SD-CPS: Taming the Challenges of Cyber-Physical Systems with a Software-Defined Approach

SD-CPS: Taming the Challenges of
Cyber-Physical Systems
with a Software-Defined Approach
Pradeeban Kathiravelu1,2, Lu´ıs Veiga1
1Instituto Superior Técnico, Universidade de Lisboa, Portugal
2Université catholique de Louvain, Louvain-la-Neuve, Belgium
4th IEEE International Conference on Software Defined Systems (SDS 2017).
May 8 - 11, 2017. Valencia, Spain.
Pradeeban Kathiravelu (IST-ULisboa) SD-CPS 1 / 24

Introduction
Cyber-Physical System (CPS)
A system composed of cyber and physical/mechanical elements.
Internet of Things (IoT).
Industry 4.0.
1
1
Christoph Roser at AllAboutLean.com

Introduction
Challenges in CPS
Modelling and designing the systems.
Management and orchestration of the intelligent agents.
Large-scale heterogeneous execution environments.
Unpredictable.
Decision making: communication and coordination.
Security and distributed fault-tolerance.
Recovery upon system and network failures.

Motivation
Motivation
A novel approach to address the challenges of CPS.

Motivation
Motivation
A novel approach to address the challenges of CPS.
Desired Properties in a new CPS Approach
Easy to adopt from current CPS approaches.
Should not introduce more/new challenges.

Motivation
Our Proposal!
Software-Deﬁned Cyber-Physical Systems (SD-CPS)

Motivation
Our Proposal!
Why it is?
How it is?

Motivation
Our Proposal!
An architectural enhancement to tackle the core
challenges of CPS.
Bring SDN to CPS!
A middleware framework for CPS.
Inspired by the centralized control oﬀered by SDN.

Motivation
Contributions of SD-CPS
A dual (physical and virtual/cyber) execution environment.
An efficient approach to CPS modelling and simulations.
Mitigate the unpredictability of the physical execution environment.
Enhanced communication and coordination of autonomous agents.
Virtually separated control and data flows.
A clear logical separation of devices from their execution.
Resilience for critical flows with a differentiated QoS.
End-to-end delivery guarantees.

Architecture
SD-CPS Controller Architecture

Architecture
SD-CPS
Taming the Challenges of CPS with SD-CPS!

Architecture
1. Controlling the CPS Data Plane

Architecture
2. Scaling the CPS with SD-CPS
Controller Farm: A federated deployment of SDN controllers.
SDN + Message-Oriented Middleware.
Protected access among controller clusters in wide area.
Scaling the control plane - Flat; without a hierarchy.
Software-Deﬁned Sensor Networks.
Extend SDN for sensor networks.
SDN with sensors and actuators.

Architecture
3. Modelling and Simulating CPS
Cyberspace to model the smart devices as virtual intelligent agents.
Interactions between the actors in physical and cyber spaces mapped.
Incrementally model and load from the controller farm.

Architecture
3. Modelling and Simulating CPS
Orchestrator: Build once, deploy and execute twice (or more).
Software-Deﬁned Simulations ⇒
Software-Deﬁned Service Compositions.
Tenant-Aware Virtual Network Allocation.
Multiple isolated virtual execution environments.

Architecture
4. Resilience in SD-CPS

Architecture
Case 1: Sending the subﬂow to the destination node

Architecture
Case 2: Sending the subﬂow to an intermediate node

Architecture
5. Security in SD-CPS
Distributed fault tolerance and recovery upon system failures.
Diﬀerentiated bandwidth provisioning with QoS awareness.
Global knowledge of CPS to the controller.
Controller as the most valuable entity in the CPS.
Avoid introducing vulnerabilities.
Security researches on SDN and CPS are applicable.

Implementation
Prototype Implementation and Feasibility Assessment
Assessments with simulations and microbenchmarks.
Increased controller performance.
CHIEF [SDS’16] as the controller farm.
based on OpenDaylight, AMQP, . . ..
Increased orchestration/centralized management capabilities.
Cassowary [M4IoT’15] for Software-Defined Sensor Networks.
Reduced Modelling Time.
Dual reality of cyber-physical spaces for simulations and designs.
SDNSim [CoopIS’16] for Software-Defined Simulations.
SENDIM [IC2E’16] to offer the orchestration capabilities.
Performance growth = f(problem size, workflow as services).
Mayan [ICWS’16] to offer Software-Defined Service Compositions.

Conclusion
Related Work
SDN for Heterogeneous Devices.
Sensor OpenFlow (Software-Defined Wireless Sensor Networks).
SDN + WSN = SD-WSN.
Scaling SDN.
Federated Controller Deployments
Clustering SDN controller with Akka.
OpenDaylight Federation and Conceptual Data Tree projects.
Software-Defined Systems for Smart Environments.
Software-Defined Buildings (SDB).
Software-Defined Environments (SDE).
Software-Defined IoT
SDSecurity + SDStorage + SDN + . . . = SDIoT.
Taming the challenges of distributed systems.
Albatross.

Conclusion
Conclusion and Future Work
Summary
An approach to mitigate the design and operations challenges in CPS.
Benefits of SDN to CPS.
Unified and centralized control.
Improved QoS, management, and resilience.
Reduced repeated effort in modelling.

Conclusion
Summary
Future Work
Increased resource eﬃciency using locality data in the Internet scale.
Metering and billing the tenant resource consumption in CPS.
Securing SD-CPS in the presence of malicious participants.

Conclusion
Summary
Future Work
Increased resource eﬃciency using locality data in the Internet scale.
Metering and billing the tenant resource consumption in CPS.
Securing SD-CPS in the presence of malicious participants.
Thank you! Questions?

SD-CPS: Taming the Challenges of Cyber-Physical Systems with a Software-Defined Approach

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