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Energy efficiency in cloud computing data center: a survey on hardware technologies

Abstract

The internet is expanding its viewpoint into each conceivable part of the cutting-edge economy. Unshackled from our web programs today, the internet is characterizing our way of life, regardless of whether it's sitting in front of the TV or driving an independent auto. The enchantment of the internet appears to be relatively unbounded. In any case, with each new spell there comes an ever-increasing amount of data, and interest for computational power. Cloud computing which is an on-request conveyance of computing power, applications, database storage, and other IT assets by means of the Internet has violently expanded our computerized lives. Though, there have been critical improvements as far as accessibility, fluctuation, time and quality in administrations are concerned; the unbounded development of our computerized way of life requires monstrous measures of power, especially for the data centers that fill in as the mind of the advanced economy. Data organizations foresee a decrease in the quantity of data centers, as more businesses close their little data centers and move towards cloud computing. All things considered, the move by clients towards cloud, will increase the general energy utilization significantly, exceeding any energy productivity increase; which has recorded for over 70% of data center development in 2018. Many research advancements are already made in this domain for minimizing the energy utilization of the computing types of gear included; for efficient power energy consumption, decrease of carbon impression and e-squander. These procedures are supporters of green cloud computing, which are focused on planning and advancing energy-proficient activities to contain inordinate energy utilization in data centers. This paper discusses different mechanisms for lowering the power utilization in data centers. It provides in depth detail about the various mechanisms that can be employed at the hardware component level so that the utilization of energy by component can be reduced. Techniques that can be applied at network, cluster of servers’ level along with the various dynamic power management measures that can be employed at the hardware or firmware level and can lead to energy efficient or green data centers are also studied in detail. The paper concludes with the research challenges for building the green data centers.

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References

  1. 1.

    Paper, W.: Cisco Visual Networking Index: Forecast and Methodology. Forecast and Methodology 2015–2020. White Paper. CISCO, San Jose, CA, USA (2015)

  2. 2.

    Cisco Visual Networking Index (VNI) Mobile Forecast Projects Nearly 10-fold Global Mobile Data Traffic Growth Over Next Five Years | The Network. https://newsroom.cisco.com/press-release-content?type=webcontent&articleId=1578507. Accessed 26 Jan 2021.

  3. 3.

    El-Latif, A.A.A., Abd-El-Atty, B., Hossain, M.S., Elmougy, S., Ghoneim, A.: Secure quantum steganography protocol for fog cloud internet of things. IEEE Access 6, 10332–10340 (2018)

    Article  Google Scholar 

  4. 4.

    You, W. & Learn, W.: Cisco Global Cloud Index: Forecast and Methodology. 2013–2018 (2013)

  5. 5.

    Koomey, J.G.: Worldwide electricity used in data centers. Environ. Res. Lett. 3, 4008 (2008)

    Article  Google Scholar 

  6. 6.

    Garimella, S.V., Persoons, T., Weibel, J., Yeh, L.T.: Technological drivers in data centers and telecom systems: Multiscale thermal, electrical, and energy management. Appl. Energy 107, 66–80 (2013)

    Article  Google Scholar 

  7. 7.

    Andrae, A., Edler, T.: On global electricity usage of communication technology: trends to 2030. Challenges 6, 117–157 (2015)

    Article  Google Scholar 

  8. 8.

    Mittal, S.: Power management techniques for data centers: a survey. arXiv preprint http://arxiv.org/abs/1404.6681 (2014)

  9. 9.

    Rong, H., Zhang, H., Xiao, S., Li, C., Hu, C.: Optimizing energy consumption for data centers. Renew. Sustain. Energy Rev. 58, 674–691 (2016)

    Article  Google Scholar 

  10. 10.

    Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18, 732–794 (2016)

    Article  Google Scholar 

  11. 11.

    Borah, A.D., Muchahary, D., Singh, S.K., Borah, J.: Power saving strategies in green cloud computing systems. Int. J. Grid Distrib. Comput. 8, 299–306 (2015)

    Article  Google Scholar 

  12. 12.

    Shuja, J., et al.: Survey of techniques and architectures for designing energy-efficient data centers. IEEE Syst. J. 10, 507–519 (2016)

    Article  Google Scholar 

  13. 13.

    Guitart, J.: Toward sustainable data centers: a comprehensive energy management strategy. Computing 99, 597–615 (2016)

    MathSciNet  Article  Google Scholar 

  14. 14.

