 307–322. https://doi.org/10.1016/j.future.2019.08.012
Liu, D., Chen, X., Ying, Y., Zhang, L., Li, W., Jiang, L., & Che, S. (2016). MnZn power ferrite with high Bs and low core loss. Ceramics International, 42(7), 9152–9156. https://doi.org/10.1016/j.ceramint.2016.03.005
Peng, Z., Barzegar, B., Yarahmadi, M., Motameni, H., & Pirouzmand, P. (2020). Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud. Scientific Programming, 2020, 1–14. https://doi.org/10.1155/2020/8898059
Safari, M., & Khorsand, R. (2018). Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment. Simulation Modelling Practice and Theory. https://doi.org/10.1016/j.simpat.2018.07.006
Tang, Z., Cheng, Z., Li, K., & Khan, S. . (2016). An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput.
Topcuoglu, H., Hariri, S., & Wu, M. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 260–274.
Wu, T., Gu, H., Zhou, J., Wei, T., Liu, X., & Chen, M. (2018). Soft error-aware energy-efficient task scheduling for workflow applications in DVFS-enabled cloud. Journal of Systems Architecture. https://doi.org/https://doi.org/10.1016/j.sysarc.2018.03.001
Xie, G., Chen, Y., Liu, Y., Wei, Y., Li, R., & Li, K. (2016). Resource consumption cost minimization of reliable parallel applications on heterogeneous embedded systems. IEEE Transactions on Industrial Informatics, 13(4), 1629–1640.
Xie, G., Chen, Y., Xiao, X., Xu, C., Li, R., & Li, K. (2017). Energy-efficient fault-tolerant scheduling of reliable parallel applications on heterogeneous distributed embedded systems. IEEE Transactions on Sustainable Computing, 3(3), 167–181.
Yeganeh-Khaksar, A., Ansari, M., Safari, S., Yari-Karin, S., & Ejlali, A. (2021). Ring-DVFS: Reliability-Aware Reinforcement Learning-Based DVFS for Real-Time Embedded Systems. IEEE Embedded Systems Letters, 13(3), 146–149. https://doi.org/10.1109/LES.2020.3033187
Zhang, L., Li, K., Li, C., & Li, K. (2017a). Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems. Inf. Sci. (Ny). 379, 241–256.
Zhang, L., Li, K., Li, C., & Li, K. (2017b). Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems. Information Sciences, 379, 241–256.
Zhang, L., Li, K., & Xu, Y. (2016). Joint optimization of energy efficiency and system reliability for precedence constrained tasks in heterogeneous systems. International Journal of Electrical Power & Energy Systems.
Zhou, J., & Wei, T. (2015). Stochastic thermal-aware real-time task scheduling with considerations of soft errors. Journal of Systems and Software. https://doi.org/https://doi.org/10.1016/j.jss.2014.12.009
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