The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Failover Timing Analysis in Orchestrating Container-based Critical Applications
Abstract
Emerging industrial applications require highly reconfigurable and flexible environments to effectively respond to dynamic market demands while complying with rigorous nonfunctional requirements. The orchestration of virtualized industrial components over edge/fog computing infrastructures allows the required reconfigurability to be achieved by enforcing automated management of hardware resources and applications. However, existing orchestration systems fall short of meeting crucial nonfunctional requirements, such as determinism, reliability, and application/criticality awareness, preventing their use in industrial environments. In this short paper, we conduct a preliminary timing analysis on failover mechanisms used in Kubernetes in order to identify sources of nondeterminism, which are fundamental to be mitigated in an industrial mixed-criticality scenario.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Second, at an implementation level, existing resilience solutions for the edge are either too heavyweight (container orchestration requiring significant resources [8,35]), too slow (cold standby with recovery times exceeding minutes [11,16]), or too narrow (sensor-level backups that cannot assume full node functionality [13]).”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [8]
In the citing paper
“Edge nodes—frequently implemented on commodity single-board computers—are vulnerable to faults arising from power instability, network disturbances, and adverse environmental conditions [7,8].”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [8]
In the citing paper
“faster than container orchestration (K8s 9.59 s [8], StarlingX 10.33 s [12])”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [8]
Citations
Cites
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276 · cited 1×
“0) because it allows flexibility, elasticity, resiliency, and reduced costs to be achieved [1]–[4].”
BackgroundContainer‐based cluster orchestration systems: A taxonomy and future directions
Rodriguez, M. A., & Buyya, R. (2018) · Software Practice and Experience · doi:10.1002/spe.2660 · Cited by 130
Key Characteristics of a Container Orchestration Platform to Enable a Modern Application
Khan, A. U. (2017) · IEEE Cloud Computing · doi:10.1109/mcc.2017.4250933 · Cited by 111
State machine replication in containers managed by Kubernetes
Netto, H. V., Lung, L. C., Correia, M. P., Luiz, A. F., & de Souza, L. M. S. (2016) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2016.12.007 · Cited by 84
Container-based real-time scheduling in the Linux kernel
Abeni, L., Balsini, A., & Cucinotta, T. (2019) · ACM SIGBED Review · doi:10.1145/3373400.3373405 · Cited by 82
Virtualizing mixed-criticality systems: A survey on industrial trends and issues
Cinque, M., Cotroneo, D., De Simone, L., & Rosiello, S. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.12.002 · Cited by 80
An Availability Analysis Approach for Deployment Configurations of Containers
Sebastio, S., Ghosh, R., & Mukherjee, T. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2788442 · Cited by 60
Vayghan, L. A., Saied, M. A., Toeroe, M., & Khendek, F. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110924 · Cited by 60
Rearchitecting Kubernetes for the Edge
Jeffery, A., Howard, H., & Mortier, R. (2021) · doi:10.1145/3434770.3459730 · Cited by 53
Lightweight Kubernetes Distributions: A Performance Comparison of MicroK8s, k3s, k0s, and Microshift
Koziolek, H., & Eskandani, N. (2023) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3578244.3583737 · Cited by 50
Research works printed but not linked: 2
- Kubernetes scalability and performance SLIs/SLOs
- Barletta, Achieving isolation in mixed-criticality industrial edge systems with real-time containers
Cited by
Sorted by citations
A Multi-Domain Survey on Time-Criticality in Cloud Computing
Andreoli, R., Mini, R. A. F., Skarin, P., Gustafsson, H., Harmatos, J., Abeni, L., & Cucinotta, T. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3539197 · Cited by 23
“The same authors provided also an interesting analysis [95] of the time needed to handle and repair detected faulty pods in Kubernetes.”
KubeFence: Security Hardening of the Kubernetes Attack Surface
Cesarano, C., & Natella, R. (2025) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn64029.2025.00054 · Cited by 6
“In contrast, KubeFence bridges this gap by dynamically enforcing workload-specific API policies at runtime, reducing the attack surface through fine-grained control of resource specifications.”
Neagu, M., Serban, C. M., Hangan, A., & Sebestyen, G. (2026) · Future Internet · doi:10.3390/fi18030137 · Cited by 3
PREEMPT-K8S: Pod Prioritization for Mixed-Criticality Edge-Cloud Services
Toscano, S., Simone, L. D., Barletta, M., & Cinque, M. (2025) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd67783.2025.00039 · Cited by 1
“For example, when a cluster event must be managed by a sequence of controller actions, in each of them, a request can suffer from queuing delay and resource contention due to the other control loop threads [19].”
Self-citationA Systematic Mapping Study of Container-Based Virtualization Technologies
Arias-Pinzón, J. A., Correa-Urbano, A. H., Rodríguez, L. E. S., & Uribe, C. A. C. (2026) · Journal of Internet Services and Applications · doi:10.5753/jisa.2026.6589
Metadata
- Authors
- Barletta, M., De Simone, L., Corte, R. D., & Di Martino, C.
- Affiliations
- University of Naples Federico II (IT)
- DIETI - Università degli Studi di Napoli Federico II,Napoli,Italy,80125
- Nokia Bell Labs,USA
- Venue
- 2024 19th European Dependable Computing Conference (EDCC)
- Publisher
- IEEE
- Published
- 2024-04-08
- Pages
- 81-84
- OpenAlex
- W4398199936
- Cited by
- 5
- References
- 22
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 1.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- European Dependable Computing Conference (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Real-Time Systems Scheduling
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- failover mechanisms, Kubernetes, timing analysis, non-determinism, mixed-criticality systems, container orchestration, edge and fog computing, industrial applications, non-functional requirements, determinism, reliability, critical applications
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Failover Timing Analysis in Orchestrating Container-based Critical Applications. https://app.cochat.ai/papers/10.1109/edcc61798.2024.00026
- The paper
- Barletta, M., De Simone, L., Corte, R. D., & Di Martino, C. (2024). Failover Timing Analysis in Orchestrating Container-based Critical Applications. 2024 19th European Dependable Computing Conference (EDCC). https://doi.org/10.1109/edcc61798.2024.00026