    Giridhar, B., et al.: Exploring DRAM organizations for energy-efficient and resilient exascale memories. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC (2013). https://doi.org/10.1145/2503210.2503215

  15. 15.

    Li, Z., Greenan, K.M., Leung, A.W., Zadok, E.: Power consumption in enterprise-scale backup storage systems. In: Proceedings of FAST 2012: 10th USENIX Conference on File and Storage Technologies, pp. 65–71 (2012)

  16. 16.

    Koomey, J.G.: Growth in Data Center Electricity Use 2005 to 2010. (2011)

  17. 17.

    Cherry, S.: Edholm’s law of bandwidth. IEEE Spectr. 41, 58–60 (2003)

    Article  Google Scholar 

  18. 18.

    Barroso, L.A., Hölzle, U., Ranganathan, P.: The datacenter as a computer: Designing Warehouse-Scale Machines: Third edition. Synthesis Lectures on Communication Networks, Vol. 13, pp. 1–189 (2018)

  19. 19.

    Malladi, K.T., et al.: Towards energy-proportional datacenter memory with mobile DRAM. In: Proceedings—International Symposium on Computer Architecture, pp. 37–48 (2012). https://doi.org/10.1109/ISCA.2012.6237004

  20. 20.

    Fargo, F., Franza, O., Tunc, C., Hariri, S.: Autonomic resource management for power, performance, and security in cloud environment. In: Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA (2019)

  21. 21.

    Abd-El-Atty, B., Iliyasu, A.M., Alaskar, H., El-Latif, A.A.A.: A robust quasi-quantum walks-based steganography protocol for secure transmission of images on cloud-based E-healthcare platforms. Sensors 20, 3108 (2020)

    Article  Google Scholar 

  22. 22.

    Attia, K.M., El-Hosseini, M.A., Ali, H.A.: Dynamic power management techniques in multi-core architectures: a survey study. Ain Shams Eng. J. 8, 445–456 (2017)

    Article  Google Scholar 

  23. 23.

    Pudukotai Dinakarrao, S.M.: Self-aware power management for multi-core microprocessors. Sustain. Comput. Inform. Syst. 29(1), 1480 (2021). https://doi.org/10.1016/j.suscom.2020.100480

    Article  Google Scholar 

  24. 24.

    Dorronsoro, B., et al.: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustain. Comput. Inf. Syst. 4, 252–261 (2014)

    Google Scholar 

  25. 25.

    Johari, S., Kumar, A.: Algorithmic approach for applying load balancing during task migration in multi-core system. In: Proceedings of 2014 3rd International Conference on Parallel, Distributed and Grid Computing, pp. 27–32 (2015). https://doi.org/10.1109/PDGC.2014.7030710

  26. 26.

    Mann, Z.A.: Multicore-aware virtual machine placement in cloud data centers. IEEE Trans. Comput. 65, 3357–3369 (2016)

    MathSciNet  Article  Google Scholar 

  27. 27.

    LKML: Peter Zijlstra: On numa interfaces and stuff. https://lkml.org/lkml/2011/11/17/204. Accessed 26 Jan 2021

  28. 28.

    LKML: Andrea Arcangeli: [PATCH 00/39] [RFC] AutoNUMA alpha10. https://lkml.org/lkml/2012/3/26/398. Accessed 26 Jan 2021

  29. 29.

    Chen, Y.-L., Chang, M.-F., Yu, C.-W., Chen, X.-Z., Liang, W.-Y.: Learning-directed dynamic voltage and frequency scaling scheme with adjustable performance for single-core and multi-core embedded and mobile systems. Sensors 18, 3068 (2018)

    Article  Google Scholar 

  30. 30.

    Ukidave, Y., Li, X., Kaeli, D.: Mystic: Predictive scheduling for GPU based cloud servers using machine learning. In: Proceedings—2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS, pp. 353–362 (2016). :https://doi.org/10.1109/IPDPS.2016.73

  31. 31.

    Xu, Z., Dong, F., Jin, J., Luo, J., Shen, J.: GScheduler: Optimizing resource provision by using GPU usage pattern extraction in cloud environments. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3225–3230 (2017)

  32. 32.

    Mishra, A., Khare, N.: Analysis of DVFS techniques for improving the GPU energy efficiency. Open J. Energy Effic. 4, 77–86 (2015)

    Article  Google Scholar 

  33. 33.

    Jararweh, Y., Hariri, S.: Power and performance management of GPUs based cluster. Int. J. Cloud Appl. Comput. 2, 16–31 (2012)

    Google Scholar 

  34. 34.

