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Energy-driven cloud simulation: existing surveys, simulation supports, impacts and challenges

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Abstract

A large scale cloud data center is needed to provision various applications in different domains. As a result, power consumption is expected to increase due to huge operations and expansion of cloud data centers. Furthermore, it also intensifies environment concern. Various approaches and solutions for energy-driven cloud data center have been proposed to overcome this challenge. Testing and evaluating these solutions in large scale is costly and time consuming. Hence, simulation techniques become the preferred approach to tackle this concern. There are a few cloud simulators have been developed with different features and capabilities which can be chosen for this reason. A survey work can serve as a guideline. A few cloud simulation surveys have been done but limited survey is found for energy-driven cloud simulation. This review complements the existing surveys by considering different aspects of energy-driven cloud simulators. Therefore, this paper presents a review of existing cloud simulation surveys with several classifications. Furthermore, it provides some insights of the selected cloud simulators by emphasizing on the energy-driven simulation supports and the impact of the cloud simulators in succeeding works. This paper also highlights open and future challenges.

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References

  1. 1.

    Abu Sharkh, M., Kanso, A., Shami, A., Öhlén, P.: Building a cloud on earth: a study of cloud computing data center simulators. Comput. Netw. 108, 78–96 (2016). https://doi.org/10.1016/J.COMNET.2016.06.037

  2. 2.

    Ahmed, A., Sabyasachi, A.S.: Cloud computing simulators: a detailed survey and future direction, pp. 866–872. In: IEEE (2014)

  3. 3.

    Bak, S., Krystek, M., Kurowski, K., Oleksiak, A., Piatek, W., Waglarz, J.: GSSIM—a tool for distributed computing experiments. Sci. Prog. 19(4), 231–251 (2011). https://doi.org/10.3233/SPR-2011-0332

  4. 4.

    Belady, C., Rawson, A., Pfleuger, J., Tahir Cader, D.: Green Grid Data Center Power Efficiency Metrics: PUE and DCiE. Technical Report. The Green Grid, The Green Grid, Beaverton (2008)

  5. 5.

    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Fut. Gener. Comput. Syst. 28(5), 755–768 (2012). https://doi.org/10.1016/j.future.2011.04.017

  6. 6.

    Bilal, K., Malik, S.U.R., Khalid, O., Hameed, A., Alvarez, E., Wijaysekara, V., Irfan, R., Shrestha, S., Dwivedy, D., Ali, M., Shahid-Khan, U., Abbas, A., Jalil, N., Khan, S.U.: A taxonomy and survey on green data center networks. Fut. Gener. Comput. Syst. 36, 189–208 (2014). https://doi.org/10.1016/J.FUTURE.2013.07.006

  7. 7.

    Bilal, K., Malik, S.U.R., Khan, S.U., Zomaya, A.Y.: Trends and challenges in cloud datacenters. IEEE Cloud Comput. 1(1), 10–20 (2014). https://doi.org/10.1109/MCC.2014.26

  8. 8.

    Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., Zomaya, A.Y.: Energy-efficient data replication in cloud computing datacenters. Clust. Comput. 18(1), 385–402 (2015). https://doi.org/10.1007/s10586-014-0404-x

  9. 9.

    Buyya, R., Calheiros, R., Li, X.: Autonomic cloud computing: open challenges and architectural elements. In: Emerging Applications of 2012 (2012)

  10. 10.

    Byrne, J., Svorobej, S., Giannoutakis, K.M., Tzovaras, D., Byrne, P.J., Östberg, P.O., Gourinovitch, A., Lynn, T.: A review of cloud computing simulation platforms and related environments. In: Proceedings of the 7th International Conference on Cloud Computing and Services Science (2017). https://doi.org/10.5220/0006373006790691

  11. 11.

    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

  12. 12.

    Calheiros, R.N., Ranjan, R., De Rose, C.A.F., Buyya, R.: Cloudsim: a novel framework for modeling and simulation of cloud computing infrastructures and services. arXiv preprint. arXiv:0903.2525 (2009)

  13. 13.

