ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Resource-efficient load-balancing framework for cloud data center networks

  • Kumar, Jitendra (Department of Computer Applications, National Institute of Technology) ;
  • Singh, Ashutosh Kumar (Department of Computer Applications, National Institute of Technology) ;
  • Mohan, Anand (Department of Electronics Engineering, Indian Institute of Technology, Banaras Hindu University)
  • Received : 2019.06.12
  • Accepted : 2020.01.20
  • Published : 2021.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

Keywords

Acknowledgement

This research was supported by the Ministry of Electronics & Information Technology (MeitY), Government of India.

References

  1. Navigant Consulting Inc. SAIC, Analysis and Representation of Miscellaneous Electric Loads in NEMS, prepared for the U.S. Energy Information Administration, Navigant Reference: 160750, 2017, pp. 1-138.
  2. J. Kumar and A. K. Singh, Cloud datacenter workload estimation using error preventive time series forecasting models, Cluster Comput (2019), in press.
  3. R. Birke et al., Data Centers in the Wild: A Large Performance Study, Tech. report, IBM Research - Zurich, Switzerland, 2012.
  4. C. Reiss et al., Heterogeneity and dynamicity of clouds at scale: google trace analysis, in Proc. ACM Symp. Cloud Comput. (San Jose, CA, USA), Oct., 2012, pp. 1-18.
  5. L. Barroso, J. Clidaras, and U. Holzle, The Datacenter as a Computer An Introduction to the Design of Warehouse-Scale Machines, 2 ed, Morgan & Claypool Publishers, 2013.
  6. J. Kumar and A. K. Singh, Workload prediction in cloud using artificial neural network and adaptive differential evolution, Future Generation Comput. Syst. 81 (2018), 41-52. https://doi.org/10.1016/j.future.2017.10.047
  7. J. Kumar, R. Goomer, and A. K. Singh, Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters, Procedia Comput. Sci. 125 (2018), 676-682. https://doi.org/10.1016/j.procs.2017.12.087
  8. P. D. Bharathi, P. Prakash, and M. V. K. Kiran, Virtual machine placement strategies in cloud computing, in Proc. Innovations Power Adv. Comput. Technol. (Vellore, India), Apr. 2017, pp. 1-7.
  9. A. C. Adamuthe, R. M. Pandharpatte, and G. T. Thampi, Multiobjective virtual machine placement in cloud environment, in Proc. Int. Conf. Cloud Ubiquitous Comput. Emerg. Technol. (Pune, India), Nov. 2013, pp. 8-13.
  10. T. Ferreto, C. A. F. De Rose, and H. Heiss, Maximum migration time guarantees in dynamic server consolidation for virtualized data centers, E Jeannot, R Namyst, and J Roman (eds), Euro-Par 2011 Parallel Processing, Springer Berlin Heidelberg: Berlin, Heidelberg, 2011, pp. 443-454.
  11. S. Shigeta et al., Design and implementation of a multi-objective optimization mechanism for virtual machine placement in cloud computing data center, M. Yousif and L. Schubert (eds), Cloud Computing (Cham), Springer International Publishing, 2013, pp. 21-31.
  12. J. Xu and J. A. B. Fortes, Multi-objective virtual machine placement in virtualized data center environments, in Proc. IEEE/ACM Int. Conf. Green Comput. Commun. Int. Conf. Cyber, Phys. Social Comput. (Hangzhou, China), Dec. 2010, pp. 179-188.
  13. F. Tseng et al., Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm, IEEE Syst J. 12 (2018), no. 2, 1688-1699. https://doi.org/10.1109/jsyst.2017.2722476
  14. N. Gunantara, A review of multi-objective optimization: methods and its applications, Cogent Eng. 5 (2018), no. 1, 1502242:1-16. https://doi.org/10.1080/23311916.2018.1502242
  15. R. T. Marler and J. S. Arora, The weighted sum method for multiobjective optimization: new insights, Structural Multidisciplinary Optimization 41 (2010), no. 6, 853-862. https://doi.org/10.1007/s00158-009-0460-7
  16. F. Fang and B. B. Qu, Multi-objective virtual machine placement for load balancing, ITM Web Conf.: Int. Conf. Inf. Sci. Technol. 11 (2017), 01011:1-9.
  17. J. Kumar and A. K. Singh, Cloud resource demand prediction using differential evolution based learning, in Proc. Int. Conf. Smart Comput. Commun. (Sarawak, Malaysia), June 2019, pp. 1-5.
  18. J. Zhang et al., Load balancing in data center networks: a survey, IEEE Commun. Surveys Tutorials 20 (2018), no. 3, 2324-2352. https://doi.org/10.1109/COMST.2018.2816042
