The digital transformation of industry requires industrial control networks provide high flexibility and determinacy. Time-Sensitive Software-Defined Networking (TSSDN) that combines Time-Sensitive Networking (TSN) and Software-Defined Networking (SDN) is a new network paradigm which provides both real-time transmission feature and network flexibility. During network updates, the transmission consistency needs to be maintained. However, previous mechanisms mostly target on the proper schedule transition, which cannot guarantee no frame loss and also introduces extra update overhead. The paper proposes a novel flow schedule generation model which guarantees no frame loss during network updates even with the basic two-phase update mechanism and introduces no extra update overhead. Two algorithms are designed for the model to adapt to different application scenarios: the offline algorithm poses better schedulability, whereas the online one consumes less time with slightly decreased schedulability. The experiments on two real-world industrial networks demonstrate our mechanism achieves zero frame loss without extra update overhead compared to existing methods, and the online algorithm saves 40% execution time with at most 10% schedulability decrease when the bandwidth utilization is less than 50%.

Emerging industrial applications involving robotic collaborative operations and mobile robots require a more reliable and precise wireless network for deterministic data transmission. To meet this demand, the 3rd Generation Partnership Project (3GPP) is promoting the integration of 5th Generation Mobile Communication Technology (5G) and Time-Sensitive Networking (TSN). Time synchronization is essential for deterministic data transmission. Based on the 3GPP’s vision of the 5G and TSN integrated networking with interoperability, we improve the time synchronization of TSN to conquer the multi-gNB competition, re-transmission, and mobility problems for the integrated 5G time synchronization. We implemented the improvement mechanisms and systematically validated the performance of 5G+TSN time synchronization. Based on the simulation in 500m x 500m industrial environments, the improved time synchronization achieved a precision of 1 microsecond with interoperability between 5G nodes and TSN nodes.

Distributed real-time systems (DRTS) composed of many embedded end-systems have been widely adopted in the industrial fields. Time-sensitive Networking (TSN), as a promising communication infrastructure for DRTS, has shown great potential in industry and academia. TSN assumes that an end-system can release critical tasks to process critical packets strictly according to the pre-scheduled time. Unfortunately, two factors currently damage this assumption: (1) the jitter caused by system architecture during task release, task execution and packet transmission; (2) a TSN schedule result may exceed the execution capability of the end-system and cause conflicts. This paper proposed DetChip, a system-on-chip capable of deterministically implementing the TSN schedule result. DetChip supports time-triggered task release, time-predictable task execution and precise network transmission. Based on DetChip, this paper first formalizes the execution capability of the end-system as end-system constraints (ECs). Existing TSN scheduling algorithms integrating ECs can solve conflicts by co-scheduling end-systems and the TSN network. Compared with previous works, DetChip only introduces few clock cycles jitter for critical task execution according to the TSN schedule result. The proposed ECs obtain 2 8x more conflict-free solutions for advanced scheduling algorithms with a linear increase in time overhead. Compared with the general end-system, DetChip can reduce 6 20x processing jitter to achieve better clock synchronization.

Time-triggered (TT) network provides a low-cost service to meet the strong demand of modern industry networks for real-time communication. Both simulation and reachability analysis provide effective research methods for TT networks. This paper presents a verification framework for the TT network based on Timed Colored Petri Nets (TCPN). We propose an automatic formal modeling method for the behavior of message transmissions in the TT network. We harness timed multisets of TCPN to model and analyze time-triggered message transmission latencies. For a holistic system evaluation, we characterize the critical system properties such as boundedness and liveness. We substantiate the properties by the reachability analysis. We demonstrate the effectiveness through a case study by simulating the message transmission and reachability analysis in the state space. Finally, we analyze the influencing factors of the state space in the automotive scenario. The proposed automatic modeling method can effectively reduce the state space scale.

The pervasive deployment of cloud services poses an ever-increasing demand for cross-client deduplication solutions to save network bandwidth, lower storage costs, and improve backup speeds. However, existing solutions typically depend on lock based approaches relying on a centralized chunk database, which tends to hinder performance scalability. In this work, we present a new cross-client cloud backup solution, named Duplicacy, based on a Lock-Free Deduplication approach. Lock-Free Deduplication stores chunks to network or cloud storage using content hashes as file names. It then adopts a two-step fossil deletion algorithm to solve the hard problem of deleting unreferenced chunks in the presence of concurrent backups, without the need for any locks. Experiments demonstrate that Duplicacy enables significant performance improvement for backups over previous well-known backup tools. In addition, Duplicacy can work with many general-purpose network or cloud storage services which only support a basic set of file operations, and turn them into sophisticated deduplication-aware storage servers without server-side changes.