Abstract
Containers have gain popularity because they support fast development and deployment of cloud-native software such as micro-services and server-less applications. Additionally, containers have low overhead, hence they save resources in cloud data centers. However, the difficulty of the Resource Allocation in Container-based clouds (RAC) is far beyond Virtual Machine (VM)-based clouds. The allocation task selects heterogeneous VMs to host containers and consolidate VMs to Physical Machines (PMs) simultaneously. Due to the high complexity, existing approaches use simple rule-based heuristics and meta-heuristics to solve the RAC problem. They either prone to stuck at local optima or have inherent defects in their indirect representations. To address these issues, we propose a novel group genetic algorithm (GGA) with a direct representation and problem-specific operators. This design has shown significantly better performance than the state-of-the-art algorithms in a wide range of test datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bhandari, D., Murthy, C., Pal, S.K.: Genetic algorithm with elitist model and its convergence. Int. J. Pattern Recogn. Artif. Intell. 10(06), 731–747 (1996)
Tan, B., Ma, H., Mei, Y.: A NSGA-II-based approach for service resource allocation in Cloud. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2574–2581 (2017)
Falkenauer, E.: A hybrid grouping genetic algorithm for bin packing. J. Heuristics 2(1), 5–30 (1996)
Guerrero, C., Lera, I., Juiz, C.: Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J. Grid Comput. 16(1), 113–135 (2018)
Kaaouache, M.A., Bouamama, S.: Solving bin packing problem with a hybrid genetic algorithm for VM placement in cloud. Proc. Comput. Sci. 60, 1061–1069 (2015)
Kaur, K., Dhand, T., Kumar, N., Zeadally, S.: Container-as-a-service at the edge: trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wirel. Commun. 24(3), 48–56 (2017)
Koch, S., Wäscher, G.: A grouping genetic algorithm for the order batching problem in distribution warehouses. J. Bus. Econ. 86(1–2), 131–153 (2016)
Lin, M., Xi, J., Bai, W., Wu, J.: Ant colony algorithm for multi-objective optimization of container-based microservice scheduling in cloud. IEEE Access 7, 83088–83100 (2019)
Liu, B., Li, P., Lin, W., Shu, N., Li, Y., Chang, V.: A new container scheduling algorithm based on multi-objective optimization. Soft Comput. 22(23), 7741–7752 (2018). https://doi.org/10.1007/s00500-018-3403-7
Liu, X.F., Zhan, Z.H., Deng, J.D., Li, Y., Gu, T., Zhang, J.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput. 22(1), 113–128 (2016)
Mann, Z.Á.: Interplay of virtual machine selection and virtual machine placement. In: Aiello, M., Johnsen, E.B., Dustdar, S., Georgievski, I. (eds.) ESOCC 2016. LNCS, vol. 9846, pp. 137–151. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44482-6_9
Mann, Z.Á.: Resource optimization across the cloud stack. IEEE Trans. Parallel Distrib. Syst. 29(1), 169–182 (2018)
Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Syst. 9(3), 193–212 (1995)
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: International Conference on Data Science and Data Intensive Systems, pp. 368–375. IEEE (2015)
Poon, P.W., Carter, J.N.: Genetic algorithm crossover operators for ordering applications. Comput. Oper. Res. 22(1), 135–147 (1995)
Quiroz-Castellanos, M., Cruz-Reyes, L., Torres-Jimenez, J., Gómez, C., Huacuja, H.J.F., Alvim, A.C.: A grouping genetic algorithm with controlled gene transmission for the bin packing problem. Comput. Oper. Res. 55, 52–64 (2015)
Şahin, M., Kellegöz, T.: An efficient grouping genetic algorithm for u-shaped assembly line balancing problems with maximizing production rate. Memetic Comput. 9(3), 213–229 (2017)
Shen, S., van Beek, V., Iosup, A.: Statistical characterization of business-critical workloads hosted in cloud datacenters. In: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 465–474. IEEE (2015)
Tan, B., Ma, H., Mei, Y.: Novel genetic algorithm with dual chromosome representation for resource allocation in container-based clouds. In: International Conference on Cloud Computing, pp. 452–456. IEEE (2019)
Wen, Y., Li, Z., Jin, S., Lin, C., Liu, Z.: Energy-efficient virtual resource dynamic integration method in cloud computing. IEEE Access 5, 12214–12223 (2017)
Wolke, A., Bichler, M., Setzer, T.: Planning vs. dynamic control: resource allocation in corporate clouds. IEEE Trans. Cloud Comput. 4(3), 322–335 (2016)
Zhang, R., Zhong, A., Dong, B., Tian, F., Li, R.: Container-VM-PM architecture: a novel architecture for docker container placement. In: Luo, M., Zhang, L.-J. (eds.) CLOUD 2018. LNCS, vol. 10967, pp. 128–140. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94295-7_9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tan, B., Ma, H., Mei, Y. (2020). A Group Genetic Algorithm for Resource Allocation in Container-Based Clouds. In: Paquete, L., Zarges, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2020. Lecture Notes in Computer Science(), vol 12102. Springer, Cham. https://doi.org/10.1007/978-3-030-43680-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-43680-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43679-7
Online ISBN: 978-3-030-43680-3
eBook Packages: Computer ScienceComputer Science (R0)