    Camus, B., Dufossé, F., Orgerie, A.C.: A stochastic approach for optimizing green energy consumption in distributed clouds. https://hal.inria.fr/hal-01475431/ (2017)

  14. 14.

    Castañé, G.G., Núñez, A., Llopis, P., Carretero, J.: E-mc2: A formal framework for energy modelling in cloud computing. Simul. Model. Pract. Theory 39, 56–75 (2013). https://doi.org/10.1016/J.SIMPAT.2013.05.002

  15. 15.

    Cotes-Ruiz, I.T., Prado, R.P., García-Galán, S., Muñoz-Expósito, J.E., Ruiz-Reyes, N.: Dynamic voltage frequency scaling simulator for real workflows energy-aware management in green cloud computing. PLoS ONE 12(1), e0169803 (2017). https://doi.org/10.1371/journal.pone.0169803

  16. 16.

    Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016). https://doi.org/10.1109/COMST.2015.2481183

  17. 17.

    Fiandrino, C., Kliazovich, D., Bouvry, P., Zomaya, A.Y.: Performance and energy efficiency metrics for communication systems of cloud computing data centers. IEEE Trans. Cloud Comput. 5(4), 738–750 (2017). https://doi.org/10.1109/TCC.2015.2424892

  18. 18.

    Filho, M.C.S., Oliveira, R.L., Monteiro, C.C., Inacio, P.R.M., Freire, M.M.: CloudSim Plus: a cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 400–406. IEEE (2017). https://doi.org/10.23919/INM.2017.7987304

  19. 19.

    Garraghan, P., Al-Anii, Y., Summers, J., Thompson, H., Kapur, N., Djemame, K.: A unified model for holistic power usage in cloud datacenter servers. In: Proceedings of the 9th International Conference on Utility and Cloud Computing—UCC ’16, pp. 11–19. ACM Press, New York (2016). https://doi.org/10.1145/2996890.2996896, http://dl.acm.org/citation.cfm?doid=2996890.2996896

  20. 20.

    Gill, S.S., Chana, I., Singh, M., Buyya, R.: RADAR: Self-configuring and self-healing in resource management for enhancing quality of cloud services. Concurr. Comput. Pract. Exp. 31(1), e4834 (2019). https://doi.org/10.1002/cpe.4834

  21. 21.

    Guérout, T., Monteil, T., Da Costa, G., Neves Calheiros, R., Buyya, R., Alexandru, M.: Energy-aware simulation with DVFS. Simul. Model. Pract. Theory 39, 76–91 (2013). https://doi.org/10.1016/J.SIMPAT.2013.04.007

  22. 22.

    Gupta, S.K., Gilbert, R.R., Banerjee, A., Abbasi, Z., Mukherjee, T., Varsamopoulos, G.: GDCSim: a tool for analyzing green data center design and resource management techniques. In: 2011 International Green Computing Conference and Workshops, IGCC 2011 (2011). https://doi.org/10.1109/IGCC.2011.6008612

  23. 23.

    Gupta, S.K.S., Banerjee, A., Abbasi, Z., Varsamopoulos, G., Jonas, M., Ferguson, J., Gilbert, R.R., Mukherjee, T.: GDCSim: a simulator for green data center design and analysis. ACM Trans. Model. Comput. Simul. 24(1), 1–27 (2014). https://doi.org/10.1145/2553083

  24. 24.

    Horvath, T., Abdelzaher, T., Skadron, K., Liu, X.: Dynamic voltage scaling in multitier web servers with end-to-end delay control. IEEE Trans. Comput. 56(4), 444–458 (2007). https://doi.org/10.1109/TC.2007.1003

  25. 25.

    Ismail, A., Jamaludin, N.A., Zambri, S.: A review of energy-aware cloud computing surveys. TELKOMNIKA 16(6), 2740 (2018). https://doi.org/10.12928/telkomnika.v16i6.9938

  26. 26.

