Abstract
The placement of a virtual machine in cloud computing generates a cost derived from consuming the energy of the allocated network elements. In this paper, we present an optimization model for effective virtual machine placement in the heterogeneous multi-cloud systems by considering peak demand time and geographical position of allocated resources, with target of minimizing the energy cost of allocated network elements. We also build a dynamic energy model for cloud physical machines and communication components. Then, we propose a correlation aware virtual machine placement algorithm, namely MGGAVP, with these issues in mind. The algorithm is based on the hybridization of the Grouping Genetic Algorithm and Hill-climbing and extended for the multi-cloud environment. The results of simulation reveal that the proposed algorithm can have significantly better performance than the three comparison algorithms with the energy saving of 51.93% average performance promotion and energy cost of 70.41% average performance promotion.
Similar content being viewed by others
References
Panda, S.K., Gupta, I., Jana, P.K.: Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Inf. Syst. Front. (2017). https://doi.org/10.1007/s10796-017-9742-6
Durao, F., Carvalho, J.F.S., Fonseka, A., Garcia, V.C.: A systematic review on cloud computing. J. Supercomput. 68, 1321–1346 (2015)
Zhu, J., Li, D., Wu, J., Liu, H., Zhang, Y., Zhang, J.: Towards bandwidth guarantee in multi-tenancy cloud computing networks. In: Proceedings of the IEEE International Conference on Network Protocols (2012). https://doi.org/10.1109/icnp.2012.6459986
Panda, S.K., Pande, S.K., Das, S.: Task partitioning scheduling algorithms for heterogeneous multi-cloud environment. Arab. J. Sci. Eng. (2018). https://doi.org/10.1007/s13369-017-2798-2
Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72, 666–677 (2012)
Assis, M., Bittencourt, L.: A survey on cloud federation architectures: identifying functional and non-functional properties. J. Netw. Comput. Appl. (2016). https://doi.org/10.1016/j.jnca.2016.06.014
Toosi, A.N., Calheiros, R.N., Buyya, R.: Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput. Surv. (2014). https://doi.org/10.1145/2593512
Heilig, L., Buyya, R., Voß, S.: Location-aware brokering for consumers in multi-cloud computing environments. J. Netw. Comput. Appl. (2017). https://doi.org/10.1016/j.jnca.2017.07.010
Chun, S., Choi, B.: Service models and pricing schemes for cloud computing. Cluster Comput. (2013). https://doi.org/10.1007/s10586-013-0296-1
Gelazanskas, L., Gamage, A.A.K.: Demand side management in smart grid: a review and proposals for future direction. Sustain. Cities Soc. 11, 22–30 (2014)
Kostková, K., Omelina, L., Kycina, P., Jamrich, P.: An introduction to load management. Electric Power Syst. Res. 95, 184–191 (2013)
Bergaentzle, C., Clastres, C., Khalfallah, N.: Demand-side management and European environmental and energy goals: an optimal complementary approach. Energy Policy 67, 858–869 (2014)
Gupta, M.K., Tarachand, A.: Resource-aware virtual machine placement algorithm for IaaS cloud. J Supercomput. 74, 122–140 (2018)
Jamali, S., Malektaji, S.: Improving grouping genetic algorithm for virtual machine placement in cloud data centers. In: Proceedings of the 4th International Conference on Computer and Knowledge Engineering (2014). https://doi.org/10.1109/iccke.2014.6993461
Ferdaus, H., Murshed, M., Buyya, R., Calheiros, R.N.: Virtual machine consolidation in cloud data centers using ACO metaheuristic. In: Proceedings of the Euro-Par 2014 parallel processing (2014). https://doi.org/10.1007/978-3-319-09873-9_26
Tang, Z., Mo, Y., Li, K., Li, K.: Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing. J Supercomput. (2014). https://doi.org/10.1007/s11227-014-1227-5
