Network failures in Artificial Intelligence (AI) training clusters can degrade entire jobs, making fast and accurate fault localization critical. Existing active probing systems suffer from two fundamental limitations: probabilistic path coverage that cannot guarantee complete link observability, and binary anomaly detection that fails to distinguish concurrent failures or localize gray failures. Equal-Cost Multi-Path (ECMP) routing is deterministic given the same 5-tuple, and ECMP configurations are accessible in operator-controlled clusters. We exploit this property to derive exact probe paths through offline hash computation without network measurement. Based on this approach, we design a hash-aware probing system that constructs a deterministic coverage matrix to select minimal probes guaranteeing complete link coverage. We introduce edge signatures to ensure fault distinguishability and a tiered diagnosis approach where lightweight iterative localization handles hard failures while sparse regression localizes gray failures. Preliminary evaluations on fat-tree topologies with up to 10,240 hosts show that our system achieves 100% link coverage with 39× fewer probes than R-Pingmesh, F1 score of 0.75–0.92 for multi-link failures, and 0.67 F1 for gray failures where existing methods fail entirely.

Due to the high operational complexity and limited deployment scale of lossless RDMA networks, the community has been exploring efficient RDMA communication over lossy fabrics. State-of-the-art (SOTA) lossy RDMA solutions implement a simplified selective repeat mechanism in RDMA NICs (RNICs) to enhance loss recovery efficiency. However, these solutions still face performance challenges, such as unavoidable ECMP hash collisions and excessive retransmission timeouts (RTOs). In this paper, we revisit RDMA reliability with the goals of being independent of PFC, compatible with packet-level load balancing, free from RTO, and friendly to hardware offloading. To this end, we propose DCP, a transport architecture that co-designs both the switch and RNICs, fully meeting the design goals. At its core, DCP-Switch introduces a simple yet effective lossless control plane, which is leveraged by DCP-RNIC to enhance reliability support for high-speed lossy fabrics, primarily including header-only-based retransmission and bitmap-free packet tracking. We prototype DCP-Switch using P4 switch and DCP-RNIC using FPGA. Extensive experiments demonstrate that DCP achieves 1.6× and 2.1× performance improvements, compared to SOTA lossless and lossy RDMA solutions, respectively.

In a hybrid scenario of 5G and Industrial Internet of Things (IIoT), there is a lack of a theoretical tool to analyze probabilistic end-to-end (E2E) delay. In this article, we provide a comprehensive procedure, which is based on stochastic network calculus (SNC) with moment-generating functions (MGFs), for calculating the E2E delay violation probability for the target traffic in IIoT. The particularity of the scenario is that the E2E network is composed of two segments: 1) the industrial wireless link challenged by the complex fading channel and 2) the multinode wired network that supports common schedulers. An improved concatenation theorem is proposed to calculate the service capability of the E2E network, and a method based on Meijer G-functions is proposed to calculate the MGF for the service processes of various wireless fading channels. We investigate the impacts of various resource allocation strategies (on both wireless and wired networks) and parameters (e.g., bandwidth, weight, and cycle time) on probabilistic E2E delay and provide numerical performance bounds. We show that the capability joint adaptation of the wireless and wired networks is the key to E2E service guarantee. Moreover, related parameters such as the weights and message sizes should be carefully considered to improve E2E delay.

End-to-end (E2E) reliability provisioning is critical for network slice customization. Leveraging martingale theory, we construct an analysis framework of the reliability with regard to E2E latency for the multihop system, where service processes provided by wireless nodes and networking nodes are heterogeneous. Based on the Wald martingale constructions of arrival processes and service processes, tandem service descriptor is defined to embody the features of tandem service mode for the targeted flow. Arrival bias and service bias are derived to describe the fluctuations of arrival and service processes, respectively. Relying on the Doob maximum inequality of martingales, a tight upper bound of the unreliability regard to E2E latency is captured. Packet duplication and hot-backup parallel transmission patterns are adopted for network reliability enhancement. A reliability decomposition and bandwidth abstraction algorithm is proposed, which achieves the decoupling between the statistical reliability requirement and the desired bandwidth of each node. Slice instantiation is performed in the access network and the core network, respectively. In the wireless access network, a long-term stochastic optimization problem is formulated with reliability requirements constraint. Lyapunov drift-plus-penalty (DPP) optimization theory is explored to transform the intractable long-term optimization problem into dynamic evolutionary per-slot optimization problems. We yield the closed-form solutions of user scheduling and power allocation. In the core network, the forwarding rates in the networking nodes for the targeted flow are obtained.

