{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:52:44Z","timestamp":1774630364058,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2014NextGenerationEU","award":["BG-RRP-2.004-0005"],"award-info":[{"award-number":["BG-RRP-2.004-0005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Distributed Kubernetes clusters provide robust solutions for geo-redundancy and fault tolerance in modern cloud architectures. However, default scheduling mechanisms primarily optimize for resource availability, often neglecting network topology, inter-node latency, and global resource efficiency, leading to suboptimal task placement in multi-region deployments. This paper proposes network-aware scheduling plugins that integrate heuristic, metaheuristic, and linear programming methods to optimize resource utilization and inter-zone communication latency for containerized workloads, particularly Apache Spark batch-processing tasks. Unlike the default scheduler, the presented approach incorporates inter-node latency constraints and prioritizes locality-aware scheduling, ensuring efficient pod distribution while minimizing network overhead. The proposed plugins are evaluated using the kube-scheduler-simulator, a tool that replicates Kubernetes scheduling behavior without deploying real workloads. Experiments cover multiple cluster configurations, varying in node count, region count, and inter-region latencies, with performance metrics recorded for scheduler efficiency, inter-zone communication impact, and execution time across different optimization algorithms. The obtained results indicate that network-aware scheduling approaches significantly improve latency-aware placement decisions, achieving lower inter-region communication delays while maintaining resource efficiency.<\/jats:p>","DOI":"10.3390\/computers14040114","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T04:58:38Z","timestamp":1742533118000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Efficient Orchestration of Distributed Workloads in Multi-Region Kubernetes Cluster"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2857-9496","authenticated-orcid":false,"given":"Radoslav","family":"Furnadzhiev","sequence":"first","affiliation":[{"name":"Department of Computer Systems and Technologies, Faculty of Electronics and Automation, Technical University of Sofia, Plovdiv Branch, 1797 Sofia, Bulgaria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8195-0459","authenticated-orcid":false,"given":"Mitko","family":"Shopov","sequence":"additional","affiliation":[{"name":"Department of Computer Systems and Technologies, Faculty of Electronics and Automation, Technical University of Sofia, Plovdiv Branch, 1797 Sofia, Bulgaria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9227-0603","authenticated-orcid":false,"given":"Nikolay","family":"Kakanakov","sequence":"additional","affiliation":[{"name":"Department of Computer Systems and Technologies, Faculty of Electronics and Automation, Technical University of Sofia, Plovdiv Branch, 1797 Sofia, Bulgaria"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TC.1979.1675348","article-title":"Assignment of Tasks in a Distributed Processor System with Limited Memory","volume":"30","author":"Rao","year":"1979","journal-title":"IEEE Trans. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abdul-Rahman, O., and Aida, K. (2014, January 15\u201318). Towards Understanding the Usage Behavior of Google Cloud Users: The Mice and Elephants Phenomenon. Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, Singapore.","DOI":"10.1109\/CloudCom.2014.75"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Centofanti, C., Tiberti, W., Marotta, A., Graziosi, F., and Cassioli, D. (2023, January 19\u201323). Latency-Aware Kubernetes Scheduling for Microservices Orchestration at the Edge. Proceedings of the 2023 IEEE 9th International Conference on Network Softwarization (NetSoft), Madrid, Spain.","DOI":"10.1109\/NetSoft57336.2023.10175431"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Santos, J., Wauters, T., Volckaert, B., and Turck, F.D. (2019, January 24\u201328). Towards Network-Aware Resource Provisioning in Kubernetes for Fog Computing Applications. Proceedings of the 2019 IEEE Conference on Network Softwarization (NetSoft), Paris, France.","DOI":"10.1109\/NETSOFT.2019.8806671"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pusztai, T.W., Rossi, F., and Dustdar, S. (2021, January 5\u201310). Pogonip: Scheduling Asynchronous Applications on the Edge. Proceedings of the 