Node-centric Data Centers (NDCs) are highly flexible, cost-efficient, and failure-resilient, and have gained growing popularity in recent years. However, RDMA technology used in NDC still faces challenges, including high retransmission overhead, Head-of-Line Blocking (HoLB) and deadlock problems. Existing solutions for traditional data centers cannot simultaneously address these issues due to the unique topology and server transmission characteristics of NDC. In this paper, we propose a per-port flow control named PortFC for NDC. PortFC addresses the above problems through the designs of a Pause/Resume control signal, a per-port queue allocation method, an egress-detecting per-port flow control mechanism, and a server-aware queue scheduling method. Our evaluation shows that PortFC is free from retransmission, capable of eliminating HoLB and avoiding deadlocks. PortFC achieves 1.7-8.0 times higher throughput and reduces latency by 11.7%-87.7% compared to the state-of-the-art lossy RDMA based on IRN and the lossless RDMA method based on PFC. In particular, PortFC still demonstrates good performance in a Rail-only NDC with heterogeneous bandwidth domains.

BCube is a modular data center network.Compared with other topologies, BCube has natural advantages, such as lower deployment costs and stronger failure recovery capabilities.However, RDMA technology used in BCube still faces challenges, including high retransmission overhead, Head-of-Line Blocking (HoLB) and deadlock problems.Existing solutions for traditional data centers cannot simultaneously address these issues due to the unique topology and server transmission characteristics of BCube.In this paper, we propose a per-port flow control named PortFC for BCube.PortFC addresses the above problems through the designs of a Pause/Resume control signal, a per-port queue allocation method, an egress-detecting per-port flow control mechanism, and a serveraware queue scheduling method.Our evaluation shows that PortFC is free from retransmission, capable of eliminating HoLB and avoiding deadlocks.PortFC achieves 1.7-8.0times higher throughput and reduces latency by 11.7%-87.7%compared to the state-of-the-art

The booming of artificial intelligence (AI) agents has brought about promising business scenarios for sixth-generation (6G) mobile networks, while simultaneously posing significant challenges to network functionalities and infrastructure. These AI agents can be deployed on end devices (e.g., intelligent robots and intelligent cars) or as digital entities (e.g., personal AI assistants). As novel service entities with autonomous decision-making and task execution capabilities, AI agents introduce potential risks of uncontrollable actions and privacy disclosures. AI agents also require new 6G capabilities beyond traditional communication, including multimodality information interaction (e.g., AI models and tokens) and support for service requirements (e.g., computing and sensing of data). In this article, we introduce the concept of AI-agent communication network (ACN), a new paradigm to enable global information interaction and on-demand capability provisioning for single or multiple AI agents. We first introduce the vision and architectural framework of ACN. Then, key technologies and future research directions related to ACN are discussed. Furthermore, we provide potential use cases to elaborate on how ACN can expand the service capabilities of 6G networks.

Recently, Computing Force Networks (CFNs) have emerged to deeply integrate and flexibly schedule multi-layer, multi-domain, distributed, and heterogeneous computing force resources. CFNs build a resources trading platform between consumers and providers, facilitating efficient resource sharing. Therefore, resources trading is an important issue but it faces some challenges. Firstly, because all kinds of large-scale and small-scale resource providers are distributed in a wide area and the number of consumers is larger compared with edge/cloud computing scenarios, the credibility of consumers and providers is hard to guarantee. Secondly, due to market monopolies by large resource providers, fixed pricing strategies, and information asymmetry, both consumers and providers exhibit a low willingness to engage in resources trading. To solve these challenges, the paper proposes an incentive mechanism for trust-driven resources trading to guarantee trusted and efficient resources trading. We first design a trust guarantee scheme based on reputation evaluation, blockchain, and trust threshold setting. Then, the proposed incentive scheme can dynamically adjust prices and enable the platform to provide appropriate rewards based on providers’ classified types and contributions. We formulate an optimization problem aiming at maximizing the trading platform’s utility and obtaining an optimal contract based on individual rationality and incentive compatible constraints. Simulation results verify the feasibility and effectiveness of our scheme, highlighting its potential to reshape the future of computing resource management, increase overall economic efficiency, and foster innovation and competitiveness in the digital economy.

Massive logs contain crucial information about the working status of software systems, which contributes to anomaly detection and troubleshooting. For engineers, it is a laborious task to manually inspect raw logs to know the system running status, and therefore an automated log summarization tool can be helpful. However, due to the specificity of logs in terms of grammar, vocabulary and semantics, existing natural language-based methods cannot perform well in log analysis. To address these issues, we propose LogNotion, a general log summarization framework that highlights the log messages to assist human reading and decision making. We first explore the role played by triplets in log analysis, and propose a triplet extraction method based on sequence tagging and component alignment, in which the specificity of logs is fully taken into account. Then, we propose an unsupervised log summarization method to extract both regular and noteworthy information based on triplets. Comprehensive experiments are conducted on seven real-world log datasets and the results show that LogNotion improves the average ROUGE-1 by 0.26, recall by 0.12, and compression ratio by 2.13%, compared to state-of-the-art log summarization tools. The helpfulness, readability and generalizability are also verified through human evaluation and cross-dataset tests.