This paper presents a study of the optimal Admission Control in Damper-based Networks (ACDN) problem. The use of dampers in large-scale networks is becoming increasingly beneficial for a wide range of applications as it provides a reliable means of achieving deterministic delay guarantees without the need for synchronization between routers. In this context, optimal admission control solutions are required to fully utilize capacity. The problem being studied is a variant of the Unsplittable Multi-Commodity Flow (UMCF) problem, with additional constraints related to forwarding and shaping. This paper proposes two Integer Linear Programming (ILP) formulations to address the ACDN problem. The former is a compact formulation, which is solved using the CPLEX solver. The latter is an extended path formulation, for which a Branch-and-Price algorithm is developed, including a column generation procedure, an efficient branching scheme, and reinforced by a primal heuristic. Tests on realistic instances show that solving the path formulation using the Branch-and-Price algorithm is better than solving the compact formulation using CPLEX. Our algorithm divides by 14 the average running time given by CPLEX, and the path formulation gives a stronger linear relaxation with an average optimally gap of 0.3%. This work builds upon previous research [1] that developed a heuristic for finding near-optimal solutions, and instead aims to find exact optimal solutions for ACDN.

With the prosperity of emerging applications, the$6^{th}$Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.

With 5G networking, deterministic guarantees are emerging as a key enabler. In this context, we present a scalable Damper-based architecture for Large-scale Deterministic IP Networks (D-LDN) that meets required bounds on end-to-end delay and jitter. This work extends the original LDN [1] architecture, where flows are shaped at ingress gateways and scheduled for transmission at each link using an asynchronous and cyclic opening of gate-controlled queues. To further relax the need for clock synchronization between devices, we use dampers, that consist in jitter regulators, to control the burstiness flows to provide a constant target delay at each hop. We introduce in details how data plane functionalities are implemented at all nodes (gateways and core) and we derive how the end-to-end delay and jitter are calculated. For the control plane, we propose a column generation algorithm to quickly take admission control decisions and maximize the accepted throughput. For a set of flows, it determines acceptance and selects the best shaping and routing policy. Through a proof-of-concept implementation in simulation, we verify that the architecture meets promised guarantees and that the control plane can operate efficiently at large-scale.

Earliest Deadline First (EDF) scheduling, is known to obtain the optimality of deterministic delay performance upon the single link. However, due to the generality of the network with multiple nodes and multiple flows transmitted within it, the optimal online scheduling is impossible. In this paper, we prove that EDF can still provide the optimal delay performance in a class of practical multiple-nodes-and-multiple-flows network scenarios, such as 5G Cloud VR/AR applications in up-link cases, namely in sink-tree networks multiple flows sharing the root node as the same destination node. The proof uses the delay-based schedulability region to quantify the performance of different scheduling policies. Finally, the proof indicates that EDF can achieve the largest schedulability region in sink-tree networks.

Deterministic performance is a key enabler for 5G networking. In this context, we present a highly scalable Large-scale Deterministic Network (LDN) architecture providing end-to-end latency and bounded jitter guarantees in IP networks. At the data plane, flows are first shaped at ingress gateways using gate-control queues, achieving a very fine granularity compared to existing state of the art solutions. Inside the network, traffic is scheduled using an asynchronous cyclic queuing mechanism that can be implemented in real devices as it requires only 3 FIFO queues. The data plane relies on standard IP routing and a quasi-static mapping table to deterministically aggregate and forward packets over cycles with a low complexity in O(1). For the control plane, we present an advanced column generation algorithm to quickly take admission control decisions in large-scale networks. For a set of flows, it determines acceptance and selects the best shaping and routing policy. Through a proof-of-concept implementation and simulations, we show that our LDN architecture can guarantee end-to-end latency and bounded jitter. We also demonstrate that our advanced control plane algorithm brings an improvement up to 40% in terms of accepted traffic over classical routing.

