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

Authors

  • Indrani Vasireddy Associate Professor, Department of Computer Science and Engineering, Geethanjali College of Engineering, Hyderabad, India Author
  • Prathima Kandi Assistant Professor, Department of Computer Science and Engineering, Geethanjali College of Engineering, Hyderabad, India Author
  • SreeRamya Gandu Assistant Professor, Department of Computer Science and Engineering, Geethanjali College of Engineering, Hyderabad, India Author

Keywords:

Kubernetes, Cloud computing, Conatiners, Load Balancing

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. 

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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.

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Published

2023-12-30

How to Cite

Efficient Resource Utilization in Kubernetes: A Review of Load Balancing Solutions . (2023). International Journal of Innovative Research in Engineering & Management, 10(6), 44–48. Retrieved from https://acspublisher.com/journals/index.php/ijirem/article/view/12288