A Novel Methodology for Container Scheduling and Load Balancing in Distributed Environments
Abstract
Deployment of applications in distributed environments via containers has gained huge popularity lately, specifically with cloud-based ecosystems. Inspired by the quick growth of container usage and deployment in distributed environments, efficient scheduling techniques are of prior significance embedded with load balancing in it for cloud computing tasks. Most of the scheduling strategies adopt conventional methods and fail to execute efficiently in the dynamic cloud or distributed environments where applications around the world depend on them for scalability, efficiency, and availability. Existing applications focus more on performance metrics instead of scheduling efficiency, so often they offer performance that can come at the expense of scheduling. This paper proposes a new algorithm that includes consideration of contention over the network, along with efficient canister planning and load distribution. The algorithm we have designed to achieve the proposed scheduling and load balancing is Contention-aware Greedy Heuristic Scheduling and Load Balancing for Containers (CGHSLBC), which has been extensively evaluated under continuous workload and has outperformed current state-of-the-art algorithms by 20% in load balancing efficiency and 25% in network contention reduction, demonstrating its promise for container scheduling in dynamic distributed environments.
Keywords
References
Alqahtani, F., Amoon, M., & Nasr, A. A. (2021). Reliable scheduling and load balancing for requests in cloud-fog computing. Peer-to-Peer Networking and Applications, 14(4), 1905–1916. doi:10.1007/s12083-021-01125-2
XIE, X., & Govardhan, S. S. (2020). A Service Mesh-Based Load Balancing and Task Scheduling System for Deep Learning Applications. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). doi:10.1109/ccgrid49817.2020.00009
Imtiaz Ahmad, Mohammad Gh. AlFailakawi, Asayel AlMutawa, Latifa Alsalman. (2022). Container scheduling techniques: A survey and assessment. Elsevier. 34(7), pp.3934-3947. https://doi.org/10.1016/j.jksuci.2021.03.002
Gabriele Proietti Mattia, and Roberto Beraldi. (2023). P2PFaaS: A framework for FaaS peer-to-peer scheduling and load balancing in Fog and Edge computing. Elsevier. 21, pp.1-7. https://doi.org/10.1016/j.softx.2022.101290
Singh, A., Aujla, G. S., & Bali, R. S. (2021). Container-based load balancing for energy efficiency in a softwaredefined edge computing environment. Sustainable Computing: Informatics and Systems, 30, 100463. doi:10.1016/j.suscom.2020.100463
Patra, Patel, D., Sahoo, B., & Turuk, A. K. (2020). A Randomized Algorithm for Load Balancing in Containerized Cloud. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). https://doi.org/10.1109/confluence47617.2020.9058147
Saravanan Muniswamy and Radhakrishnan Vignesh. (2022). DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment. Sprigner. 11(33), pp.1-19. https://doi.org/10.1186/s13677-022-00304-7
Dhahbi, S., Berrima, M., & Al-Yarimi, F. A. M. (2021). Load balancing in cloud computing using worst-fit binstretching. Cluster Computing. doi:10.1007/s10586-021-03302-7
Mufeed Ahmed Naji Saif, S. K. Niranjan, Belal Abdullah Hezam Murshed, Fahd A. Ghanem, and Ammar Abdullah Qasem Ahmed. (2023). CSO-ILB: chicken swarm optimized inter-cloud load balancer for elastic containerized multi-cloud environment. Springer. 79, p.1111–1155. https://doi.org/10.1007/s11227-022-04688-w
Oleghe, O. (2021). Container Placement and Migration in Edge Computing: Concept and Scheduling Models. IEEE Access, 9, 68028–68043. doi:10.1109/access.2021.3077550
Ma, Z., Shao, S., Guo, S., Wang, Z., Qi, F., & Xiong, A. (2020). Container Migration Mechanism for Load Balancing in Edge Network Under Power Internet of Things. IEEE Access, 8, 118405–118416. doi:10.1109/access.2020.3004615
Rajasekar, P., & Palanichamy, Y. (2020). Scheduling multiple scientific workflows using containers on IaaS cloud. Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-020-02483-0
Menouer, T. (2020). KCSS: Kubernetes container scheduling strategy. The Journal of Supercomputing. doi:10.1007/s11227-020-03427-3
Neelam Singh, Yasir Hamid, Sapna Juneja, Gautam Srivastava, Gaurav Dhiman, Thippa Reddy Gadekallu, and Mohd Asif Shah. (2023). Load balancing and service discovery using Docker Swarm for microservice based big data applications. Springer. 12(4), pp.1-9. https://doi.org/10.1186/s13677-022-00358-7
Jincheng Zhou, Umesh Kumar Lilhore, Poongodi M, Tao Hai, Sarita Simaiya, Dayang Norhayati Abang Jawawi, Deemamohammed Alsekait, Sachin Ahuja, Cresantus Biamba, and Mounir Hamdi. (2023). Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing. Sprigner. 12(85), pp.1-21. https://doi.org/10.1186/s13677-023-00453-3
Tychalas, D., & Karatza, H. (2019). A scheduling algorithm for a Fog Computing System with Bag-of-Tasks Jobs: Simulation and Performance Evaluation. Simulation Modelling Practice and Theory, 101982. doi:10.1016/j.simpat.2019.101982
Shekhar, C. A., & Sharvani, G. S. (2021). MTLBP: A Novel Framework to Assess Multi-Tenant Load Balance in Cloud Computing for Cost-Effective Resource Allocation. Wireless Personal Communications, 120(2), 1873–1893. doi:10.1007/s11277-021-08541-w
Ranjan, R., Thakur, I., Aujla, G. S., Kumar, N., & Zomaya, A. Y. (2020). Energy-Efficient Workflow Scheduling using Container based Virtualization in Software Defined Data Centers. IEEE Transactions on Industrial Informatics, 1–1. doi:10.1109/tii.2020.2985030
Cai, W., Zhu, J., Bai, W., Lin, W., Zhou, N., & Li, K. (2020). A cost saving and load balancing task scheduling model for computational biology in heterogeneous cloud datacenters. The Journal of Supercomputing. doi:10.1007/s11227-020-03305-y
Srirama, S. N., Adhikari, M., & Paul, S. (2020). Application deployment using containers with auto-scaling for microservices in cloud environment. Journal of Network and Computer Applications, 102629. doi:10.1016/j.jnca.2020.102629
ELSAKAAN Nadim and AMROUN Kamal. (2024). A novel multi-level hybrid load balancing and tasks scheduling algorithm for cloud computing environment. Sprigner, pp.1-36. https://doi.org/10.21203/rs.3.rs-3088655/v1
Nisha Devi, Sandeep Dalal, Kamna Solanki, Surjeet Dalal, Umesh Kumar Lilhore, Sarita Simaiya, and Nasratullah Nuristani. (2024). A systematic literature review for load balancing and task scheduling techniques in cloud computing. Springer. 57(276), pp.1-63. https://doi.org/10.1007/s10462-024-10925-w
M. Menaka, Research Scholar, K.S. Sendhil Kumar, Associate Profess. (2024). Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimi. Elsevier. 5(.), pp.192-198. [Online]. Available at: https://doi.org/10.1016/j.ijcce.2024.05.002
Rausch, T., Rashed, A., & Dustdar, S. (2020). Optimized container scheduling for data-intensive serverless edge computing. Future Generation Computer Systems. doi:10.1016/j.future.2020.07.017
Zhu, L., Huang, K., Hu, Y., & Tai, X. (2021). A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata. IEEE Access, 9, 81236–81252. doi:10.1109/access.2021.3078773
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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272
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