Li, W., Tordsson, J., Elmroth, E.: Virtual machine placement for predictable and time constraint peak loads. In: Proceedings of the International Workshop on Grid Economics and Business Models (2012). https://doi.org/10.1007/978-3-642-28675-9_9
Agrawal, S., Bose, S.K., Sundarrajan, S.: Grouping genetic algorithm for solving the server consolidation problem with conflicts. Genet. Evolut. Comput. (2009). https://doi.org/10.1145/1543834.1543836
Dupont, C., Giuliani, G., Hermenier, F., Schulze, T., Somov, A.: An energy aware framework for virtual machine placement in cloud federated data centers. Future Energy Syst. (2012). https://doi.org/10.1145/2208828.2208832
Fang, W., Liang, X., Li, S., Chiaraviglio, L., Xiong, N.: VMPlanner: optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers. Comput. Netw. (2013). https://doi.org/10.1016/j.comnet.2012.09.008
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. 25, 256 (2011). https://doi.org/10.1002/cpe.1867
Beloglazov, A., Buyya, R.: Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of the Middleware for Grids, Clouds and e-Science, ACM (2010). https://doi.org/10.1145/1890799.1890803
Li, X.K., Gut, C.H., Yang, Z.P., Chang, Y.H.: Virtual machine placement strategy based on discrete firefly algorithm in cloud environments. In: Proceedings of the Wavelet Active Media Technology and Information Processing (2015). https://doi.org/10.1109/iccwamtip.2015.7493907
Xu, G., Dong, Y., Fu, X.: VMs placement strategy based on distributed parallel ant colony optimization algorithm. Appl. Math. 9, 873–881 (2015)
Hogade, N., Siegel, H.J.: Minimizing energy costs for geographically distributed heterogeneous datac. In: Proceedings of the IEEE Transactions on Sustainable Computing (2018). https://doi.org/10.1109/tsusc.2018.2822674
Zhao, J., Ding, Y., Xu, G.: A location selection policy of live virtual machine migration for power saving and load balancing. Sci. World J. (2013). https://doi.org/10.1155/2013/492615
Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79, 1230–1242 (2013)
Suseel, B.B.J., Jeyakrishnan, V.: A multi-objective hybrid ACO-PSO optimization algorithm for virtual machine placement in cloud computing. J. Res. Eng. Technol. (2014). https://doi.org/10.15623/ijret.2014.0304084
Wang, B., Song, Y., Cui, X., Cao, J.: Mathematical programming for server consolidation in cloud data centers. In: Proceedings of the 4th International Conference on Systems and Informatics (2017). http://doi.org/10.1109/ICSAI.2017.8248374
Xu, C., Wang, K., Li, P., Xia, R., Guo, S., Guo, M.: Renewable energy-aware big data analytics in geo-distributed data centers with reinforcement learning. In: Proceedings of the IEEE Transactions on Network Science and Engineering (2018). http://doi.org/10.1109/TNSE.2018.2813333
Xu, J., Fortes, J.A.B.: Multi-objective virtual machine placement in virtualized data center environments. Green Comput. Commun. (2010). https://doi.org/10.1109/GreenCom-CPSCom.2010.137
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28, 755–768 (2012)
Li, X., Li, X., Qian, Z., Lu, S., Wu, J.: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math. Comput. Model. (2013). https://doi.org/10.1016/j.mcm.2013.02.003
Zahedi Fard, S.Y., Ahmadi, M.R., Adabi, S.: A dynamic VM consolidation technique for QoS and energy consumption in cloud environment. J Supercomput. (2017). https://doi.org/10.1007/s11227-017-2016-8
Zhao, J., Wu, C., Li, Z.: Cost minimization in multiple IaaS clouds: a double auction approach. Comput. Sci. Game Theor. (2013). http://arxiv.org/abs/1308.0841. Accessed 8 Dec 2013
Gu, L., Zeng, D., Barnawi, A., Guo, S., Stojmenovic, I.: Optimal task placement with QoS constraints in geo-distributed data centers using DVFS. IEEE Trans. Comput. 64, 2049–2059 (2015)
Nazir, B.: QoS aware VM placement and migration for hybrid cloud infrastructure. J. Supercomput. (2018). https://doi.org/10.1007/s11227-017-2071-1
