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Seven Pillars to Achieve Energy Efficiency in High-Performance Computing Data Centers

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Recent Trends and Advances in Wireless and IoT-enabled Networks

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Nowadays, data centers and high-performance computing (HPC) systems are crucial for intensive computing environments. The energy efficiency in HPC is an evergreen problem. Moreover, energy-efficient design and energy ecology measures are core challenges in HPC. However, current research focuses on practical methods to measure power utilization to take decisions for green computing without exceeding resources and without compromising on performance. This paper surveys the issues, challenges, and their solutions over the period 2010–2016, by focusing on the energy consumption of data centers and HPC systems. We grouped existing problems in energy efficiency that data centers are currently facing. Our contribution is twofold. Firstly, with this categorization, we aim to provide an easy and concise view of the underlying energy efficiency model adopted by each approach. Secondly, we propose seven-pillar framework for energy efficiency in HPC systems and data centers for the first time.

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References

  1. Liu, Y., & Zhu, H. (2010). A survey of the research on power management techniques for high-performance systems. Software Practice and Experience, 40(11), 943–964.

    Article  Google Scholar 

  2. Computing, M. (2013). Energy awareness in HPC: A survey. International Journal of Computer Science and Mobile Computing, 2, 46–51.

    Google Scholar 

  3. Kamil, S., Shalf, J., & Strohmaier, E. (2008). Power efficiency in high performance computing. In 2008 IEEE International Symposium on Parallel and Distributed Processing (pp. 1–8).

    Google Scholar 

  4. Fürlinger, K., Klausecher, C., & Kranzlmüller, D. (2011). The AppleTV-Cluster: Towards energy efficient parallel computing on consumer electronic devices. In White paper. Ludwig-Maximilians-Universitat.

    Google Scholar 

  5. Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275, 314–347.

    Article  Google Scholar 

  6. Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools and good practices. In 2013 6th International Conference on Contemporary Computing IC3 2013 (pp. 404–409).

    Google Scholar 

  7. Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.

    Article  Google Scholar 

  8. Xu, X., Lin, G., & Wang, J. (2014). An adaptive model of energy consumption predictor for big data centers. In Proceedings of 2014 International Conference on Computer, Communications and Information Technology (pp. 60–64).

    Google Scholar 

  9. Villars, R. L., Olofson, C. W., & Eastwood, M. (2011). White paper big data: What it is and why you should care information everywhere, but where’s the knowledge? (pp. 1–14).

    Google Scholar 

  10. Reed, D. A., & Dongarra, J. (2015). Exascale computing and big data. Communications of the ACM, 58(7), 56–68.

    Article  Google Scholar 

  11. Hpc, E. (2009). The challenge of energy efficient HPC (pp. 50–57). Doctoral dissertation, Louisiana State University.

    Google Scholar 

  12. Wilde, T., Auweter, A., & Shoukourian, H. (2014). The 4 pillar framework for energy efficient HPC data centers. Computer Science, 29(3–4), 241–251.

    Google Scholar 

  13. Lövehagen, N., & Bondesson, A. (2013). Evaluating sustainability of using ICT solutions in smart cities – Methodology requirements.

    Google Scholar 

  14. Agrawal, D., Das, S., & El Abbadi, A. (2011). Big data and cloud computing: Current state and future opportunities. In Proceedings of the 14th International Conference on Extending Database Technology (pp. 530–533).

    Google Scholar 

  15. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel Distributed Computing, 74(7), 2561–2573.

    Article  Google Scholar 

  16. Rodero, I., Viswanathan, H., Lee, E. K., Gamell, M., Pompili, D., & Parashar, M. (2012). Energy-efficient thermal-aware autonomic management of virtualized HPC cloud infrastructure. Journal of Grid Computing, 10(3), 447–473.

    Article  Google Scholar 

  17. Kaisler, S., & Armour, F. (2013). Big data: Issues and challenges moving forward. In 2013 46th Hawaii International Conference on System Sciences (HICSS) (pp. 995–1004). Maui, HI: IEEE.

    Chapter  Google Scholar 

  18. Michel, B., Brunschwiler, T., Meijer, G. I., Paredes, S., & Escher, W. (2010). Direct waste heat utilization from liquid-cooled supercomputers. In 14th International Heat Transfer Conference, Washington (p. 23352).

    Google Scholar 

  19. Torrellas, J., Quinlan, D., & Livermore, L. (2012). Thrifty: An exascale architecture for energy-proportional computing (pp. 2011–2012).

    Google Scholar 

  20. Bakshi, K. (2012). Considerations for big data: Architecture and approach. In 2012 IEEE Aerospace Conference (pp. 1–7).

