It enables what-if analysis by simulating IT services deployed on Kubernetes running in Compute Continuum-like scenarios.
KubeTwin builds digital replicas of Kubernetes-based systems, allowing users to simulate, analyze, and optimize their behavior without touching production environments.
Through its Digital Twin approach, KubeTwin helps you:
Simulate Kubernetes control loops, deployment, scheduling, and scaling
Explore “what-if” scenarios to validate configuration strategies
Optimize key metrics such as latency, cost, and energy efficiency
Troubleshoot and improve resilience before failures occur
Kubernetes is today’s de facto standard for deploying and managing IT services, powering everything from AWS, Azure, and Google Cloud to edge and IoT infrastructures.
Yet configuring these systems is complex and time-consuming.
KubeTwin simplifies this process by providing a safe, software-only environment where engineers can test configurations, anticipate issues, and achieve optimal performance.
Companies like Netflix use Chaos Engineering — deliberately injecting faults in production systems to test their resilience.
While effective, this method is risky and costly.
KubeTwin offers the same insights at a fraction of the risk: experiments happen on the digital twin, not on live systems.
Faults, workloads, and recovery strategies can be explored safely and efficiently.
KubeTwin is a collaborative effort involving:
Big Data & Compute Continuum Lab, University of Ferrara, Italy
University of Bologna, Italy
St. John’s University, NY, USA
IBM Consulting, NY, USA
Ghent University, Belgium
KubeTwin's current publications list:
D. Borsatti et al., "Modeling Digital Twins of Kubernetes-Based Applications," 2023 IEEE Symposium on Computers and Communications (ISCC), Gammarth, Tunisia, 2023, pp. 219-224, doi: 10.1109/ISCC58397.2023.10217853.
L. Manca et al., "Characterization of Microservice Response Time in Kubernetes: A Mixture Density Network Approach," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-9, doi: 10.23919/CNSM59352.2023.10327842.
D. Borsatti et al., "KubeTwin: A Digital Twin Framework for Kubernetes Deployments at Scale," in IEEE Transactions on Network and Service Management, vol. 21, no. 4, pp. 3889-3903, Aug. 2024, doi: 10.1109/TNSM.2024.3405175.
M. Zaccarini et al., "TELKA: Twin-Enhanced Learning for Kubernetes Applications," 2024 IEEE Symposium on Computers and Communications (ISCC), Paris, France, 2024, pp. 1-6, doi: 10.1109/ISCC61673.2024.10733736.
M. Zaccarini et al., "Chaos Engineering Based Kubernetes Pod Rescheduling Through Deep Sets and Reinforcement Learning," NOMS 2025-2025 IEEE Network Operations and Management Symposium, Honolulu, HI, USA, 2025, pp. 1-7, doi: 10.1109/NOMS57970.2025.11073590.
KubeTwin has received outstanding recognition across the Network and Service Management research community:
selected by the IEEE ComSoc Technical Committee on Network Operation and Management (CNOM)
2023 IFIP-supported Best Paper Award
at the 19th International Conference on Network and Service Management
(CNSM 2023), Niagara Falls, Canada
Awarded Paper: Characterization of Microservice Response Time in Kubernetes: A Mixture Density Network Approach
DOI: 10.23919/cnsm59352.2023.10327842
Authors:
Lorenzo Manca University of Bologna, Bologna, Italy
Davide Borsatti University of Bologna, Bologna, Italy
Filippo Poltronieri Big Data & Compute Continuum Lab, University of Ferrara, Ferrara, Italy
Mattia Zaccarini Big Data & Compute Continuum Lab, University of Ferrara, Ferrara, Italy
Domenico Scotece University of Bologna, Bologna, Italy
Gianluca Davoli University of Bologna, Bologna, Italy
Luca Foschini University of Bologna, Bologna, Italy
Genady Ya. Grabarnik Department of Mathematics and Computer Science, St. John's University, NY, USA
Larisa Shwartz HCS (Hybrid-Cloud Services) Department, IBM Consulting, NY, USA
Cesare Stefanelli Distributed Systems Research Group, University of Ferrara, Ferrara, Italy
Mauro Tortonesi Big Data & Compute Continuum Lab, University of Ferrara, Ferrara, Italy
Walter Cerroni University of Bologna, Bologna, Italy
Digital Twin · Kubernetes · Simulation · Configuration Optimization · Microservices · Orchestration · Troubleshooting · Chaos Engineering · Resilience · Scalability
GitHub Repository: github.com/DSG-UniFE/KubeTwin
Coming Soon: Official documentation and demo portal.