Acknowledgement
This research was supported by the Ministry of Electronics & Information Technology (MeitY), Government of India.
References
- 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.
- J. Kumar and A. K. Singh, Cloud datacenter workload estimation using error preventive time series forecasting models, Cluster Comput (2019), in press.
- R. Birke et al., Data Centers in the Wild: A Large Performance Study, Tech. report, IBM Research - Zurich, Switzerland, 2012.
- 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.
- 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.
- 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
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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.
- 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.
- 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
- 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.
- 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.
- 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
- 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.
- 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
- 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
- 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.
- 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
- 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.
- 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.
- 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.
- 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
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- L. Minas and B. Ellison, Energy efficiency for information technology: How to reduce power consumption in servers and data centers, Intel Press, 2009.
- 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
- 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
- SPECpower, Accessed: 2019-12-18 [http://www.spec.org/power_ssj2008/results/res2011q1/power_ssj2008-20110124-00339.html].
- SPECpower, Accessed: 2019-12-18 [http://www.spec.org/power_ssj2008/results/res2011q2/power_ssj2008-20110406-00368.html].
- SPECpower, Accessed: 2019-12-18 [http://www.spec.org/power_ssj2008/results/res2010q4/power_ssj2008-20101001-00297.html].
- 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
- Fuzzy-Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach vol.2021, 2021, https://doi.org/10.1155/2021/5539186
- 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