    Google Scholar 

  21. Valentini, G. L., Lassonde, W., Khan, S. U., Min-Allah, N., Madani, S. A., Li, J., et al. (2013). An overview of energy efficiency techniques in cluster computing systems. Cluster Computing, 16(1), 3–15.

    Article  Google Scholar 

  22. Zimmermann, S., Meijer, I., Tiwari, M. K., Paredes, S., Michel, B., & Poulikakos, D. (2012). Aquasar: A hot water cooled data center with direct energy reuse. Energy, 43(1), 237–245.

    Article  Google Scholar 

  23. Crump, G. (2014). The modern HPC storage architecture. Journal of Parallel and Distributed Computing, 74(7), 2561–2573.

    Article  Google Scholar 

  24. Demchenko, Y., & Zhao, Z. (2012). Addressing big data challenges for scientific data infrastructure. In IEEE 4th International Conference (pp. 614–617).

    Google Scholar 

  25. Huber, H., Auweter, A., Wilde, T., Meijer, I., Archer, C., Bloth, T., et al. (2012). Case study: LRZ liquid cooling, energy management, contract specialities. In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis (pp. 962–992).

    Chapter  Google Scholar 

  26. Zomaya, A., Lee, Y., Ge, R., & Cameron, K. (2012). Power-aware high performance computing. In Energy-efficient distributed computing systems. Hoboken, NJ: Wiley.

    Chapter  Google Scholar 

  27. Meijer, G. I. (2010). Cooling energy-hungry data centers. Science, 328, 318.

    Article  Google Scholar 

  28. Younge, A. J., Henschel, R., Brown, J. T., von Laszewski, G., Qiu, J., & Fox, G. C. (2011). Analysis of virtualization technologies for high performance computing environments. In 2011 IEEE 4th International Conference on Cloud Computing (pp. 9–16).

    Chapter  Google Scholar 

  29. Clarke, J., Kirk, K., Collins, J., Chopra, A., & Renard, K. (2011, September). Project HPC: A multi-tier architecture for simulation and analysis.

    Google Scholar 

  30. Mitra, S. (2014). Using UML modeling to facilitate three-tier architecture projects in software engineering courses. ACM Transactions on Computing Education, 14(3), 1–31.

    Article  Google Scholar 

  31. Jaffe, A. B., & Stavins, R. N. (1994). The energy-efficiency gap: What does it mean? Energy Policy, 22(10), 804–810.

    Article  Google Scholar 

  32. Gupta, A., Sarood, O., Kale, L. V., & Milojicic, D. (2013). Improving HPC application performance in cloud through dynamic load balancing. In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (pp. 402–409).

    Chapter  Google Scholar 

  33. Tiwari, A., Laurenzano, M. A., Carrington, L., & Snavely, A. (2012). Modeling power and energy usage of HPC kernels. In 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (pp. 990–998).

    Chapter  Google Scholar 

  34. Rodero, I., & Parashar, M. (2012). Energy efficiency in HPC systems. In Energy-efficient distributed computing systems (pp. 81–108). Hoboken, NJ: Wiley.

    Chapter  Google Scholar 

  35. Courtney, M. (2012). The larging-up of big data. Engineering and Technology, 7(8), 72–75.

    Article  Google Scholar 

  36. Mills, B., Znati, T., Melhem, R., Ferreira, K. B., & Grant, R. E. (2014). Energy consumption of resilience mechanisms in large scale systems. In 2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (pp. 528–535).

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Sardar Mehboob Hussain, Abdul Wahid, Munam Ali Shah, Adnan Akhunzada & Saba Arshad

  2. Department of Computer Science, Bacha Khan University, Charsadda, Pakistan

    Faheem Khan & Noor ul Amin

  3. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Ihsan Ali

Authors
  1. Sardar Mehboob Hussain
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  2. Abdul Wahid
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  3. Munam Ali Shah
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  4. Adnan Akhunzada
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  5. Faheem Khan
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  6. Noor ul Amin
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  7. Saba Arshad
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  8. Ihsan Ali
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Editor information

Editors and Affiliations

  1. Abdul Wali Khan University Mardan, Mardan, Pakistan

    Mian Ahmad Jan

  2. Abdul Wali Khan University Mardan, Mardan, Pakistan

    Fazlullah Khan

  3. Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China

    Muhammad Alam

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Hussain, S.M. et al. (2019). Seven Pillars to Achieve Energy Efficiency in High-Performance Computing Data Centers. In: Jan, M., Khan, F., Alam, M. (eds) Recent Trends and Advances in Wireless and IoT-enabled Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-99966-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-99966-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99965-4

  • Online ISBN: 978-3-319-99966-1

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