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), Chicago, IL, USA.","DOI":"10.1109\/CLOUD53861.2021.00085"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105192","DOI":"10.1109\/ACCESS.2021.3100082","article-title":"Zeus: Improving Resource Efficiency via Workload Colocation for Massive Kubernetes Clusters","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"83088","DOI":"10.1109\/ACCESS.2019.2924414","article-title":"Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud","volume":"7","author":"Lin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","first-page":"138","article-title":"Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges","volume":"55","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_9","unstructured":"Zhang, W.-G., Ma, X.-L., and Zhang, J.-Z. (2018, January 2\u20134). Research on Kubernetes\u2019 Resource Scheduling Scheme. Proceedings of the 8th International Conference on Communication and Network Security, Qingdao, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1186\/s13677-023-00471-1","article-title":"A survey of Kubernetes scheduling algorithms","volume":"12","author":"Senjab","year":"2023","journal-title":"J. Cloud Comput."},{"key":"ref_11","first-page":"151","article-title":"Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches","volume":"55","author":"Rejiba","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5605","DOI":"10.1007\/s11831-022-09778-9","article-title":"A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future","volume":"29","author":"Sharma","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11047-008-9098-4","article-title":"A survey on metaheuristics for stochastic combinatorial optimization","volume":"8","author":"Bianchi","year":"2009","journal-title":"Nat. Comput."},{"key":"ref_14","first-page":"403","article-title":"A Task Scheduling Based on Simulated Annealing Algorithm in Cloud Computing","volume":"9","author":"Liu","year":"2016","journal-title":"Int. J. Hybrid Inf. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Feller, E., Rilling, L., and Morin, C. (2011, January 21\u201323). Energy-Aware Ant Colony Based Workload Placement in Clouds. Proceedings of the 2011 IEEE\/ACM 12th International Conference on Grid Computing, Lyon, France.","DOI":"10.1109\/Grid.2011.13"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10723-017-9419-x","article-title":"Genetic Algorithm for Multi-Objective Optimization of Container Allocation in Cloud Architecture","volume":"16","author":"Guerrero","year":"2017","journal-title":"J. Grid Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yan, B., Feng, Z., Zhang, C., and Wang, Y.-X. (2017, January 21\u201323). Container oriented job scheduling using linear programming model. Proceedings of the 2017 3rd International Conference on Information Management (ICIM), Chengdu, China.","DOI":"10.1109\/INFOMAN.2017.7950370"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rodrigues, L.R., Pasin, M., Alves, O.C., Miers, C.C., Pillon, M.A., Felber, P., and Koslovski, G.P. (2019, January 9\u201313). Network-Aware Container Scheduling in Multi-Tenant Data Center. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013128"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huang, W., Zhou, J., and Zhang, D. (2021). On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes. Sensors, 21.","DOI":"10.3390\/s21092971"},{"key":"ref_20","unstructured":"(2025, January 20). Kubernetes-Sigs\/Scheduler-Plugins: Repository for Out-of-Tree Scheduler Plugins Based on Scheduler Framework. Available online: https:\/\/github.com\/kubernetes-sigs\/scheduler-plugins."},{"key":"ref_21","unstructured":"(2025, January 20). Scheduling Framework|Kubernetes. Available online: https:\/\/kubernetes.io\/docs\/concepts\/scheduling-eviction\/scheduling-framework\/."},{"key":"ref_22","unstructured":"(2025, January 20). kubernetes-Sigs\/Kube-Scheduler-Simulator: The Simulator for the Kubernetes Scheduler. Available online: https:\/\/github.com\/kubernetes-sigs\/kube-scheduler-simulator\/."},{"key":"ref_23","unstructured":"(2025, January 20). kubernetes-Sigs\/Kwok: Kubernetes Without Kubelet\u2014Simulates Thousands of Nodes and Clusters. Available online: https:\/\/github.com\/kubernetes-sigs\/kwok."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/114\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:57:42Z","timestamp":1760029062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,21]]},"references-count":23,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["computers14040114"],"URL":"https:\/\/doi.org\/10.3390\/computers14040114","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,21]]}}}