Li, H., Wu, C., Li, Z., Zhang, Z., Lau, F.C.M.: Dynamic pricing and profit maximization for the cloud with geo-distributed data centers. In: Proceedings of the IEEE Conference on Computer Communications (2014). https://doi.org/10.1109/infocom.2014.6847931
Dalvandi, A., Gurusamy, M., Chua, K.: Time-aware VMFlow placement, routing and migration for power efficiency in data centers. IEEE Trans. Netw. Serv. Manage. 12, 349–362 (2015)
Simarro, J., Vozmediano, R., Montero, R. Llorente, I.: Dynamic placement of virtual machines for cost optimization in multi-cloud environments. In: Proceedings of the International Conference on High Performance Computing & Simulation (2011). https://doi.org/10.1109/hpcsim.2011.5999800
Khosravi, A., Buyya, R.: Energy and carbon footprint aware management of geo-distributed cloud data centers: a taxonomy, state of art, and future directions. Sustain. Dev. (2017). https://doi.org/10.4018/978-1-5225-2013-9.ch002
Silva, P., Perez, C.: An efficient communication aware heuristic for multiple cloud application placement. In: Proceedings of the European Conference on Parallel Processing (2017). https://doi.org/10.1007/978-3-319-64203-1_27
Kumar, K.S.S., Jaisankar, N.: Towards data centre resource scheduling via hybrid cuckoo search algorithm in multi-cloud environment. Int. J. Intell. Enterp. (2017). https://doi.org/10.1504/ijie.2017.087008
Liu, F., Luo, F., Niu, Y.: Cost-effective service provisioning for hybrid cloud applications. Mob. Netw. Appl. (2017). https://doi.org/10.1007/s11036-016-0738-0
Zhu, W., Zhuang, Y., Zhang, L.: A three-dimensional virtual resource scheduling method for energy saving in cloud computing. Future Gener. Comput. Syst. (2017). https://doi.org/10.1016/j.future.2016.10.034
Lin, W., Wang, W., Wu, W., Pang, X., Liu, B., Zhang, Y.: A heuristic task scheduling algorithm based on server power efficiency model in cloud environments. Sustain. Comput. (2018). https://doi.org/10.1016/j.suscom.2017.10.007
Mao, L., Li, Y., Peng, G., Xu, X., Lin, W.: A multi-resource task scheduling algorithm for energy-performance trade-offs in green clouds. Sustain. Comput. (2018). https://doi.org/10.1016/j.suscom.2018.05.003
Mehta, D., Sullivan, B.O., Simonis, H.: Energy cost management for geographically distributed data centres under time-variable demands and energy prices. In: Proceedings of the IEEE/ACM 6th International Conference on Utility and Cloud Computing (2013). https://doi.org/10.1109/ucc.2013.22
Khosravi, A., Andrew, L.H., Buyya, R.: Dynamic VM placement method for minimizing energy and carbon cost in geographically distributed cloud data centers. In: Proceedings of the IEEE Transactions on Sustainable Computing (2017). https://doi.org/10.1109/tsusc.2017.2709980
Lagana, D., Mastroianni, C., Meo, M., Renga, D.: Reducing the operational cost of cloud data centers through renewable energy. Algorithms (2018). https://doi.org/10.3390/a11100145
Arianyan, E., Taheri, H., Khoshdel, V.: Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. J. Netw. Comput. Appl. (2016). https://doi.org/10.1016/j.jnca.2016.09.016
Amoon, M., Tobely, T.E.E.: A green energy-efficient scheduler for cloud data centers. Clust. Comput. (2018). https://doi.org/10.1007/s10586-018-2028-z
Lucas-Simarro, J.L., Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Cost optimization of virtual infrastructures in dynamic multi-cloud scenarios. Concurr. Comput. (2012). https://doi.org/10.1002/cpe.2972
Wood, T., Shenoy, P., Ramakrishnan, K.K., Merwe, J.: CloudNet: dynamic pooling of cloud resources by live wan migration of virtual machines. IEEE/ACM Trans. Netw. 23, 1568–1583 (2015)
Gupta, M.K., Jain, A., Amgoth, T.: Power and resource-aware virtual machine placement for IaaS cloud. Sustain. Comput. (2018). https://doi.org/10.1016/j.suscom.2018.07.001