    Tang, Z., Wang, Y., Wang, Q., Chu, X.: The impact of GPU DVFS on the energy and performance of deep Learning: An Empirical Study. e-Energy 2019—Proceedings of the 10th ACM International Conference on Future Energy Systems, pp. 315–325 (2019). https://doi.org/10.1145/3307772.3328315

  35. 35.

    Fujita, S. et al.: Novel memory hierarchy with e-STT-MRAM for near-future applications. In: 2017 International Symposium on VLSI Technology, Systems and Application, pp. 3–4 (2017). https://doi.org/10.1109/VLSI-TSA.2017.7942444

  36. 36.

    Sakamoto, M., Yamaguchi, S.: Dynamic memory allocation in virtual machines based on cache hit ratio. In: Proceedings—2015 3rd International Symposium on Computing and Networking, pp. 613–615 (2016). https://doi.org/10.1109/CANDAR.2015.34

  37. 37.

    Chen, L., Dai, W., Qiu, M.: A greedy approach for caching in distributed data stores. In: Proceedings—2nd IEEE International Conference on Smart Cloud, pp. 244–249 (2017). https://doi.org/10.1109/SmartCloud.2017.46

  38. 38.

    Blankstein, A., et al.: Hyperbolic caching: flexible caching for web applications. In: Proceedings of the 2017 USENIX Annual Technical Conference (2017)

  39. 39.

    Yang, H., Yan, X.: Memory coherency based CPU-Cache-FPGA acceleration architecture for cloud computing. In: Proceedings—2015 2nd International Conference on Information Science and Control Engineering, pp. 304–307 (2015). https://doi.org/10.1109/ICISCE.2015.74

  40. 40.

    Han, H., et al.: Cashing in on the cache in the cloud. IEEE Trans. Parallel Distrib. Syst. 23, 1387–1399 (2012)

    Article  Google Scholar 

  41. 41.

    Kitagata, D., Yamamoto, S., Sugahara, S.: Design and energy-efficient architectures for nonvolatile static random access memory using magnetic tunnel junctions. Jpn. J. Appl. Phys. 58, SBB12 (2019)

    Article  Google Scholar 

  42. 42.

    Bazzi, H., Harb, A., Aziza, H., Moreau, M.: Design of hybrid CMOS non-volatile SRAM cells in 130 nm RRAM technology. In: Proceedings of the International Conference on Microelectronics, pp. 228–231 (2018)

  43. 43.

    Venkatesan, V., Tay, Y.C., Zhang, Y.I., Wei, Q.: A 3-level cache miss model for a nonvolatile extension to transcendent memory. In: Proceedings of the International Conference on Cloud Computing Technology and Science, pp. 218–225 (2015)

  44. 44.

    Qiu, M., Ming, Z., Li, J., Gai, K., Zong, Z.: Phase-change memory optimization for green cloud with genetic algorithm. IEEE Trans. Comput. 64, 3528–3540 (2015)

    MathSciNet  MATH  Article  Google Scholar 

  45. 45.

    Wang, J., Wang, B.: A hybrid main memory applied in virtualization environments. In: 2016 1st IEEE International Conference on Computer Communication and the Internet, pp. 413–417 (2016). https://doi.org/10.1109/CCI.2016.7778955

  46. 46.

    He, J., Callenes-Sloan, J.: Optimizing energy in a DRAM based hybrid cache. In: Proceedings—International Symposium on Quality Electronic Design. pp. 37–42 (2018)

  47. 47.

    Gurumurthi, S., Sivasubramaniam, A.: Energy-efficient storage systems for data centers. In: Energy-Efficient Distributed Computing Systems, pp. 361–376 (Wiley, New York, 2012). https://doi.org/10.1002/9781118342015.ch13.

  48. 48.

    Zhu, Q., et al.: Hibernator: helping disk arrays sleep through the winter. In: Proceedings of the 20th ACM Symposium on Operating Systems Principles, pp. 177–190 (2005). https://doi.org/10.1145/1095810.1095828

  49. 49.

    Tomes, E., Altiparmak, N.: A comparative study of HDD and SSD RAIDs’ impact on server energy consumption. In; Proceedings—IEEE International Conference on Cluster Computing, pp. 625–626 (2017)

  50. 50.

    Pinheiro, E., Bianchini, R.: Energy conservation techniques for disk array-based servers. In: Proceedings of the International Conference on Supercomputing, pp. 68–78 (2004). https://doi.org/10.1145/1006209.1006220

  51. 51.

