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A novel virtual machine placement algorithm using RF element in cloud infrastructure

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Abstract

Finding the best approach for virtual machine placement (VMP) in cloud infrastructure is one of the most important optimization problems. The obtained solution of this problem significantly impacts on costs, energy, performance, etc. Physical machine (PM) processing capacity and virtual machine (VM) workloads have played important roles in VMP. Besides, in recent years with the increasingly development of semiconductors industry, fabricated chips including multiple homogeneous or heterogeneous processing elements (PEs) are of interest. The latest produced chip contains several general-purpose cores side by side with reconfigurable fabrics (RF) which have been used for accelerated computing and performing on par with ASIC hardware. In this paper a methodology is proposed to design VMP algorithms using arbitrary PEs. Moreover, a novel algorithm to address VMP problem using RF elements in cloud infrastructure is proposed. The methodology includes discovering, evaluation environment, models, parameters extraction, limitations, adaptation, problem formulation and heuristic. Among those, parameters extraction has a critical role in the overall performance. The extracted parameters are employed to make decision about which PM is more appropriate for hosting the desired VM. According to simulation results on synthetic workloads our proposed VMP algorithm outperforms others in operation with our proposed cloud architecture model.

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References

  1. Ferdman M, Adileh A, Kocberber O, Volos S, Alisafaee M, Jevdjic D, Kaynak C, Popescu AD, Ailamaki A, Falsafi B (2014) A case for specialized processors for scale-out workloads. IEEE Micro 34(3):31–42

    Article  Google Scholar 

  2. The Xilinx SDAccel development environment: bringing the best performance/watt to the data center. Tech. Rep, 2015

  3. Yanovskaya O, Yanovsky M, Kharchenko V (2014) The concept of green cloud infrastructure based on distributed computing and hardware accelerator within FPGA as a Service. In: Proceedings of IEEE East-West Design & Test Symposium (EWDTS 2014), pp 1–4

  4. Magaki I, Khazraee M, Gutierrez LV, Taylor MB (2016) ASIC clouds: specializing the datacenter. In: ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), pp 178–190

  5. Putnam A, Caulfield AM, Chung ES, Chiou D, Constantinides K, Demme J, Esmaeilzadeh H, Fowers J, Gopal GP, Gray J, Haselman M, Hauck S, Heil S, Hormati A, Kim JY, Lanka S, Larus J, Peterson E, Pope S, Smith A, Thong J, Xiao PH, Burger D (2015) A reconfigurable fabric for accelerating large-scale datacenter services. IEEE Micro 35(3):10–22

    Article  Google Scholar 

  6. Crago S, Dunn K, Eads P, Hochstein L, Kang D, Kang M, Modium D, Singh K, Suh J, Walters JP (2011) Heterogeneous Cloud Computing. In: IEEE International Conference on Cluster Computing, pp 378–385

  7. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  8. Anand A, Lakshmi J, Nandy SK (2013) Virtual machine placement optimization supporting performance SLAs. In: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, pp 298–305

  9. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28(5):755–768

    Article  Google Scholar 

  10. Buyya R, Yeo CS, Venugopal S (2008) Market-oriented cloud computing: vision, hype, and reality for delivering it services as computing utilities. In: 10th IEEE International Conference on High Performance Computing and Communications (HPCC’08), pp 5–13

  11. Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput Pract Exp 24:1397–1420

    Article  Google Scholar 

  12. Salimian L, Safi F (2013) Survey of energy efficient data centers in cloud computing. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. IEEE Computer Society, pp 369–374

  13. Lopez Pires F, Bar´an B (2014) Virtual machine placement´ literature review. Polytechnic School, National University of Asuncion, Tech. Rep. https://sites.google.com/site/flopezpires/. Accessed May 2015

  14. Han G, Que W, Jia G, Shu L (2016) Jara A (2016) An efficient virtual machine consolidation scheme for multimedia cloud computing. Sensors 16(2):246

    Article  Google Scholar 

  15. Zhao L, Lu L, Jin Z, Yu C (2017) Online virtual machine placement for increasing cloud provider’s revenue. IEEE Trans Serv Comput 10(2):273–285

