The rapid growth of large-scale Industrial Internet of Things (IIoT) drives the deployment of time-sensitive applications requiring stringent deterministic latency guarantees and high-throughput communication. Consequently, Large-Scale Deterministic Networking (LDN) and its variant, Advanced-LDN (A-LDN), have been proposed to achieve deterministic transmission by partitioning the timeline into fixed-length cycles and allocating sufficient resources for time-sensitive (TS) flows within each cycle. To reduce the complexity of resource reservation decisions, LDN adopts a uniform resource reservation pattern (RRP) that allocates identical resources in each cycle, while A-LDN uses a periodic RRP that reserves resources at fixed cycle intervals. However, these simplified RRPs lead to excessive over-provisioning and fail to meet the high-throughput demands of IIoT applications. To address this, we propose a flexible LDN (Flex-LDN) mechanism that supports arbitrary RRPs and minimizes resource reservation for TS flows. First, due to the lack of an analytical method to establish upper bounds of shaping delays for arbitrary RRPs, Flex-LDN constructs a service curve model and calculates delay bounds using network calculus. Furthermore, since shaping delay budgets vary for different paths of a TS flow, the required resource reservation to ensure deterministic latency and jitter bounds also differs. Therefore, we design a load-balancing-based RRP decision algorithm and a transmission strategy space generation algorithm to assign RRPs to each path, thereby constructing a transmission strategy space that consists of RRPs and paths for each TS flow. The RRPs generated by these algorithms minimize resource reservation while satisfying the shaping delay budgets. This approach ensures minimal resource allocation during TS flow scheduling. Finally, to efficiently solve the TS flow scheduling problem, we model it as an Exact Potential Game (EPG) and propose a distributed Ordered Best-Response (OBR) algorithm that can converge to a Nash equilibrium in polynomial time. Simulation results show that Flex-LDN improves the TS flow scheduling success ratio by 62.2% and 24.5% over LDN and A-LDN, respectively, while reducing scheduling time by 72.5% compared to A-LDN.

Deterministic IP (DIP) networking is a time division duplex technique providing delay-bounded transmissions in large-scale networks. Dedicated resources are allocated to each time-sensitive (TS) flow, reducing queuing delay uncertainties. However, most research relies on offline scheduling, unfit for dynamic networks. Some online heuristics prioritize early flows, risking later traffic access issues. To tackle this, we propose an online algorithm using historical data for better foresight. We develop and analyze a competitive algorithm considering historical data, demonstrating its effectiveness through simulations.

IEEE 802.1Qbv (TAS) is the most widely used technique in Time-Sensitive Networking (TSN) which aims to provide bounded transmission delays and ultra-low jitters in industrial local area networks. With the development of emerging technologies (e.g., cloud computing), many wide-range time-sensitive network services emerge, such as factory automation, connected vehicles, and smart grids. Nevertheless, TAS is a Layer 2 technique for local networks, and cannot provide large-scale deterministic transmission. To tackle this problem, this paper proposes a hierarchical network containing access networks and a core network. Access networks perform TAS to aggregate time-sensitive traffic. In the core network, we exploit DIP (a well-known deterministic networking mechanism for backbone networks) to achieve long-distance deterministic transmission. Due to the differences between TAS and DIP, we design cross-domain transmission mechanisms at the edge of access networks and the core network to achieve seamless deterministic transmission. We also formulate the end-to-end scheduling to maximize the amount of accepted time-sensitive traffic. Experimental simulations show that the proposed network can achieve end-to-end deterministic transmission even in high-loaded scenarios.

Deterministic IP (DIP) networking is a promising technique that can provide delay-bounded transmission in large-scale networks. Nevertheless, DIP faces several challenges in the mixed traffic scenarios, including (i) the capability of ultralow latency communications, (ii) the simultaneous satisfaction of diverse QoS requirements, and (iii) the network efficiency. The problems are more formidable in the dynamic surroundings without prior knowledge of traffic demands. To address the above-mentioned issues, this paper designs a flexible DIP (FDIP) network. In the proposed network, we classify the queues at the output port into multiple groups. Each group operates with different cycle lengths. FDIP can assign the time-sensitive flows with different groups, hence delivering diverse QoS requirements, simultaneously. The ultra-low latency communication can be achieved by specific groups with short cycle lengths. Moreover, the flexible scheduling with diverse cycle lengths improves resource utilization, hence increasing the throughput (i.e., the number of acceptable time-sensitive flows). We formulate a throughput maximization problem that jointly considers the admission control, transmission path selection, and cycle length assignment A branch and bound (BnB)-based heuristic is developed. Simulation results show that the proposed FDIP significantly outperforms the standard DIP in terms of both the throughput and the latency guarantees.

With the development of 5G, innovative applications requiring bounded transmission delays and zero packet loss emerge, e.g., AR, industrial automation, and smart grid. In this circumstance, time-sensitive networking (TSN) is proposed, which addresses the deterministic transmission in the local area networks. Nevertheless, TSN is essentially a Layer 2 technique, which cannot provide deterministic transmission on a large geographic area. To solve this problem, this paper proposes a hierarchical network for the end-to-end deterministic transmission. In the proposed network, we leverage CQF (i.e., one of the most efficient TSN mechanisms) in the access networks which aggregates the traffic from end-devices. Meanwhile, in the core network, we exploit the DIP (i.e., a well-known deterministic networking mechanism for backbone networks) for long-distance deterministic transmission. We design the cycle alignment mechanism to enable seamless and deterministic transmission among hierarchical networks. A joint schedule is also formulated, which introduces the traffic shaping at the network edge to maximize the network throughput. Experimental simulations show that the proposed network can achieve end-to-end deterministic transmission, even in the highly-load scenarios.

Mobile edge computing (MEC) is proposed to boost high-efficient and time-sensitive 5G applications. However, the "microburst" may occur even in lightly-loaded scenarios, which leads to the indeterministic service latency, hence hindering the deployment of MEC. Deterministic IP networking (DIP) has been proposed to provide bounds on latency, and high reliability in the large-scale networks. Nevertheless, the direct migration of DIP into the MEC network is non-trivial owing to its original design for the Ethernet with homogeneous devices. Meanwhile, DIP also faces the challenges on the network throughput and scheduling flexibility. In this paper, we delve into the adoption of DIP for the MEC networks and some of the relevant aspects. A deterministic MEC (D-MEC) network is proposed to deliver the deterministic MEC service. In the D-MEC network, the cycle mapping and cycle shifting are designed to enable: (i) seamless and deterministic transmission with heterogeneous underlaid resources; and (ii) traffic shaping on the edges to improve the resource utilization. We also formulate a joint configuration to maximize the network throughput with deterministic QoS guarantees. Extensive simulations verify that the proposed D-MEC network can achieve a deterministic MEC service, even in the highly-loaded scenarios.