Abstract
In recent years, container-based clouds are becoming increasingly popular for their lightweight nature. Existing works on container consolidation mainly focus on reducing the energy consumption of cloud data centers. However, reducing energy consumption often results in container migrations which have big impact on the performance (i.e. availability) of applications in the containers. In this paper, we consider container consolidation as one multi-objective optimization problem with the objectives of minimizing the total energy consumption and minimizing the total number of container migrations within the certain period of time and present an NSGA-II based algorithm to find solutions for the container consolidation problem. Our experimental evaluation based on the real-world workload demonstrates that our proposed approach can lead to further energy saving and significant reduction of container migrations at the same time compared with some existing approaches.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Docker website (2018). https://www.docker.com/
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., Merle, P.: Elasticity in cloud computing: state of the art and research challenges. IEEE Trans. Serv. Comput. 11(2), 430–447 (2018)
Blackburn, M., Grid, G.: Five ways to reduce data center server power consumption. Green Grid 42, 12 (2008)
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., 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)
Deb, K.: Multi-objective Optimization using Evolutionary Algorithms, vol. 16. Wiley, Hoboken (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Durillo, J.J., Nebro, A.J.: jMetal: a java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)
Hanafy, W.A., Mohamed, A.E., Salem, S.A.: Novel selection policies for container-based cloud deployment models. In: 2017 13th International Computer Engineering Conference (ICENCO), pp. 237–242. IEEE (2017)
Kaur, T., Chana, I.: Energy efficiency techniques in cloud computing: a survey and taxonomy. ACM Comput. Surv. (CSUR) 48(2), 22 (2015)
Mann, Z.A.: Resource optimization across the cloud stack. IEEE Trans. Parallel Distrib. Syst. 29(1), 169–182 (2018)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
Park, K., Pai, V.S.: Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Oper. Syst. Rev. 40(1), 65–74 (2006)
Piraghaj, S.F., Dastjerdi, A.V., Calheiros, R.N., Buyya, R.: A framework and algorithm for energy efficient container consolidation in cloud data centers. In: 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS), pp. 368–375. IEEE (2015)
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 (2017)
Quan, D.M., Mezza, F., Sannenli, D., Giafreda, R.: T-alloc: a practical energy efficient resource allocation algorithm for traditional data centers. Futur. Gener. Comput. Syst. 28(5), 791–800 (2012)
Sharma, N., Guddeti, R.M.: Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans. Serv. Comput. (2016)
Tan, B., Ma, H., Zhang, M.: Optimization of location allocation of web services using a modified non-dominated sorting genetic algorithm. In: Ray, T., Sarker, R., Li, X. (eds.) ACALCI 2016. LNCS (LNAI), vol. 9592, pp. 246–257. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28270-1_21
Zhang, D., Yan, B.H., Feng, Z., Zhang, C., Wang, Y.X.: Container oriented job scheduling using linear programming model. In: 2017 3rd International Conference on Information Management (ICIM), pp. 174–180. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, T., Ma, H., Chen, G. (2018). Multi-objective Container Consolidation in Cloud Data Centers. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-03991-2_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03990-5
Online ISBN: 978-3-030-03991-2
eBook Packages: Computer ScienceComputer Science (R0)