Liu, L., Zheng, S., Yu, H., Anand, V., Xu, D.: Correlation-based virtual machine migration in dynamic cloud environment. Photon Netw. Commun. (2016). https://doi.org/10.1007/s11107-015-0539-6
Sun, G., Liao, D., Zhao, D., Xu, Z., Yu, H.: Live migration for multiple correlated virtual machines in cloud-based data centers. J. Serv. Comput. (2015). https://doi.org/10.1109/TSC.2015.2477825
Alshraideh, M., Mahafzah, B., Al-Sharaeh, S.: A multiple-population genetic algorithm for branch coverage test data generation. Softw. Qual. J. 19, 489–513 (2011)
Josepha, C.T., Chandrasekaran, K., Cyriac, R.: A novel family genetic approach for virtual machine allocation. Procedia Comput. Sci. (2015). https://doi.org/10.1016/j.procs.2015.02.090
Anand, A.: Adaptive virtual machine placement supporting performance SLAs. Dissertation, Super Computer Education and Research Centre Indian Institute of Science Bangalore. 10-23 (2013)
Masdari, M., Nabavi, S.S., Ahmadi, V.: An overview of virtual machine placement schemes in cloud computing. J. Netw. Comput. Appl. (2016). https://doi.org/10.1016/j.jnca.2016.01.011
Gahlawat, M., Sharma, P.: Survey of virtual machine placement in federated clouds. In: Proceedings of the IEEE International Advance Computing Conference (2014). https://doi.org/10.1109/iadcc.2014.6779415
Barzkar, A., Hosseini, S.M.H.: A novel peak load shaving algorithm via real-time battery scheduling for residential distributed energy storage systems. Int. J. Energy Res. (2018). https://doi.org/10.1002/er.4010
Tordsson, J., Montero, R.S., Vozmediano, R.M., Llorente, I.M.: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener. Comput. Syst. (2012). https://doi.org/10.1016/j.future.2011.07.003
Theja, P.R., Babu, S.K.K.: An adaptive genetic algorithm based robust QoS oriented green computing scheme for VM consolidation in large scale cloud infrastructures. J. Sci. Technol. (2014). https://doi.org/10.17485/ijst/2015/v8i27/79175
Tsakalozos, K., Verroios, V., Roussopoulos, M., Delis, A.: Live VM migration under time- constraints in share-nothing IaaS-clouds. IEEE Trans. Parallel Distrib. Syst. (2017). https://doi.org/10.1109/tpds.2017.2658572
Pires, F.L., Baran, B.: Virtual machine placement literature review. Polytechnic School National University of Asuncion Tech. Rep. https://sites.google.com/site/fiopezpires/ (2014). Accessed 4 June 2014
Jonardi, E., Oxley, M.A., Pasricha, S., Maciejewski, A.A., Siegel, H.J.: Energy cost optimization for geographically distributed heterogeneous data centers. In: Proceedings of the Sixth International Green and Sustainable Computing Conference (2015). https://doi.org/10.1109/igcc.2015.7393677
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Internet Serv. Appl. (2010). https://doi.org/10.1007/s13174-010-0007-6
Baker, T., Al-Dawsari, B., Tawfik, H., Reid, R., Ngoko, Y.: GreeDi: an energy efficient routing algorithm for big data on cloud. AdHoc Netw. (2015). https://doi.org/10.1016/j.adhoc.2015.06.008
Forestiero, A., Mastroianni, Meo, M., Papuzzo, G., Sheikhalishahi, M.: Hierarchical approach for efficient workload management in geo-distributed data centers. In: Proceedings of the IEEE Transactions on Green Communications and Networking (2017). http://doi.org/10.1109/TGCN.2016.2603586
Speitkamp, B., Bichler, M.: A mathematical programming approach for server consolidation problems in virtualized data centers. J. Serv. Comput. (2010). https://doi.org/10.1109/TSC.2010.25
Diaz, J.L., Entrialgo, J., Garcia, M., Garcia, J., Garcia, D.F.: Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing. J Future Gener. Comput. Syst. 71, 129–144 (2017)
Renders, J.M., Bersini, H.: Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways. In: Proceedings of The First IEEE Conference on Evolutionary Computation (1994). https://doi.org/10.1109/icec.1994.349948