  19. J. Kumar and A. K. Singh, Dynamic resource scaling in cloud using neural network and black hole algorithm, in Proc. Int. Conf. Eco-friendly Comput. Commun. Syst. (Bhopal, India), Dec 2016, pp. 63-67.
  20. I. Cuadrado-Cordero, A. Orgerie, and C. Morin, GRaNADA: A network-aware and energy-efficient PaaS cloud architecture, in Proc. IEEE Int. Conf. Data Sci. Data Intensive Syst. (Sydney, Australia), Dec. 2015, pp. 412-419.
  21. J. Kumar and A. K. Singh, An efficient machine learning approach for virtual machine resource demand prediction, Int. J. Adv. Sci. Technol. 123 (2019), 21-30. https://doi.org/10.33832/ijast.2019.123.03
  22. I. De Falco et al., Effective processor load balancing using multi-objective parallel extremal optimization, in Proc. Genetic Evolutionary Comput. Conf. Companion (New York, NY, USA), July 2018, pp. 1292-1299.
  23. Z. A. Mann, Multicore-aware virtual machine placement in cloud data centers, IEEE Trans. Comput. 65 (2016), no. 11, 3357-3369. https://doi.org/10.1109/TC.2016.2529629
  24. F. Ramezani et al., A multi-objective load balancing system for cloud environments, Comput. J. 60 (2017), no. 9, 1316-1337. https://doi.org/10.1093/comjnl/bxw109
  25. F. L. Pires and B. Baran, A virtual machine placement taxonomy, in Proc. IEEE/ACM Int. Symp. Cluster, Cloud Grid Comput. (Shenzhen, China), May 2015, pp. 159-168.
  26. M. Gabay and S. Zaourar, Vector bin packing with heterogeneous bins: application to the machine reassignment problem, Ann. Oper. Res. 242 (2016), no. 1, 161-194. https://doi.org/10.1007/s10479-015-1973-7
  27. P. Silva, C. Perez, and F. Desprez, Efficient heuristics for placing large-scale distributed applications on multiple clouds, in Proc. IEEE/ACM Int. Symp. Cluster, Cloud Grid Comput. (Cartagena, Colombia), May 2016, pp. 483-492.
  28. A. Marotta and S. Avallone, A simulated annealing based approach for power efficient virtual machines consolidation, in Proc. IEEE Int. Conf. Cloud Comput. (New York, USA), June 2015, pp. 445-452.
  29. Y. Yu and Y. Gao, Constraint programming-based virtual machines placement algorithm in datacenter, Z. Shi, D. Leake, and S. Vadera (eds), Intelligent Information Processing VI, Springer Berlin Heidelberg: Berlin, Heidelberg, 2012, pp. 295-304.
  30. L. Zhang, Y. Zhuang, and W. Zhu, Constraint programming based virtual cloud resources allocation model, Int. J. Hybrid Inf. Technol. 6 (2013), no. 6, 333-344. https://doi.org/10.14257/ijhit.2013.6.6.30
  31. W. Song et al., Adaptive resource provisioning for the cloud using online bin packing, IEEE Trans. Comput. 63 (2014), no. 11, 2647-2660. https://doi.org/10.1109/TC.2013.148
  32. Y. Zhang and N. Ansari, Heterogeneity aware dominant resource assistant heuristics for virtual machine consolidation, in Proc. IEEE Global Commun. Conf. (Atlanta, GA, USA), Dec. 2013, pp. 1297-1302.
  33. C. Lin, P. Liu, and J. Wu, Energy-efficient virtual machine provision algorithms for cloud systems, in Proc. IEEE Int. Conf. Utility Cloud Comput. (Victoria, Australia), Dec. 2011, pp. 81-88.
  34. H. Mi et al., Online self-configuration with performance guarantee for energy-efficient large-scale cloud computing data centers, in Proc. IEEE Int. Conf. Services Comput. (Miami, FL, USA), July 2010, pp. 514-521.
  35. Md H Ferdaus et al., Virtual machine consolidation in cloud data centers using ACO metaheuristic, F. Silva, I. Dutra, and V. S. Costa (eds), Euro-Par 2014 Parallel Processing, Springer International Publishing, Porto, 2014, pp. 306-317.
  36. Q. Zheng et al., Multi-objective optimization algorithm based on bbo for virtual machine consolidation problem, in Proc. IEEE Int. Conf. Parallel Distr. Syst. (Melbourne, Australia), Dec. 2015, pp. 414-421.
  37. M. Tang and S. Pan, A hybrid genetic algorithm for the energy efficient virtual machine placement problem in data centers, Neural Process. Lett. 41 (2015), no. 2, 211-221. https://doi.org/10.1007/s11063-014-9339-8
  38. Y. Gao et al., A multi-objective ant colony system algorithm for virtual machine placement in cloud computing, J. Comput. Syst. Sci. 79 (2013), no. 8, 1230-1242. https://doi.org/10.1016/j.jcss.2013.02.004
