Abstract
Cloud container resource allocation aims to find container placements in cloud Virtual Machines (VM) and Physical Machines (PM) such that overall energy consumption is minimised. A resource allocation architecture where application containers are consolidated into cloud VMs in a container-VM-PM model is common practise in data centers. The VM layer may provide additional administrative or security features, but adds complexity to the optimization problem when deploying containers initially on a large scale. Research addressing this two-level resource allocation is limited, some of the recent work try to optimise consolidation of containers to VM layer separately from consolidation of VMs to PMs, which results in large portions of the search space remaining unexplored. A Grouping Genetic Algorithm (GGA) framework that can simultaneously optimize consolidation on both levels is promising. However, for large instances of the two-level optimisation, it may suffer from premature convergence and limited population diversity. In this work, we propose a new fixed-length crossover operator that is designed to improve population diversity and exploration in GGA for container resource allocation optimisation. We also propose problem-specific Best-Fit and Largest VM heuristic operators to aid local search by rearranging containers from the lower fitness PMs at the chromosome tail into existing VMs and PMs with better utilization when possible. We demonstrate that with the newly developed operators, the proposed GGA can significantly reduce energy consumption in large-scale test cases.
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
References
Bernstein, D.: Containers and cloud: from LXC to docker to Kubernetes. IEEE Cloud Comput. 1(3), 81–84 (2014)
Falkenauer, E.: A hybrid grouping genetic algorithm for bin packing. J. Heurist. 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, 113–135 (2018)
Helali, L., Omri, M.N.: A survey of data center consolidation in cloud computing systems. Comput. Sci. Rev. 39 (2021). https://doi.org/10.1016/j.cosrev.2021.100366
Hussein, M.K., Mousa, M.H., Alqarni, M.A.: A placement architecture for a container as a service (CaaS) in a cloud environment. J. Cloud Comput. 8(1), 1–15 (2019). https://doi.org/10.1186/s13677-019-0131-1
Kaewkasi, C., Chuenmuneewong, K.: Improvement of container scheduling for docker using ant colony optimization. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 254–259 (2017)
step method for large-scale container deployment (2020). https://www.alibabacloud.com/blog/4-step-method-for-large-scale-container-deployment_596928. Accessed 16 August 2021
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 (2018)
Mann, Z.Á.: Interplay of virtual machine selection and virtual machine placement. In: Service-Oriented and Cloud Computing, pp. 137–151 (2016)
Mann, Z.Á.: Resource optimization across the cloud stack. IEEE Trans. Parallel Distrib. Syst. 29(1), 169–182 (2018)
Quiroz-Castellanos, M., et al.: A grouping genetic algorithm with controlled gene transmission for the bin packing problem. Comput. Operat. Res. 55, 52–64 (2015)
Shen, S., Van Beek, V., Iosup, A.: Statistical characterization of business-critical workloads hosted in cloud datacenters. In: 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 465–474 (2015)
Shi, T., Ma, H., Chen, G.: Energy-aware container consolidation based on PSO in cloud data centers. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2018)
Swarm mode overview (2021). https://docs.docker.com/engine/swarm/. Accessed 16 August 2021
Tan, B., Ma, H., Mei, Y.: A NSGA-ii-based approach for service resource allocation in cloud. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2574–2581 (2017). https://doi.org/10.1109/CEC.2017.7969618
Tan, B., Ma, H., Mei, Y.: Novel genetic algorithm with dual chromosome representation for resource allocation in container-based clouds. In: IEEE International Conference on Cloud Computing (CLOUD), pp. 452–456 (2019)
Tan, B., Ma, H., Mei, Y.: A group genetic algorithm for resource allocation in container-based clouds. In: Paquete, L., Zarges, C. (eds.) Evolutionary Computation in Combinatorial Optimization, pp. 180–196 (2020)
Varasteh, A., Goudarzi, M.: Server consolidation techniques in virtualized data centers: a survey. IEEE Syst. J. 11(2), 772–783 (2017)
Zhang, C., Wang, Y., Wu, H., Guo, H.: An energy-aware host resource management framework for two-tier virtualized cloud data centers. IEEE Access 9, 3526–3544 (2021)
Zhang, R., Zhong, A.m., Dong, B., Tian, F., Li, R.: Container-VM-PM architecture: a novel architecture for docker container placement. In: Cloud Computing - CLOUD 2018, pp. 128–140 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Akindele, T., Tan, B., Mei, Y., Ma, H. (2022). Hybrid Grouping Genetic Algorithm for Large-Scale Two-Level Resource Allocation of Containers in the Cloud. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-97546-3_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97545-6
Online ISBN: 978-3-030-97546-3
eBook Packages: Computer ScienceComputer Science (R0)