    Jing, S.Y., Ali, S., She, K., Zhong, Y.: State-of-the-art research study for green cloud computing. J. Supercomput. 65(1), 445–468 (2013). https://doi.org/10.1007/s11227-011-0722-1

  27. 27.

    Kecskemeti, G.: DISSECT-CF: a simulator to foster energy-aware scheduling in infrastructure clouds. Simul. Model. Pract. Theory 58, 188–218 (2015). https://doi.org/10.1016/J.SIMPAT.2015.05.009

  28. 28.

    Kecskemeti, G., Hajji, W., Tso, F.P.: Modelling low power compute clusters for cloud simulation. In: 2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 39–45. IEEE (2017). https://doi.org/10.1109/PDP.2017.33, http://ieeexplore.ieee.org/document/7912623/

  29. 29.

    Kliazovich, D., Bouvry, P., Khan, S.U.: GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J. Supercomput. 62(3), 1263–1283 (2012). https://doi.org/10.1007/s11227-010-0504-1

  30. 30.

    Kolpe, T., Zhai, A., Sapatnekar, S.S.: Enabling improved power management in multicore processors through clustered DVFS. In: 2011 Design, Automation & Test in Europe, pp. 1–6. IEEE (2011). https://doi.org/10.1109/DATE.2011.5763052, http://ieeexplore.ieee.org/document/5763052/

  31. 31.

    Kurowski, K., Oleksiak, A., Piatek, W., Piontek, T., Przybyszewski, A., Wȩglarz, J.: DCworms—a tool for simulation of energy efficiency in distributed computing infrastructures. Simul. Model. Pract. Theory 39, 135–151 (2013). https://doi.org/10.1016/J.SIMPAT.2013.08.007

  32. 32.

    Li, X., Garraghan, P., Jiang, X., Wu, Z., Xu, J.: Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. IEEE Trans. Parallel Distrib. Syst. 29(6), 1317–1331 (2018). https://doi.org/10.1109/TPDS.2017.2688445

  33. 33.

    Li, X., Jiang, X., Garraghan, P., Wu, Z.: Holistic energy and failure aware workload scheduling in cloud datacenters. Fut. Gener. Comput. Syst. 78, 887–900 (2018). https://doi.org/10.1016/J.FUTURE.2017.07.044

  34. 34.

    Li, S., Li-Shiuan, P., Jha, N.: Dynamic voltage scaling with links for power optimization of interconnection networks. In: The Ninth International Symposium on High-Performance Computer Architecture, 2003, HPCA-9 2003. Proceedings, pp. 91–102. IEEE Computer Society (2003). https://doi.org/10.1109/HPCA.2003.1183527, http://ieeexplore.ieee.org/document/1183527/

  35. 35.

    Lim, S.H., Sharma, B., Nam, G., Kim, E.K., Das, C.R.: MDCSim: a multi-tier data center simulation, platform. In: 2009 IEEE International Conference on Cluster Computing and Workshops, pp. 1–9. IEEE (2009). https://doi.org/10.1109/CLUSTR.2009.5289159, http://ieeexplore.ieee.org/document/5289159/

  36. 36.

    Liu, J., Zhao, F., Liu, X., He, W.: Challenges towards elastic power management in internet data centers. In: 2009 29th IEEE International Conference on Distributed Computing Systems Workshops, pp. 65–72. IEEE (2009). https://doi.org/10.1109/ICDCSW.2009.44, http://ieeexplore.ieee.org/document/5158835/

  37. 37.

    Louis, B., Mitra, K., Saguna, S., Åhlund, C.: CloudSimDisk: energy-aware storage simulation in CloudSim. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp. 11–15 (2015). https://doi.org/10.1109/UCC.2015.15

  38. 38.