Le, K., Bianchini, R., Zhang, J., Jaluria, Y., Meng, J., Nguyen, D.: Reducing electricity cost through virtual machine placement in high performance computing clouds. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2011). https://doi.org/10.1145/2063384.2063413
Thirumalaiselvan, C., Venkatachalam, V.: A strategic performance of virtual task scheduling in multi cloud environment. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-1268-7
Anand, A., Lakshmi, J., Nandy, S.K.: Virtual machine placement optimization supporting performance SLAs. In: Proceedings of the Cloud Computing Technology and Science (2013). http://doi.org/10.1109/CloudCom.2013.46
Zheng, Q., Li, R., Li, X., Shah, N., Zhang, J., Tian, F., Chao, K.M., Li, J.: Virtual machine consolidated placement based on multi objective biogeography-based optimization. J. Future Gener. Comput. Syst. (2016). https://doi.org/10.1016/j.future.2015.02.010
Wilcox, D., McNabb, A., Seppi, K.: Solving virtual machine packing with a reordering grouping genetic algorithm. Evolut. Comput. (2011). https://doi.org/10.1109/cec.2011.5949641
Vhansure, F., Deshmukh, A., Sumathy, S.: Load balancing in multi cloud computing environment with genetic algorithm. Mater. Sci. Eng. (2017). https://doi.org/10.1088/1757-899x/263/4/042010
Hu, H., Li, Z., Hu, H., Chen, J., Ge, J., Li, C., Chang, V.: Multi-objective scheduling for scientific workflow in multi cloud environment. J. Netw. Comput. Appl. (2018). https://doi.org/10.1016/j.jnca.2018.03.028
Hong, C.: A grouping genetic algorithm for virtual machine placement in cloud computing. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (2017). https://doi.org/10.1007/978-3-319-59288-6_43
Panda, S.K., Jana, P.K.: Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J. Supercomput. (2015). https://doi.org/10.1007/s11227-014-1376-6
Panda, S.K., Jana, P.K.: Normalization-based task scheduling algorithms for heterogeneous multi-cloud environment. Inf. Syst. Front. 20, 373–399 (2016)
Aldossary, M., Djemame, K.: Performance and energy-based cost prediction of virtual machines live migration in clouds. In: Proceedings of the 8th International Conference on Cloud Computing and Services Science (2018). https://doi.org/10.5220/0006682803840391
Sharma, N.K., Sharma, P., Guddeti, R.M.R.: Energy efficient quality of service aware virtual machine migration in cloud computing. In: Proceedings of the 4th International Conference on Recent Advances in Information Technology (2018). https://doi.org/10.1109/rait.2018.8389047
Liu, Z., Lin, M., Wierman, A., Low. S., Andrew, L.L.H.: Greening geographical load balancing. In: Proceedings of the SIGMETRICS ‘11 ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems (2011). https://doi.org/10.1145/1993744.1993767
Fiandino, C., Bouvry, P.: Performance and energy efficiency metrics for communication systems of cloud computing data centers. In: Proceedings of the IEEE Transactions on Cloud Computing, pp. 99–113 (2015)
Kusic, D., Kephart, J.O., Hanson, J.E., Kandasamy, N., Jiang, G.: Power and performance management of virtualized computing environments via look ahead control. Clust. Comput. (2009). https://doi.org/10.1007/s10586-008-0070-y
Kliazovich, D., Bouvry, P., Khan, S.U.: Dens: data center energy-efficient network-aware scheduling. Clust. Comput. (2013). https://doi.org/10.1007/s10586-011-0177-4
Wei, J., Zhou, A., Yuan, J., Yang, F.: AIMING: resource allocation with latency awareness for federated-cloud applications. Wirel. Commun. Mob. Comput. (2018). https://doi.org/10.1155/2018/4593208
Shah, S.A.R., Jaikar, A.H., Noh, S.Y.: A performance analysis of precopy, postcopy and hybrid live VM migration algorithms in scientific cloud computing environment. In: Proceedings of the International Conference on High Performance Computing & Simulation (2015). https://doi.org/10.1109/hpcsim.2015.7237044
