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.

Network function virtualization (NFV) implements mobile edge computing (MEC) services as software appliances, and allows resources to be adaptively allocated to accommodate demand variations. Scalability and network cost (including operational cost and response latency) are key challenges. This paper presents a new game-theoretic approach to minimizing the network cost, where access points (APs) select MEC servers and routes in a decentralized manner, and unloaded routers and links are deactivated for cost saving. The key idea is that we interpret the minimization of network cost as a mixed game with a non-monotonic cost function capturing both the operational cost and response latency. We prove that the game is conditionally an ordinary potential game and converges to$\alpha $-approximate equilibriums. A closed-form expression is derived for the convergence delay. Another important aspect is that we integrate Stackelberg routing into the proposed mixed game to avoid inefficient equilibriums (with high cost or latency). We prove that the mixed game can converge faster to better equilibriums under linear response latency models. Extensive simulations corroborate the new game-theoretic approach can significantly outperform existing techniques in terms of efficiency, convergence, and scalability.

Recent proposals leverage Cyclic-Queuing and Forwarding (CQF) to achieve bounded-delay transmission for cyclic flows in Time-Sensitive Networking (TSN). However, the Ping-Pong queue-based model in CQF will introduce the jitter of two cycles, which is inapplicable to industrial automation scenarios where isochronous flows such as synchronized frames and motor control loops require zero jitter.We present TACQ, a first time-aware cyclic-queuing mechanism to support the co-transmission of cyclic flows and isochronous flows. Our key insight is that isochronous flows should enable zero-jitter while minimally impacting the bounded-delay of cyclic flows. To achieve this goal, we design a novel no-wait shaper (NWS) that handles isochronous flows with as little time-slot as possible. For cyclic flows, we extend the CQF to schedule flows with a double-closed state on the Tx-gate and compute the open time on the Rx-gate. Simulation results show that TACQ significantly reduces the delay of isochronous flows by 81.3% and achieves zero jitter compared with CQF. And the NWS effectively schedules 87.2% flows at 500 flows level in feasible execution time.

Recent proposals leverage Time-Aware Shaper (TAS) to achieve precise transmission in Time-Sensitive Networking (TSN). However, most of the proposals require the information of all time-triggered flows to be known in advance and synthesize the gate control list of each switch offline, making the mechanisms they designed inapplicable to industrial automation scenarios where the devices are changed dynamically and the flows should be scheduled online. In this paper, we propose an online routing and scheduling mechanism of TAS for time-sensitive networks. In order to maximize the number of schedulable flows and reduce bandwidth waste, we devise the variable time slot mechanism and minimize the sending start time of each flow. Based on these mechanisms, a novel incremental routing and scheduling (IRAS) algorithm is designed to achieve per-flow deployment, with a pre-routing algorithm to reduce synthesis time. The evaluations show that the IRAS algorithm approaches 96.5 % of the optimal solution in scheduling 2000 flows, and has a feasible per-flow computational time from sub-seconds to less than ten seconds.

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.

Compared with the conventional mobile edge cloud (MEC) network, the network function virtualization (NFV)-enabled MEC network provides new flexibility on the MEC service deployment. Resource wastage owing to dynamic workloads in traditional MEC networks can be overcome through adaptive resource allocation. In this paper, we investigate the resource allocation problem to minimize the operational cost (e.g., energy consumption, capital expenditure) and the average response time in the NFV-enabled MEC network. We consider the problem from the perspective of MEC service deployment, assignment, and routing among the access points (APs) and MEC servers. We propose an user-network cooperation-based algorithm with low-complexity. In the proposed algorithm, the network announces a path-switching rule (i.e., α-approximate deviation) with proportionally shared operational cost, while the APs selfishly choose their paths with the least cost accordingly. We analyze the selfish behaviors of APs with game theory. We prove existence and convergence of α-approximate equilibriums. Also, we evaluate the efficiency of the equilibriums with the price of stability (POS). Furthermore, an enhanced algorithm based on public service advertising (PSA) is proposed to improve the convergence performance and equilibriums efficiency. Through simulations, we show the superiority of the proposed algorithms over existing algorithms (e.g., BnB-SD and greedy routing) on the accuracy and convergence performance (measured by the overall path switching).

Mobile edge computing (MEC) is a promising technique which enables the user equipment (UE) to leverage the vast computation resources on the clouds (or cloudlets). The redundant design and dynamic nature of traffic raise an energy inefficiency issue in MEC network. In this paper, we aim to minimize the energy consumption and average response time in the MEC network. We jointly consider the cloud selection and routing optimization on both wired and wireless links. Based on the game theory, we propose a low-complex resource allocation algorithm, which can achieve the global optimal solution. Further, to reduce the number of re-routing (routing times), an improved algorithm is proposed, which introduces an approximate factor (i.e., β). The β represents the additional cost during the re-routing, such as session migrations, energy consumption. We demonstrate the convergence of the improved algorithm. The simulations show that the proposed algorithms outperform the other conventional algorithms.

The explosive growth of user traffic brings a great pressure on evolved packet core (EPC) caused by huge numbers of emerging mobile intelligent applications. An EPC with good flexibility and scalability is required. In this paper, we proposed a virtualized and programmable EPC architecture (NOS-EPC) by leveraging the framework of not only stack (NOS) framework in order to satisfy the stringent requirements in beyond 5G (B5G) network. The global controller (GC) and the global network view (GNV) are established for the NOS-EPC. The control plane (C-Plane), the user plane (U-Plane) and the management plane (M-Plane) for the NOS-EPC, which are mutual and decoupled, are realized. NS3 based simulations are performed to verify the performance of the NOS-EPC. We compare the proposed NOS-EPC with different LTE solutions, including the LTE/EPC and software- defined network based EPC (SDN- EPC). The results show that the NOS-EPC can efficiently improve the EPC performance on the aspects of procedure duration and signaling overheads.