    Lynn, T., Gourinovitch, A., Byrne, J., Byrne, P.J., Svorobej, S., Giannoutakis, K., Kenny, D., Morrison, J.: A preliminary systematic review of computer science literature on cloud computing research using open source simulation platforms. In: Proceedings of the 7th International Conference on Cloud Computing and Services Science—CLOSER, vol. 1, pp. 565–573. SciTePress (2017). https://doi.org/10.5220/0006351805650573

  39. 39.

    Makaratzis, A.T., Giannoutakis, K.M., Tzovaras, D.: Energy modeling in cloud simulation frameworks. Fut. Gener. Comput. Syst. 79, 715–725 (2018). https://doi.org/10.1016/J.FUTURE.2017.06.016

  40. 40.

    Malik, A.W., Bilal, K., Aziz, K., Kliazovich, D., Ghani, N., Khan, S.U., Buyya, R.: CloudNetSim++: a toolkit for data center simulations in OMNET++. In: 2014 11th Annual High Capacity Optical Networks and Emerging/Enabling Technologies (Photonics for Energy), pp. 104–108. IEEE (2014). https://doi.org/10.1109/HONET.2014.7029371, http://ieeexplore.ieee.org/document/7029371/

  41. 41.

    Malik, A.W., Bilal, K., Malik, S.U., Anwar, Z., Aziz, K., Kliazovich, D., Ghani, N., Khan, S.U., Buyya, R.: CloudNetSim++: a GUI based framework for modeling and simulation of data centers in OMNeT++. IEEE Trans. Serv. Comput. 10(4), 506–519 (2017). https://doi.org/10.1109/TSC.2015.2496164

  42. 42.

    Mann, Z.î: Cloud simulators in the implementation and evaluation of virtual machine placement algorithms. Softw. Pract. Exp. (2018). https://doi.org/10.1002/spe.2579

  43. 43.

    Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.M., Vasilakos, A.V.: Cloud computing: survey on energy efficiency. ACM Comput. Surv. 47(2), 1–36 (2014). https://doi.org/10.1145/2656204

  44. 44.

    Mayer, P., Klarl, A., Hennicker, R., Puviani, M., Tiezzi, F., Pugliese, R., Keznikl, J., Bure, T.: The autonomic cloud: a vision of voluntary, peer-2-peer cloud computing. In: 2013 IEEE 7th International Conference on Self-Adaptation and Self-Organizing Systems Workshops, pp. 89–94. IEEE (2013). https://doi.org/10.1109/SASOW.2013.16

  45. 45.

    Mayer, P., Velasco, J., Klarl, A., Hennicker, R., Puviani, M., Tiezzi, F., Pugliese, R., Keznikl, J., Bureš, T.: The autonomic cloud. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems: The ASCENS Approach, pp. 495–512. Springer, Cham (2015)

  46. 46.

    Meisner, D., Gold, B.T., Wenisch, T.F., Meisner, D., Gold, B.T., Wenisch, T.F., Meisner, D., Gold, B.T., Wenisch, T.F.: PowerNap. In: Proceeding of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems—ASPLOS ’09, vol. 44, p. 205. ACM Press, New York (2009). https://doi.org/10.1145/1508244.1508269, http://portal.acm.org/citation.cfm?doid=1508244.1508269

  47. 47.

    Meisner, D., Sadler, C.M., Barroso, L.A., Weber, W.D., Wenisch, T.F., Meisner, D., Sadler, C.M., Barroso, L.A., Weber, W.D., Wenisch, T.F.: Power management of online data-intensive services. ACM SIGARCH Comput. Arch. News 39(3), 319 (2011). https://doi.org/10.1145/2024723.2000103

  48. 48.

    Meisner, D., Wu, J., Wenisch, T.F.: Towards a scalable data center-level evaluation methodology. In: (IEEE ISPASS) IEEE International Symposium on Performance Analysis of Systems and Software, pp. 121–122. IEEE (2011). https://doi.org/10.1109/ISPASS.2011.5762723, http://ieeexplore.ieee.org/document/5762723/

  49. 49.

