Date of Award
Doctor of Philosophy (PhD)
This dissertation contributes to the area of adaptive real-time and fault-tolerant systems research, applied to Industrial Internet-of-Things (IIoT) systems. Heterogeneous timing and reliability requirements arising from IIoT applications have posed challenges for IIoT services to efficiently differentiate and meet such requirements. Specifically, IIoT services must both differentiate processing according to applications' timing requirements (including latency, event freshness, and relative consistency of each other) and enforce the needed levels of assurance for data delivery (even as far as ensuring zero data loss). It is nontrivial for an IIoT service to efficiently differentiate such heterogeneous IIoT timing/reliability requirements to fit each application, especially when facing increasingly large data traffic and when common fault-tolerant mechanisms tend to introduce latency and latency jitters.
This dissertation presents a new adaptive real-time fault-tolerant framework for IIoT systems, along with efficient and adaptive strategies to meet each IIoT application's timing/reliability requirements. The contributions of the framework are demonstrated by three new IIoT middleware services: (1) Cyber-Physical Event Processing (CPEP), which both differentiates application-specific latency requirements and enforces cyber-physical timing constraints, by prioritizing, sharing, and shedding event processing. (2) Fault-Tolerant Real-Time Messaging (FRAME), which integrates real-time capabilities with a primary-backup replication system, to fit each application's unique timing and loss-tolerance requirements. (3) Adaptive Real-Time Reliable Edge Computing (ARREC), which leverages heterogeneous loss-tolerance requirements and their different temporal laxities, to perform selective and lazy (yet timely) data replication, thus allowing the system to meet needed levels of loss-tolerance while reducing both the latency and bandwidth penalties that are typical of fault-tolerant sub-systems.
Christopher Gill Chenyang Lu
Kunal Agrawal, Sanjoy Baruah, Jing Li,