Efficient Resource Utilization in Kubernetes: A Review of Load Balancing Solutions

Indrani Vasireddy; Prathima Kandi; SreeRamya Gandu

doi:10.55524/ijirem.2023.10.6.6

Abstract

Modern distributed systems face the challenge of efficiently distributing workloads across nodes to ensure optimal resource utilization, high avail-ability, and performance. In this context, Kubernetes, an open-source container orchestration engine, plays a pivotal role in automating deployment, scaling, and management of containerized applications. This paper explores the landscape of load balancing strategies within Kubernetes, aiming to provide a comprehensive overview of existing techniques, challenges, and best practices. The paper delves into the dynamic nature of Kubernetes environments, where applications scale dynamically, and demand for resources fluctuates. We review various load balancing approaches, including those based on traffic, resource-aware algorithms, and affinity policies. Special attention is given to the unique characteristics of containerized workloads and their impact on load balancing decisions. In this paper the implications of load balancing on the scalability and performance of applications deployed in Kubernetes clusters. It explores the trade-offs between different strategies, considering factors such as response time, throughput, and the adapt-ability to varying workloads. As cloud-native architectures continue to evolve, understanding and addressing the intricacies of load balancing in dynamic con-tainer orchestration environments become increasingly crucial. In this paper we had consolidated the current state of knowledge on load balancing in Kubernetes, providing researchers and practitioners with valuable insights and a foundation for further advancements in the quest for efficient, scalable, and resilient distrib-uted systems.

Keywords

Kubernetes, Cloud computing, Conatiners, Load Balancing.

References

Chang, C., Yang, S., Yeh, E., Lin, P. & Jeng, J. A Kubernetes-based monitoring platform for dynamic cloud resource provisioning. GLOBECOM 2017-2017 IEEE Global Communica-tions Conference. pp. 1-6 (2017)
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.

Cites this article as

I. Vasireddy, P. Kandi, S. Gandu, "Efficient Resource Utilization in Kubernetes: A Review of Load Balancing Solutions", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-10, Issue-6, Page No-40-44, 2023. Available from: https://doi.org/10.55524/ijirem.2023.10.6.6