In the inter-domain routing infrastructure, a route leak is defined as a violation of the routing policy agreed between two Autonomous Systems (AS). Route leaks have resulted in large-scale outages on the Internet, taking down several services. Although route leaks seem a simple problem, the solution is complex because: (i) ASes consider -partially- routing policy private, (ii) lack of a formal and standard language to express routing policy and (iii) BGP lacks adequate cryptographic-based security. In this paper, we present an experimental analysis of a distributed ledger-based architecture that provides a solution to route leaks. Specifically, the routing policy is unambiguously expressed using a formal language, that is then stored in a blockchain. This decentralized architecture allows private policies and interfaces seamlessly with the current BGP infrastructure, requiring no changes to routers. We build a prototype to evaluate our proposed architecture using Hyperledger, we analyze its performance using a real-world BGP dataset. Our results show that our architecture scales linearly with relevant metrics. Additionally, we validate the architecture preventing an artificially introduced route leak in a realistic 10 AS topology.

The Domain Name System (DNS) is one of the most fundamental infrastructures of the Internet. However, due to its design philosophy and implementation architecture, the current DNS still suffers from the centralization problem and the data authenticity problem. In this paper, we analyze these two problems and propose a blockchain-based naming system called blockDNS to solve them simultaneously. In blockDNS, domain names can be applied and transferred freely in a decentralized way. Moreover, a lightweight verification mechanism is also proposed coupled with blockDNS. The verification mechanism allows website clients to verify the authenticity of resolution results with few overheads. Simulation results show that, compared to the Simplified Payment Verification method, blockDNS can cut down the overheads for data authenticity verification from 4.955KBytes to 380Bytes. Moreover, we also present an implementation case of blockDNS, which is compatible with the current naming system and can be deployed incrementally.

Internet of Things (IoT) devices achieve the rapid development and have been widely deployed recently. Meanwhile, inherent vulnerabilities of IoT systems (including firmware and software) have been continually uncovered and thus the systems are always exposed to various attacks. The root cause of the issue is that IoT systems always have design flaws and implementation bugs. In particular, the released systems (e.g., by third-party marketplaces and IoT vendors) may be maliciously repackaged with malware. Unfortunately, IoT consumers are not able to effectively capture such vulnerabilities because of the limited detection capabilities. In this paper, we propose SmartCrowd, a blockchain-based platform that aims to outsource security detection of IoT systems to distributed detectors with strong detection incentives. SmartCrowd enables built-in accountability for IoT providers and authoritative references of detection results for IoT consumers. By building smart contracts, we can incentivize the efficient and high-coverage security detection of IoT systems, while providing decentralized and automated incentives for both IoT providers releasing secure IoT systems and detectors uncovering vulnerabilities. We present the security and theoretical analysis that demonstrates the security of SmartCrowd and the incentives for participators. We prototype SmartCrowd by using Ethereum and the experimental results show that SmartCrowd has both technical feasibility and financial benefits, which can be applied to build a secure IoT ecosystem.

The explosive growth of online videos brings a great pressure on LTE mobile networks caused by huge mobile data traffics. Deploying caches at the evolved packet core (EPC) and radio access network (RAN) in LTE mobile networks can efficiently relieve this pressure by the way of reducing duplicate content transmissions. In this paper, we propose collaborative caching algorithms for the LTE mobile network with caches deployed at the EPC and RAN to reduce the bandwidth cost of Internet access and improve the end-user experience. We decompose the caching problem into a content placement subproblem and a request routing subproblem. We solve the content placement subproblem with a greedy placement algorithm by utilizing its matroid and submodular properties. As for the request routing subproblem, we solve it by transforming it into the problem of maximizing a submodular function subject to a matroid constraint and present a greedy algorithm. Both the proposed algorithms can achieve at least 1=2 of the optimal solution. Experiment results show that our proposed algorithms can significantly improve the performance in terms of access cost and supported request compared with three reference algorithms.

Cooperation among mobile terminals (MTs) in heterogeneous wireless networks is currently widely investigated. Cooperation among MTs has a lot of advantages such as increasing network throughput, decreasing file download time, saving energy and so on. Since different cooperate MTs have different influence on quality of service and user's experience, MT selection becomes a key issue in MTs cooperation. To improve the quality of service and user's experience, we propose a novel mechanism for MT selection which we call NMC (Network MTs Cooperation) mechanism in this paper. In NMC mechanism, MTs and its access network are integrated as a virtual MT. Then we propose a Cooperative Terminal Selection (CTS) algorithm to select the optimal virtual MT. Simulation results show that NMC can help to save energy consumption of MTs as well as ensure the download rates.