    Article  Google Scholar 

  16. Hao F, Kodialam M, Lakshman TV, Mukherjee S (2017) Online allocation of virtual machines in a distributed cloud. IEEE/ACM Trans Netw 25(1):238–249

    Article  Google Scholar 

  17. Zhao H, Wang J, Liu F, Wang Q, Zhang W, Zheng Q (2018) Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Trans Parallel Distrib Syst 29(6):1385–1400

    Article  Google Scholar 

  18. Rahmani S, Khajehvand V, Torabian M (2020) Burstiness-aware virtual machine placement in cloud computing systems. J Supercomput 76:362–387

    Article  Google Scholar 

  19. Pell O, Mencer O, Tsoi K. H, Luk W (2013) Maximum performance computing with dataflow engines. In: High-Performance Computing Using FPGAs. Springer, Berlin, pp 747–774

  20. Grigoras P, Tottenham M, Niu X, Coutinho JGF, Luk W (2014) Elastic management of reconfigurable accelerators. In: IEEE International Symposium on Parallel and Distributed Processing with Applications, pp 174–181

  21. Eguro K, Venkatesan R (2012) FPGAs for trusted cloud computing. In: 22nd International Conference on Field Programmable Logic and Applications (FPL), pp 63–70

  22. Francisco P (2011) The Netezza data appliance architecture: a platform for high performance data warehousing and analytics. IBM Redbooks, 2011

  23. Ouyang J, Lin S, Qi W, Wang Y, Yu B, Jiang S (2014) SDA: software-defined accelerator for large-scale DNN systems. In: 2014 IEEE Hot Chips 26 Symposium (HCS), pp 1–23

  24. Byma S, Steffan JG, Bannazadeh H, Garcia AL, Chow P (2014) FPGAs in the cloud: booting virtualized hardware accelerators with OpenStack. In: IEEE 22nd Annual International Symposium on Field-Programmable Custom Computing Machines, pp 109–116

  25. Hauswald J, Laurenzano MA, Zhang Y, Li C, Rovinski A, Khurana A, Dreslinski RG, Mudge T, Petrucci V, Tang L, Mars J (2015) Sirius: an open end-to-end voice and vision personal assistant and its implications for future warehouse scale computers. In: Proceedings of the 20nd International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) pp 223–238

  26. Jacobsen M, Freund Y, Kastner R (2012) RIFFA: a reusable integration framework for FPGA accelerators. In: IEEE 20th International Symposium on Field-Programmable Custom Computing Machines, pp 216–219

  27. Vipin K, Fahmy SA (2014) DyRACT: a partial reconfiguration enabled accelerator and test platform. In: 24th International Conference on Field Programmable Logic and Applications (FPL), pp 1–7

  28. Fahmy SA, Vipin K, Shreejith S (2015) Virtualized FPGA accelerators for efficient cloud computing. In: IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp 430–435

  29. Knodel O, Spallek RG (2015) Computing framework for dynamic integration of reconfigurable resources in a cloud. In: Euromicro Conference on Digital System Design, pp 337–344

  30. Beloglazov A (2013) Energy-efficient management of virtual machines in data centers for cloud computing. Ph.D. dissertation, The University of Melbourne

  31. Varasteh A, Goudarzi M (2017) Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J 11(2):772–783

    Article  Google Scholar 

  32. Kachris C, Soudris D (2016) A survey on reconfigurable accelerators for cloud computing. In: 26th International Conference on Field Programmable Logic and Applications (FPL), pp 1–10

  33. Wang H, Tianfield H (2018) Energy-aware dynamic virtual machine consolidation for cloud datacenters. IEEE Access 6:15259–15273

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Seyed Milad Farzaneh & Omid Fatemi

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  1. Seyed Milad Farzaneh
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  2. Omid Fatemi
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Correspondence to Omid Fatemi.

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Farzaneh, S.M., Fatemi, O. A novel virtual machine placement algorithm using RF element in cloud infrastructure. J Supercomput 78, 1288–1329 (2022). https://doi.org/10.1007/s11227-021-03863-9

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  • DOI: https://doi.org/10.1007/s11227-021-03863-9

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