    Srikantaiah, S., Kansal, A. & Zhao, F.: Energy aware consolidation for cloud computing. Workshop on Power Aware Computing and Systems. HotPower (2008)

  52. 52.

    Mohseni, Z., Kiani, V., Masoud Rahmani, A.: A task scheduling model for multi-CPU and multi-hard disk drive in soft real-time systems. Int. J. Inf. Technol. Comput. Sci. 1, 1–13 (2019)

    Google Scholar 

  53. 53.

    Wu, W., Xia, W., Yu, Z., Liu, Q.: Exploring the potential of coupled array of SSD and HDD for multi-Tenant. In: 2018 3rd IEEE International Conference on Cloud Computing and Big Data Analysis, pp. 653–657 (2018). https://doi.org/10.1109/ICCCBDA.2018.8386596

  54. 54.

    Gao, Y., Zhang, H., Zhu, Y., Tang, B., Ma, H.: A load-aware data migration scheme for distributed surveillance video processing with hybrid storage architecture. In: Proceedings—2017 IEEE 19th Intl Conference on High Performance Computing and Communications, 2017 IEEE 15th Intl Conference on Smart City and 2017 IEEE 3rd Intl Conference on Data Science and Systems, pp. 563–570 (2018)

  55. 55.

    Tan, W., Fong, L., Liu, Y.: Effectiveness assessment of solid-state drive used in big data services. In: Proceedings—2014 IEEE International Conference on Web Services, pp. 393–400 (2014). https://doi.org/10.1109/ICWS.2014.63

  56. 56.

    Yin, S. et al.: DuoFS: A hybrid storage system balancing energy-efficiency, reliability, and performance. In: Proceedings—26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 478–485 (2018). https://doi.org/10.1109/PDP2018.2018.00082

  57. 57.

    Mishra, S.K., Sahoo, B., Parida, P.P.: Load balancing in cloud computing: a big picture. J. King Saud Univ. Comput. Inf. Sci. 32, 149–158 (2020)

    Article  Google Scholar 

  58. 58.

    Choudhary, A., et al.: Improved virtual machine migration approaches in cloud environment. In: Proceedings—2016 IEEE International Conference on Cloud Computing in Emerging Markets, pp. 17–24 (2017). https://doi.org/10.1109/CCEM.2016.013

  59. 59.

    Younge, A.J., von Laszewski, G., Wang, L., Lopez-Alarcon, S., Carithers, W.: Efficient resource management for cloud computing environments. In: 2010 International Conference on Green Computing, Green Comp 2010, pp. 357–364 (2010). https://doi.org/10.1109/GREENCOMP.2010.5598294

  60. 60.

    Pecero, J.E., et al.: On the energy optimization for precedence constrained applications using local search algorithms. In: Proceedings of the 2012 International Conference on High Performance Computing and Simulation, pp. 133–139 (2012). https://doi.org/10.1109/HPCSim.2012.6266902

  61. 61.

    Tucker, R.S.: Green optical communications-Part II: energy limitations in networks. IEEE J. Sel. Top. Quantum Electron. 17, 261–274 (2011)

    Article  Google Scholar 

  62. 62.

    Kliazovich, D., Bouvry, P., Khan, S.U.: DENS: Data center energy-efficient network-aware scheduling. In: Proceedings - 2010 IEEE/ACM International Conference on Green Computing and Communications, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, pp. 69–75 (2010). https://doi.org/10.1109/GreenCom-CPSCom.2010.31

  63. 63.

    Chiesa, M., Kindler, G., Schapira, M.: Traffic engineering with equal-cost-multipath: An algorithmic perspective. IEEE/ACM Trans. Netw. 25, 779–792 (2017)

    Article  Google Scholar 

  64. 64.

    Widjaja, I., Walid, A., Luo, Y., Xu, Y., Chao, H.J.: Small versus large: Switch sizing in topology design of energy-efficient data centers. In: IEEE International Workshop on Quality of Service, pp. 51–56 (2013). https://doi.org/10.1109/IWQoS.2013.6550264

  65. 65.

    Chkirbene, Z., Gouissem, A., Hadjidj, R., Foufou, S., Hamila, R.: Efficient techniques for energy saving in data center networks. Comput. Commun. 129, 111–124 (2018)

    Article  Google Scholar 

  66. 66.

    Yan, F., Calabretta, N., Xue, X.: HiFOST: a scalable and low-latency hybrid data center network architecture based on flow-controlled fast optical switches. J. Opt. Commun. Netw. 10, B1–B14 (2018)

    Article  Google Scholar 

  67. 67.

