Kubernetes and Docker Load Balancing: State-of-the-Art Techniques and Challenges

Indrani Vasireddy; G.Ramya; Prathima Kandi

doi:10.55524/ijirem.2023.10.6.7

Abstract

In the ever-evolving landscape of container orchestration, Kubernetes stands out as a leading platform, empowering organizations to deploy, scale, and manage containerized applications seamlessly. This survey paper explores the critical domain of load balancing within Kubernetes, investigating state-of-the-art techniques and the associated challenges. Container-based virtualization has revolutionized cloud computing, and Kubernetes, as a key orchestrator, plays a central role in optimizing resource allocation, scalability, and application performance. Load balancing, a fundamental aspect of distributed systems, becomes paramount in ensuring efficient utilization of resources and maintaining high availability. The study focuses on contemporary methods for achieving effect-ive load balancing on containers, with a specific examination of Docker Swarm and Kubernetes—prominent systems for container deployment and management. The paper illustrates how Docker Swarm and Kubernetes can leverage load bal-ancing techniques to optimize traffic distribution. Load balancing algorithms are introduced and implemented in both Docker and Kubernetes, and their outcomes are systematically compared. The paper concludes by highlighting why Kuber-netes is often the preferred choice over Docker Swarm for load balancing pur-poses.This paper provides a comprehensive overview of the current state-of-the-art techniques employed in Kubernetes load balancing. Challenges inherent to Docker load balancing are addressed, encompassing is-sues related to the dynamic nature of containerized workloads, varying application demands, and the need for real-time adaptability. The survey also explores the role of load balancing in enhancing the scalability and overall performance of applications within Kubernetes clusters. In conclusion, this survey consolidates the current knowledge on Docker and Kubernetes load balancing, offering a state-of-the-art analysis while identifying challenges that pave the way for future research and advancements in the realm of container orchestration and distributed systems.

Keywords

Load balancing,Docker Swarm, Kubernetes, Containers.

Reference

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Zhong Z, Buyya R (2020) A Cost-Efficient Container Orchestration Strategy in Kubernetes-Based Cloud Computing Infrastructures with Heterogeneous Resources. ACM Trans Internet Technol 20(2):1–24
Kim SH, Kim T (2023) Local scheduling in kubeedge-based edge computing environment. Sensors 23(3):1522
Peng Y, Bao Y, Chen Y, Wu C, Guo C (2018) Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters. Proceedings of the 13th EuroSys Conference, EuroSys
Mao H, Schwarzkopf M, Venkatakrishnan SB, Meng Z, Alizadeh M (2019) Learning schedul-ing algorithms for data processing clusters. SIGCOMM Conference of the ACM Special In-terest Group on Data Communication. pp 270–288
Chaudhary S, Ramjee R, Sivathanu M, Kwatra N, Viswanatha S (2020) Balancing effi-ciency and fairness in heterogeneous GPU clusters for deep learning. Proceedings of the 15th European Conference on Computer Systems, EuroSys
Kubernetes: Available: http://kubernetes.io/.
Taherizadeh S, Stankovski V (2019) Dynamic multi-level auto-scaling rules for containerized applications. Computer J 62(2):174–197
Rattihalli G, Govindaraju M, Lu H, Tiwari D (2019) Exploring potential for non-disruptive vertical auto scaling and resource estimation in kubernetes. IEEE International Conference on Cloud Computing, CLOUD. pp 33–40
Jain, N., Mohan, V., Singhai, A., Chatterjee, D. & Daly, D. Kubernetes Load-balancing and related network functions using P4. Proceedings Of The Symposium On Architectures For Networking And Communications Systems. pp. 133-135 (2021)
Toka L, Dobreff G, Fodor B, Sonkoly B (2021) Machine Learning-Based Scaling Manage-ment for Kubernetes Edge Clusters. IEEE Trans Netw Serv Manage 18(1):958–972
Masne, S., Wankar, R., Raghavendra Rao, C. & Agarwal, A. Seamless provision of cloud services using peer-to-peer (p2p) architecture. Distributed Computing And Internet Techno-logy: 8th International Conference, ICDCIT 2012, Bhubaneswar, India, February 2-4, 2012. Proceedings 8. pp. 257-258 (2012)
Kim SH, Kim T. Local Scheduling in KubeEdge-Based Edge Computing Environment. Sensors (Basel). 2023 Jan 30;23(3):1522. doi: 10.3390/s23031522. PMID: 36772562; PM-CID: PMC9921110.
Wankar, Rajeev. (2008). Grid Computing with Globus: An Overview and Research Chal-lenges. International Journal of Computer Science Applications.
Vasireddy, Indrani, Rajeev Wankar, and Raghavendra Rao Chillarige. "Recreation of a Sub-pod for a Killed Pod with Optimized Containers in Kubernetes." International Conference on Expert Clouds and Applications. Singapore: Springer Nature Singapore, 2022.
Li D, Wei Y, Zeng B (2020) A Dynamic I/O Sensing Scheduling Scheme in Kubernetes. ACM International Conference Proceeding Series. Pp 14–19
Cito, Jürgen & Ferme, Vincenzo & C. Gall, Harald. (2016). Using Docker Containers to Improve Reproducibility in Software and Web Engineering Research. 609-612. 10.1007/978-3-319-38791-8_5
Dynamic Balance Strategy of High Concurrent Web Cluster Based on Docker Container. Weizheng Ren et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 466 012011
El Haj Ahmed G, Gil-Castiñeira F, Costa-Montenegro E (2021) KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters. Software Pract Experience 51(2):213–234
Ismail, Bukhary Ikhwan, et al. "Evaluation of Docker as edge computing platform." 2015 IEEE Conference on Open Systems (ICOS). IEEE, 2015.

Cites this article as

I. Vasireddy, G.Ramya, P. Kandi, "Kubernetes and Docker Load Balancing: State-of-the-Art Techniques and Challenges", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-10, Issue-6, Page No-45-50, 2023. Available from: https://doi.org/10.55524/ijirem.2023.10.6.7