    Meisner, D., Wu, J., Wenisch, T.F.: BigHouse: A simulation infrastructure for data center systems. In: 2012 IEEE International Symposium on Performance Analysis of Systems & Software, pp. 35–45. IEEE (2012). https://doi.org/10.1109/ISPASS.2012.6189204, http://ieeexplore.ieee.org/document/6189204/

  50. 50.

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

  51. 51.

    Núñez, A., Vázquez-Poletti, J.L., Caminero, A.C., Castañé, G.G., Carretero, J., Llorente, I.M.: iCanCloud: a flexible and scalable cloud infrastructure simulator. J Grid Comput. 10(1), 185–209 (2012). https://doi.org/10.1007/s10723-012-9208-5

  52. 52.

    Olukotun, K., Nayfeh, B.A., Hammond, L., Wilson, K., Chang, K., Olukotun, K., Nayfeh, B.A., Hammond, L., Wilson, K., Chang, K., Olukotun, K., Nayfeh, B.A., Hammond, L., Wilson, K., Chang, K.: The case for a single-chip multiprocessor. In: Proceedings of the Seventh International Conference on Architectural Support for Programming Languages and Operating Systems—ASPLOS-VII, vol. 31, pp. 2–11. ACM Press, New York (1996). https://doi.org/10.1145/237090.237140, http://portal.acm.org/citation.cfm?doid=237090.237140

  53. 53.

    Patterson, M., Tschudi, B., Vangeet, O., Cooley, J., Azevedo, D.: ERE: A Metric for Measuring the Benefit of Reuse Energy from a Data Center. Technical Report. The Green Grid, Beaverton (2010)

  54. 54.

    Piraghaj, S.F., Dastjerdi, A.V., Calheiros, R.N., Buyya, R.: ContainerCloudSim: an environment for modeling and simulation of containers in cloud data centers. Softw. Pract. Exp. 47(4), 505–521 (2016). https://doi.org/10.1002/spe.2422

  55. 55.

    Reddy, V.D., Setz, B., Rao, G.S.V., Gangadharan, G., Aiello, M.: Metrics for sustainable data centers. IEEE Trans. Sustain. Comput. 2(3), 290–303 (2017). https://doi.org/10.1109/TSUSC.2017.2701883

  56. 56.

    Sakellari, G., Loukas, G.: A survey of mathematical models, simulation approaches and testbeds used for research in cloud computing. Simul. Model. Pract. Theory (2013). https://doi.org/10.1016/j.simpat.2013.04.002

  57. 57.

    Schödwell, B., Erek, K., Zarnekow, R.: Data center green performance measurement: state of the art and open research challenges. In: AMCIS 2013 Proceedings (2013)

  58. 58.

    Sebastio, S., Amoretti, M., Lafuente, A.L.: AVOCLOUDY: a simulator of volunteer clouds. Softw. Pract. Exp. 46(1), 3–30 (2016). https://doi.org/10.1002/spe.2345

  59. 59.

    Sharma, Y., Si, W., Sun, D., Javadi, B.: Failure-aware energy-efficient VM consolidation in cloud computing systems. Fut. Gener. Comput. Syst. 94, 620–633 (2019). https://doi.org/10.1016/J.FUTURE.2018.11.052

  60. 60.

    Singh, S., Chana, I.: EARTH: Energy-aware autonomic resource scheduling in cloud computing. J. Intell. Fuzzy Syst. 30(3), 1581–1600 (2016). https://doi.org/10.3233/IFS-151866

  61. 61.

    Singh, S., Chana, I., Singh, M., Buyya, R.: SOCCER: self-optimization of energy-efficient cloud resources. Clust. Comput. 19(4), 1787–1800 (2016). https://doi.org/10.1007/s10586-016-0623-4

  62. 62.

    Tian, W., Xu, M., Chen, A., Li, G., Wang, X., Chen, Y.: Open-source simulators for cloud computing: comparative study and challenging issues. Simul. Model. Pract. Theory 58, 239–254 (2015). https://doi.org/10.1016/J.SIMPAT.2015.06.002

  63. 63.

