Benchmarking Cloud-Based SCADA System
Abstract
Supervisory Control and Data Acquisition (SCADA) is a typical OT (Operational Technology) system that uses computers, networked data communications and graphical user interface for process supervisory management. SCADA systems are managing industrial processes or mission-critical infrastructure, and most installations are not making use of cloud-based infrastructure. In this paper, we present different scenarios for cloud-based SCADA and a framework for performance evaluation. Based on measurement results in different scenarios, we identify critical design issues, give recommendations how to address them, and demonstrate the general feasibility of cloud-based SCADA systems.
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Measurements of SSL VPN tunnels in cloud-based SCADA deployments show encryption adding about 1 ms to end-to-end latency, with most delays still in the 20-50 ms range; WAN distance and routing dominate over cryptographic cost.
“Measurements of SSL VPN tunnels in cloud-based SCADA deployments show encryption adding about 1 ms to end-to-end latency, with most delays still in the 20-50 ms range; WAN distance and routing dominate over cryptographic cost [34].”
From Encryption for Industrial Control Systems: A Survey of Application-Level and Network-Level Approaches in Smart Grids (2026) · cites this paper as [34]
Lightweight schemes such as YASIR et al. add only a few tens of byte times per message.
“while lightweight schemes such as YASIR et al. add only a few tens of byte times per message [34].”
From Encryption for Industrial Control Systems: A Survey of Application-Level and Network-Level Approaches in Smart Grids (2026) · cites this paper as [34]
A maximum CPU load factor of roughly 70-80% is an appropriate design criterion for control processing using real-time operating systems in PLCs.
“In the design of control processing using real-time operating systems in PLCs and the like, it has also been reported that a maximum CPU load factor of roughly 70-80% is an appropriate design criterion [30].”
From Asset Management Method of Industrial IoT Systems for Cyber-Security Countermeasures (2021) · cites this paper as [30]
Cites
Handschin, E., & Petroianu, A. I. (1991) · Electric energy systems and engineering series · doi:10.1007/978-3-642-84041-8 · Cited by 28 · see citing works
Church, P., Mueller, H., Ryan, C., Gogouvitis, S. V., Goscinski, A., Haitof, H., & Tari, Z. (2017) · Handbook of Big Data Technologies · doi:10.1007/978-3-319-49340-4_20 · Cited by 21 · see citing works
Self-citationPerformance Tuning Towards a KVM-based Embedded Real-time Virtualization System
Ma, R., Zhou, F., Zhu, E., et al. (2013) · doi:10.6688/jise.2013.29.5.13 · Cited by 12 · see citing works
Moving SCADA Systems to IaaS Clouds
Church, P. C., MUELLER, H. S., Ryan, C., et al. (2015) · doi:10.1109/smartcity.2015.186 · Cited by 12 · see citing works
Self-citationRequirement bottlenecks in a cloud based SCADA system
Šutić, D., & Atlagić, B. (2013) · International Convention on Information and Communication Technology, Electronics and Microelectronics · Cited by 3 · see citing works
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Asset Management Method of Industrial IoT Systems for Cyber-Security Countermeasures
Matsumoto, N., Fujita, J., Endoh, H., Yamada, T., Sawada, K., & Kaneko, O. (2021) · Information · doi:10.3390/info12110460 · Cited by 13 · see citing works
“In the design of control processing using real-time operating systems in PLCs and the like, it has also been reported that a maximum CPU load factor of roughly 70–80% is an appropriate design criterion [30].”
BackgroundNarayanan, M., Hafeez, M. A., & Munir, A. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6010011 · Cited by 3 · see citing works
- Authors
- Yi, M., Mueller, H., Yu, L., & Chuan, J.
- Affiliations
- Siemens (Germany) (DE)
- ORCID
- Mueller, Harald
- Venue
- 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
- Publisher
- IEEE
- Published
- 2017-12
- Pages
- 122-129
- OpenAlex
- W2781283530
- Cited by
- 14
- References
- 5
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 1 · 2018: 1
- FWCI
- 0.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Cloud Computing Technology and Science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Industrial Automation and Control Systems
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- supervisory control and data acquisition, operational technology, performance evaluation framework, industrial process monitoring, cloud computing, design challenges
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