Verma, A., Ahuja, P., Neogi, A.: pMapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp. 243–264 (2008)
Ferdaus, M.H., Calheiros, R.N., Murshed, M., Buyya, R.: Network-aware virtual machine placement and migration in cloud data centers. In: Baghchi, S. (ed.) Emerging Research in Cloud Distributed Computing Systems, pp. 42–91. IGI Global, Pennsylvania (2015)
Alshraideh, M., Mahafzah, B., Eyal Salman, H., Salah, I.: Using genetic algorithm as test data generator for stored PL/SQL program units. J. Softw. Eng. Appl. 6, 65–73 (2013)
Burke, E.K., Newall, J.R., Weare, R.E.: A memetic algorithm for university exam timetabling. In: Proceedings of the Practice and Theory of Automated Timetabling (2005). https://doi.org/10.1007/3-540-61794-9_63
Falkenauer, E., Delchambre, A.: A genetic algorithm for bin packing and line balancing. Robot. Autom. (1992). https://doi.org/10.1109/ROBOT.1992.220088
Falkenauer, E.: A hybrid grouping genetic algorithm for bin packing. J. Heuristics 2, 5–30 (1996)
Falkenauer, E.: Genetic Algorithms and Grouping Problems. Wiley, Hoboken (1998)
Rahnamayan, Sh, Tizhoosh, H.R., Salama, M.M.A.: A novel population initialization method for accelerating evolutionary algorithms. Comput. Math. Appl. (2007). https://doi.org/10.1016/j.camwa.2006.07.013
Paul, P.V., Moganarangan, N., Sampath Kumar, S., Raju, R., Vengattaraman, T., Dhavachelvan, P.: Performance analyses over population seeding techniques of the permutation-coded genetic algorithm: an empirical study based on traveling salesman problems. Appl. Soft Comput. (2015). https://doi.org/10.1016/j.asoc.2015.03.038
Bajer, D., Martinovi, G., Brest, J.: A population initialization method for evolutionary algorithms based on clustering and cauchy deviates. Expert Syst. Appl. (2016). https://doi.org/10.1016/j.eswa.2016.05.009
Zhong, J., Hu, X., Gu, M., Zhang, J.: Comparison of performance between different selection strategies on simple genetic algorithms. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and automation (2005). https://doi.org/10.1109/cimca.2005.1631619
Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Syst. 9, 193–212 (1995)
Razali, N.M., Geraghty, J.: Genetic algorithm performance with different selection strategies in solving TSP. In: Proceedings of the World Congress on Engineering. London, UK, 2011
Blickle, T., Thiele, L.: A comparison of selection schemes used in genetic algorithms. TIK-Report, Zurich (1995)
Tsang, E., Voudouris, C.: Fast local search and guided local search and their application to British telecom’s workforce scheduling problem. Op. Res. Lett. 20, 119–127 (1997)
Al-Adwan, A., Sharieh, A., Mahafzah, B.: Parallel heuristic local search algorithm on OTIS hyper hexa-cell and OTIS mesh of trees optoelectronic architectures. Appl. Intell. 49, 661–688 (2019). https://doi.org/10.1007/s10489-018-1283-2
Naldi, M.C., Campello, R.J.G.B., Hruschka, E.R., Carvalho, A.C.P.L.F.: Efficiency issues of evolutionary k-means. Appl. Soft Comput. 11, 1938–1952 (2011)
Amazon EC2 instance types. http://aws.amazon.com/ec2/. Accessed 7 July 2018
Google Cloud Platform Price. https://cloud.google.com/pric-ing/. Accessed 16 Sept 2018
Microsof Azure Price. https://azure.microsof.com/en-us/pric-ing/. Accessed 20 Oct 2018
EIA. (2012). Electric power monthly. US Energy Information Administration http://www.eia.gov/electricity/monthly/pdf/epm.pdf. Accessed 7 Jan 2012
Juarez, F., Ejarque, J., Badia, R.M.: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Gener. Comput. Syst. 25, 256 (2016). https://doi.org/10.1016/j.future.2016.06.029
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rashida, S.Y., Sabaei, M., Ebadzadeh, M.M. et al. A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment. Cluster Comput 23, 797–836 (2020). https://doi.org/10.1007/s10586-019-02956-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-019-02956-8