Self-adaptive end-to-end resource management for real-time monitoring in cyber-physical systems

This paper proposes a novel self-adaptive resource management framework that preserves a low end-to-end monitoring delay in large-scale cyber-physical systems (CPS) while providing high monitoring resolution. According to the tradeoff relationship between the end-to-end delay and monitoring resolution, our proposed algorithm is designed inspired by Lyapunov optimization framework that is mathematically time-average utility optimal under delay/stability constraints. Our proposed Lyapunov optimization-based framework dynamically controls both sensing period on sensor nodes and analysis period on back-end server nodes based on the workload of network and CPU resources. Moreover, our framework does not require additional messages between sensor and server nodes. In our framework, each node autonomously recognizes the underuse or overuse of network and CPU resources with only local state information and self-adapts periods of sensing and analysis under Lyapunov optimization theory. Furthermore, our framework provides different priorities for classes of sensing data and adaptively limits periods of sensing and analysis of low-priority classes. Our proposed framework is designed and implemented on a well-known publish/subscribe (pub/sub) network protocol, Message Queuing Telemetry Transport (MQTT), and finally we can confirm that our proposed framework achieves low end-to-end monitoring delays and high monitoring resolutions.