    Tian, W., Zhao, Y., Xu, M., Zhong, Y., Sun, X.: A Toolkit for Modeling and simulation of real-time virtual machine allocation in a cloud data center. IEEE Trans. Autom. Sci. Eng. 12(1), 153–161 (2015). https://doi.org/10.1109/TASE.2013.2266338

  64. 64.

    Tighe, M., Keller, G., Bauer, M., Lutfiyya, H.: DCSim: A data centre simulation tool for evaluating dynamic virtualized resource management. In: 2012 8th International Conference on Network and Service Management (CNSM) and 2012 Workshop on Systems Virtualiztion Management (SVM), pp. 385–392 (2012)

  65. 65.

    Tighe, M., Keller, G., Shamy, J., Bauer, M., Lutfiyya, H.: Towards an improved data centre simulation with DCSim. In: Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013), pp. 364–372. IEEE (2013). https://doi.org/10.1109/CNSM.2013.6727859, http://ieeexplore.ieee.org/document/6727859/

  66. 66.

    The Network Simulator—ns-2. https://www.isi.edu/nsnam/ns/

  67. 67.

    Tso, F.P., White, D.R., Jouet, S., Singer, J., Pezaros, D.P.: The Glasgow Raspberry Pi cloud: a scale model for cloud computing infrastructures. In: 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops, pp. 108–112. IEEE (2013). https://doi.org/10.1109/ICDCSW.2013.25, http://ieeexplore.ieee.org/document/6679872/

  68. 68.

    Wang, L., Khan, S.U.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomput. 63(3), 639–656 (2013). https://doi.org/10.1007/s11227-011-0704-3

  69. 69.

    Wang, S., Liu, J., Chen, J.J., Liu, X.: PowerSleep: a smart power-saving scheme with sleep for servers under response time constraint. IEEE J. Emerg. Select. Top. Circuits Syst. 1(3), 289–298 (2011). https://doi.org/10.1109/JETCAS.2011.2167532

  70. 70.

    Wolf, W., Jerraya, A., Martin, G.: Multiprocessor system-on-chip (MPSoC) technology. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 27(10), 1701–1713 (2008). https://doi.org/10.1109/TCAD.2008.923415

  71. 71.

    Wong, D.: Peak efficiency aware scheduling for highly energy proportional servers. In: 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), pp. 481–492. IEEE (2016). https://doi.org/10.1109/ISCA.2016.49, http://ieeexplore.ieee.org/document/7551416/

  72. 72.

    Xiang, L., Xiaohong, J., Kejiang, Y., Peng, H.: DartCSim+: enhanced CloudSim with the power and network models integrated. In: 2013 IEEE Sixth International Conference on Cloud Computing, pp. 644–651. IEEE (2013). https://doi.org/10.1109/CLOUD.2013.53

  73. 73.

    Zakarya, M., Gillam, L.: Energy efficient computing, clusters, grids and clouds: a taxonomy and survey. Sustain. Comput. Inform. Syst. 14, 13–33 (2017). https://doi.org/10.1016/J.SUSCOM.2017.03.002

  74. 74.

    Zhao, W., Peng, Y., Xie, F., Dai, Z.: Modeling and simulation of cloud computing: a review. In: 2012 IEEE Asia Pacific Cloud Computing Congress (APCloudCC), pp. 20–24. IEEE (2012). https://doi.org/10.1109/APCloudCC.2012.6486505, http://ieeexplore.ieee.org/document/6486505/

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Acknowledgements

This research has been supported by a Research Grant, 600-IRMI/ PERDANA 5/3 BESTARI (048/2018), funded by Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.

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Correspondence to Azlan Ismail.

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Ismail, A. Energy-driven cloud simulation: existing surveys, simulation supports, impacts and challenges. Cluster Comput (2020). https://doi.org/10.1007/s10586-020-03068-4

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Keywords

  • Cloud simulator
  • Energy efficiency
  • Energy metrics
  • Power management