  39. F. Alharbi et al., An ant colony system for energy-efficient dynamic virtual machine placement in data centers, Expert Syst. Appl. 120 (2019), 228-238. https://doi.org/10.1016/j.eswa.2018.11.029
  40. N. Sharma and R. M. Guddeti, Multi-objective energy efficient virtual machines allocation at the cloud data center, IEEE Trans. Serv. Comput. 12 (2018), no. 1, 158-171. https://doi.org/10.1109/tsc.2016.2596289
  41. M. Dabbagh et al., Energyefficient resource allocation and provisioning framework for cloud data centers, IEEE Trans. Netw. Serv. Manage. 12 (2015), no. 3, 377-391. https://doi.org/10.1109/TNSM.2015.2436408
  42. X. Zhang et al., Energy-aware virtual machine allocation for cloud with resource reservation, J. Syst. Softw. 147 (2019), 147-161. https://doi.org/10.1016/j.jss.2018.09.084
  43. Z. Xiao, W. Song, and Q. Chen, Dynamic resource allocation using virtual machines for cloud computing environment, IEEE Trans. Parallel Distrib. Syst. 24 (2013), no. 6, 1107-1117. https://doi.org/10.1109/TPDS.2012.283
  44. S. Chhabra and A. K. Singh, Dynamic hierarchical load balancing model for cloud data centre networks, Electron. Lett. 55 (2019), 94-96. https://doi.org/10.1049/el.2018.5427
  45. Y. Zhang, J. Yao, and H. Guan, Intelligent cloud resource management with deep reinforcement learning, IEEE Cloud Comput. 4 (2017), no. 6, 60-69. https://doi.org/10.1109/mcc.2018.1081063
  46. Z. Li et al., An exploration of designing a hybrid scale-up/out hadoop architecture based on performance measurements, IEEE Trans. Parallel Distrib. Syst. 28 (2017), no. 2, 386-400. https://doi.org/10.1109/TPDS.2016.2573820
  47. N. J. Kansal and I. Chana, Energy-aware virtual machine migration for cloud computing - a firefly optimization approach, J. Grid Comput. 14 (2016), no. 2, 327-345. https://doi.org/10.1007/s10723-016-9364-0
  48. M. Bala and D. Padha, An adaptive overload detection policy based on the estimator sn in cloud environment, Int. J. Serv. Sci. Manag. Eng. Technol. 8 (2017), no. 3, 93-107. https://doi.org/10.4018/IJSSMET.2017070106
  49. D. M. Quan et al., T-alloc: A practical energy efficient resource allocation algorithm for traditional data centers, Future Generation Comput. Syst. 28 (2012), no. 5, 791-800. https://doi.org/10.1016/j.future.2011.04.020
  50. W. Yan, J. Chen, and L. Li, A power-aware aco algorithm for the cloud computing platform, in Proc. Int. Conf. Commun. Inf. Process. (New York, NY, USA), Nov. 2018, pp. 1-6.
  51. E. Arianyan, H. Taheri, and S. Sharifian, Novel heuristics for consolidation of virtual machines in cloud data centers using multi-criteria resource management solutions, J. Supercomput. 72 (2016), no. 2, 688-717. https://doi.org/10.1007/s11227-015-1603-9
  52. N. Akhter and M. Othman, Energy aware resource allocation of cloud data center: review and open issues, Cluster Comput. 19 (2016), no. 3, 1163-1182. https://doi.org/10.1007/s10586-016-0579-4
  53. L. Minas and B. Ellison, Energy efficiency for information technology: How to reduce power consumption in servers and data centers, Intel Press, 2009.
  54. X. Li et al., Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center, Math. Comput. Modelling 58 (2013), no. 5, 1222-1235. https://doi.org/10.1016/j.mcm.2013.02.003
  55. F. Farahnakian et al., Using ant colony system to consolidate vms for green cloud computing, IEEE Trans. Serv. Comput. 8 (2015), no. 2, 187-198. https://doi.org/10.1109/TSC.2014.2382555
  56. SPECpower, Accessed: 2019-12-18 [http://www.spec.org/power_ssj2008/results/res2011q1/power_ssj2008-20110124-00339.html].
  57. SPECpower, Accessed: 2019-12-18 [http://www.spec.org/power_ssj2008/results/res2011q2/power_ssj2008-20110406-00368.html].
  58. SPECpower, Accessed: 2019-12-18 [http://www.spec.org/power_ssj2008/results/res2010q4/power_ssj2008-20101001-00297.html].
  59. A. K. Singh and J. Kumar, Secure and energy aware load balancing framework for cloud data centre networks, Electron Lett. 55 (2019), 540-541. https://doi.org/10.1049/el.2019.0022

Cited by

  1. Fuzzy-Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach vol.2021, 2021, https://doi.org/10.1155/2021/5539186
  2. Combined use of coral reefs optimization and reinforcement learning for improving resource utilization and load balancing in cloud environments vol.103, pp.7, 2021, https://doi.org/10.1007/s00607-021-00920-2