Virtualization is a technology that allows multiple virtual machines (VMs) to run on a single physical host, each with its own operating system and applications. Virtualization can provide many benefits for industrial control systems, such as reducing hardware costs, improving reliability, enhancing performance, and enabling data analysis. In this article, we will explore how virtualization can be applied to industrial control systems and what challenges and opportunities it presents.
What is Industrial Control System?
Industrial control system (ICS) is a general term that refers to the systems that monitor and control physical processes in various industries, such as manufacturing, energy, transportation, water, and healthcare. ICS typically consists of two main components: the information technology (IT) and the operational technology (OT).
The IT component includes the servers, computers, networks, and software that provide data acquisition, storage, analysis, and visualization functions. The OT component includes the sensors, actuators, controllers, and devices that interact with the physical processes and equipment. The IT and OT components are often connected by a heterogeneous network that may include wired, wireless, or cellular technologies.
Why Use Virtualization for Industrial Control System?
Virtualization can offer several advantages for industrial control systems, such as:
- Cost reduction: Virtualization can reduce the need for physical hardware, which can lower the capital expenditure, installation, and maintenance costs. For example, instead of having multiple dedicated servers for different control functions, a single server can host multiple VMs that perform the same functions. This can also save space, power, and cooling resources.
- Reliability improvement: Virtualization can improve the availability and fault tolerance of the control system by enabling features such as live migration, backup, and recovery. For example, if a physical host fails, the VMs running on it can be automatically migrated to another host without interrupting the control process. Similarly, if a VM becomes corrupted, it can be restored from a backup image or snapshot.
- Performance enhancement: Virtualization can enhance the performance and scalability of the control system by allowing dynamic allocation and optimization of the computing resources. For example, the VMs can be configured to use the optimal amount of CPU, memory, and network resources according to the workload and priority. Moreover, the VMs can be easily added or removed to meet the changing demand and capacity of the control system.
- Data analysis enablement: Virtualization can enable the integration and analysis of the data generated by the control system, which can provide valuable insights for improving the efficiency, quality, and safety of the industrial processes. For example, the data collected by the sensors and controllers can be stored and processed by the VMs running on the cloud or edge servers, which can apply advanced analytics and machine learning techniques to detect anomalies, optimize parameters, and predict outcomes.
How to Implement Virtualization for Industrial Control System?
Implementing virtualization for industrial control system requires careful planning and design, as well as addressing some technical and operational challenges, such as:
- Compatibility: The virtualization solution should be compatible with the existing hardware and software components of the control system, as well as the standards and protocols used by the industry. For example, the hypervisor, which is the software layer that enables virtualization, should support the operating systems and applications that run on the VMs, as well as the network interfaces and devices that connect to the physical processes. Moreover, the virtualization solution should comply with the industry-specific regulations and certifications, such as IEC 61508, IEC 62443, and ISA 99.
- Security: The virtualization solution should ensure the security and integrity of the control system, as well as the confidentiality and privacy of the data. For example, the hypervisor should isolate the VMs from each other and from the host, preventing unauthorized access and interference. Moreover, the virtualization solution should implement encryption, authentication, and authorization mechanisms to protect the data in transit and at rest, as well as the communication between the VMs and the physical processes.
- Latency: The virtualization solution should minimize the latency and jitter of the control system, which are the delays and variations in the data transmission and processing. For example, the hypervisor should prioritize the VMs that perform time-critical control functions, such as feedback loops and closed-loop control. Moreover, the virtualization solution should use low-latency and high-bandwidth network technologies, such as Ethernet, to connect the VMs and the physical processes.
What are the Examples of Virtualization for Industrial Control System?
Virtualization has been successfully applied to various industrial control systems, such as:
- Wind River Titanium Control: This is a secure, on-premise cloud infrastructure platform that provides industrial-grade virtualization for control systems. It supports features such as high availability, fault tolerance, live migration, backup, and recovery. It also enables data analysis and optimization using edge computing and machine learning. It has been used for applications such as power generation, smart grid, and industrial automation.
- Murillo: This is a virtual environment for industrial control systems that emulates the behavior of a non-linear plant and a networked control loop. It can be used for testing, training, and research purposes. It also implements an unknown input observer (UIO) that can protect the plant from harmful behavior, such as cyberattacks or faults.
- VMware vSphere: This is a general-purpose virtualization platform that can be used for industrial control systems. It supports features such as resource management, load balancing, and security. It also enables data analysis and visualization using cloud computing and big data. It has been used for applications such as oil and gas, mining, and manufacturing.