    Terzi, C., Korpeoglu, I.: 60 GHz wireless data center networks: a survey. Comput. Netw. 185, 1730 (2021)

    Article  Google Scholar 

  68. 68.

    Heller, B., et al.: Elastictree: Saving energy in data center networks. In: Proceedings of NSDI 2010: 7th USENIX Symposium on Networked Systems Design and Implementation, pp. 249–264 (2010)

  69. 69.

    Li, D., Shang, Y., Chen, C.: Software defined green data center network with exclusive routing. In: IEEE Conference on Computer Communications, pp. 1743–1751 (2014). https://doi.org/10.1109/INFOCOM.2014.6848112

  70. 70.

    Wang, N., Ho, K.H., Pavlou, G.: AMPLE: An adaptive traffic engineering system based on virtual routing topologies. IEEE Commun. Mag. 50, 185–191 (2012)

    Article  Google Scholar 

  71. 71.

    Kliazovich, D., Arzo, S. T., Granelli, F., Bouvry, P. & Khan, S. U.: e-STAB: Energy-efficient scheduling for cloud computing applications with traffic load balancing. Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. 7–13 (2013) doi:https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.28

  72. 72.

    Guzek, M., Kliazovich, D., Bouvry, P.: HEROS: Energy-Efficient Load Balancing for Heterogeneous Data Centers. In: Proceedings—2015 IEEE 8th International Conference on Cloud Computing, pp. 742–749 (2015). https://doi.org/10.1109/CLOUD.2015.103

  73. 73.

    Niccolini, L., Iannaccone, G., Ratnasamy, S., Chandrashekar, J., Rizzo, L.: Building a power-proportional software router. In: Proceedings of the 2012 USENIX Annual Technical Conference, pp. 89–100 (2019)

  74. 74.

    Mahadevan, P., Banerjee, S., Sharma, P.: Energy proportionality of an enterprise network. In: Proceedings of the 1st ACM SIGCOMM Workshop on Green Networking, Green Networking ’10, pp. 53–59 (2010). https://doi.org/10.1145/1851290.1851302

  75. 75.

    Ahn, J. & Park, H. S.: Measurement and modeling the power consumption of router interface. International Conference on Advanced Communication Technology. 860–863 (2014) doi:https://doi.org/10.1109/ICACT.2014.6779082

  76. 76.

    Abts, D., Marty, M.R., Wells, P.M., Klausler, P., Liu, H.: Energy proportional datacenter networks. In: Proceedings—International Symposium on Computer Architecture, pp. 338–347 (2010). https://doi.org/10.1145/1815961.1816004

  77. 77.

    Abd El-Latif, A.A., et al.: Secret images transfer in cloud system based on investigating quantum walks in steganography approaches. Physica A 541, 1233687 (2020)

    MathSciNet  Article  Google Scholar 

  78. 78.

    Dreibholz, T., Becke, M. & Adhari, H.: Report to Congress on Server and Data Center Energy Efficiency Public Law 109–431 (2007)

  79. 79.

    Greenberg, A., Hamilton, J., Maltz, D., Patel, P.: The cost of a cloud: research problems in data center networks. ACM SIGCOMM - Computer Communication Review. 39, 68–73 (2008)

    Article  Google Scholar 

  80. 80.

    Patel, C. & Ranganathan, P.: Enterprise power and cooling. ASPLOS Tutorial (2006)

  81. 81.

    Hepburn, A.: Facebook statistics, stats & facts for 2011. Digital Buzz (2011)

  82. 82.

    Feng, W.C., Cameron, K.: The green500 list: encouraging sustainable supercomputing. Computer 40, 50–55 (2007)

    Article  Google Scholar 

  83. 83.

    Greenberg, S., Mills, E., Tschudi, B., Berkeley, L.: Best Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers T. In: ACEEE Summer, pp. 76–87 (2006)

  84. 84.

    Mittal, S.: A survey of techniques for improving energy efficiency in embedded computing systems. Int. J. Comput. Aided Eng. Technol. 6, 440–459 (2014)

    Article  Google Scholar 

  85. 85.

    Barroso, L.A., Hölzle, U., Ranganathan, P.: The Datacenter as Computer, 3rd Edn (2018)

  86. 86.

    Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer 40, 33–37 (2007)

    Article  Google Scholar 

  87. 87.

    Ranganathan, P., Leech, P., Irwin, D. & Chase, J.: Ensemble-level power management for dense blade servers. Proceedings - International Symposium on Computer Architecture. 66–77 (2006)

  88. 88.

    Anderson, D., Dykes, J. & Riedel, E.: More than an interface—SCSI vs . ATA. In: 2nd Annual Conference on File and Storage Technology. (2003)

  89. 89.

    Mittal, S.: A survey of architectural techniques for improving cache power efficiency. Sustain. Comput. Inform. Syst. 4, 33–43 (2014)

    Google Scholar 

  90. 90.

    Deng, Q., Ramos, L., Bianchini, R., Meisner, D., Wenisch, T.: Active low-power modes for main memory with MemScale. IEEE Micro 32, 60–69 (2012)

    Article  Google Scholar 

  91. 91.

    Hsu, C.H., Feng, W.C.: A power-aware run-time system for high-performance computing. In: Proceedings of the ACM/IEEE 2005 Supercomputing Conference, pp. 1–1 (2005)

  92. 92.

    Song, B., Ernemann, G., Yahyapour, R.: Parallel computer workload modeling with Markov chains. Lect. Notes Comput. Sci. 3277, 47–62 (2005)

    Article  Google Scholar 

  93. 93.

    Khamse-Ashari, J., Lambadaris, I., Kesidis, G., Urgaonkar, B., Zhao, Y.: A cost-aware fair allocation mechanism for multi-resource servers. IEEE Netw. Lett. 1, 34–37 (2019)

    Article  Google Scholar 

  94. 94.

    Chen, F., Koufaty, D.A., Zhang, X.: Understanding intrinsic characteristics and system implications of flash memory based solid state drives. In: SIGMETRICS/Performance’09 - Proceedings of the 11th International Joint Conference on Measurement and Modeling of Computer Systems, Vol. 37, pp 181–192 (2009)

  95. 95.

    GitHub-Mellanox/DCTrafficGen: Data Center Traffic Generator Library. https://github.com/Mellanox/DCTrafficGen.

  96. 96.

    Tolentino, M.E., Turner, J., Cameron, K.W.: Memory miser: Improving main memory energy efficiency in servers. IEEE Trans. Comput. 58, 336–350 (2009)

    MathSciNet  Article  Google Scholar 

  97. 97.

    Liu, S., Pattabiraman, K., Moscibroda, T., Benjamin, G.Z.: Flikker: saving DRAM refresh-power through critical data partitioning. ACM SIGPLAN Notices 46, 213–224 (2011)

    Article  Google Scholar 

  98. 98.

    Isen, C. & John, L.: ESKIMO: Energy savings using semantic knowledge of inconsequential memory occupancy for DRAM subsystem. Proceedings of the Annual International Symposium on Microarchitecture. MICRO 337–346 (2009) doi:https://doi.org/10.1145/1669112.1669156

  99. 99.

    Ayoub, R., Indukuri, K. R. & Rosing, T. S.: Energy efficient proactive thermal management in memory subsystem. In: Proceedings of the International Symposium on Low Power Electronics and Design, pp 195–200 (2010) https://doi.org/10.1145/1840845.1840884

  100. 100.

    Lin, J., et al.: Software thermal management of dram memory for multicore systems. ACM Sigmetrics Perform Eval Rev 36, 337–348 (2008)

    Article  Google Scholar 

  101. 101.

    Bittman, D., et al.: Designing data structures to minimize bit flips on NVM. In: Proceedings—7th IEEE Non-Volatile Memory Systems and Applications Symposium, pp. 85–90 (2018) https://doi.org/10.1109/NVMSA.2018.00022

  102. 102.

    Zhang, W.Z., et al.: Secure and Optimized Load Balancing for Multitier IoT and Edge-Cloud Computing Systems. IEEE Internet Things J. 8, 8119–8132 (2021)

    Article  Google Scholar 

  103. 103.

    Tan, T. K., Raghunathan, A., Jha, N.K.: Software architectural transformations: a new approach to low energy embedded software. In: Proceedings—Design, Automation and Test in Europe, pp 1046–1051 (2003). https://doi.org/10.1109/DATE.2003.1253742

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by AK, SD and TC. The first draft of the manuscript was written by AK and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Avita Katal.

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Katal, A., Dahiya, S. & Choudhury, T. Energy efficiency in cloud computing data center: a survey on hardware technologies. Cluster Comput (2021). https://doi.org/10.1007/s10586-021-03431-z

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Keywords

  • Server
  • CPU utilization
  • Memory
  • VM migration
  • Workload categorization
  • Energy efficiency