With the rise of new applications such as AR/VR, cloud gaming, and vehicular networks, traditional network management solutions are no longer cost-effective. Digital Twin Network (DTN) creates a real-time virtual twin of the physical network, which improves the network's stability, security, and operational efficiency. AI models have been used to model complex network environments in DTN, whose quality mainly depends on the model architecture and data. This paper proposes an automatic data generation and optimization method for DTN called AutoOPT, which focuses on generating and optimizing data for data-driven DTN AI modeling through data-centric AI. The data generation stage generates data in small networks based on scale-independent indicators, which helps DTN AI models generalize to large networks. The data optimization stage automatically filters out high-quality data through seed sample selection and incremental optimization, which helps enhance the accuracy and generalization of DTN AI models. We apply AutoOPT to the DTN performance modeling scenario and evaluate it on simulated and real network data. The experimental results show that AutoOPT is more cost-efficient than state-of-the-art solutions while achieving similar results, and it can automatically select high-quality data for scenarios that require data quality improvement.

As the increase of geographically distributed applications continues, the demand for high-speed, long-distance data transmission across wide area networks (WANs) has significantly increased. Remote Direct Memory Access (RDMA) is extensively deployed in data center networks (DCNs) for its high throughput, low latency, and reduced CPU utilization, and its extension to WANs is expected to fully leverage these benefits. However, existing RDMA solutions, while demonstrating superior performance in data centers, face a performance gap over WANs due to their reliance on DCNs for optimal performance and lack of optimization for WANs’ high latency and loss rates. To bridge this gap, we introduce Lossy Wide-Area RDMA (LoWAR), a high-goodput, high-reliability RDMA solution for lossy WANs. LoWAR incorporates a forward error correction (FEC) shim layer to protect RDMA messages from packet loss, thus minimizing the inefficiency of retransmissions. It also fully offloads processing to RNICs with minimal computational overhead and storage burden, operating transparently on RNICs without requiring modifications to existing applications and networks. We implement a LoWAR prototype with FPGA and evaluate its performance through testbed experiments. The results demonstrate LoWAR’s enhanced performance in lossy WANs: in WANs with 40ms RTT and 0.001% to 0.01% loss rates, LoWAR increases RDMA goodput by 2.05 to 5.01 times, reduces average flow completion times (FCTs) by 3.5% to 12.2%, and eliminates 99th percentile tail FCTs in most scenarios.

网络资源的覆盖范围日益广泛, 算力资源也逐渐成为能够提供泛在计算服务的基础设施. 然而, 在广域网络, 底层网络和计算资源缺乏密切的研究或协同设计, 仍然存在计算服务调度缓慢、 数据分发不灵活、 数据传输效率低等问题. 本文提出算力感知网络(CAN)的系统架构设计, 其核心贡献在于引入感知平面来收集、 管理并综合计算和网络的信息. 这样, 感知平面、控制平面和数据平面组成一个闭环控制系统, 增强了整个系统的感知能力、 决策能力和数据转发功能. 为了使能CAN系统, 本文提出三项关键技术: 算力路由、 弹性广播和广域高吞吐传输. 本文以人工智能(AI)模型训练、 推理和离线参数传输为例, 展示CAN的适用性, 并指出未来的一些研究方向.

Traditional network management solutions can not easily meet the requirements of new applications (such as AR/VR, cloud gaming, and vehicular networks) at a reasonable cost. Digital Twin Network (DTN) builds real-time mirrors of physical networks, which can enhance the simulation, optimization, verification, and control capabilities that physical networks lack. AI models have been used to model complex network environments, which helps build real-time, lightweight, and high-precision DTN. This paper proposes a data generation and optimization method for data-driven DTN AI modeling through Data-Centric AI called AutoOPT. First, AutoOPT generates data in small networks based on scale-independent indicators, which helps the model generalize to large networks. Then, AutoOPT automatically filters out high-quality data through seed sample selection and incremental optimization, which helps the model be effectively trained to enhance accuracy and generalization. We apply AutoOPT to the DTN performance modeling scenario and test on GNNet Challenge datasets. The experimental results show that AutoOPT is more cost-efficient than the winning solutions of the challenge but can obtain similar results.

With the diversified development of the vertical industry, it's urgent to enhance end-to-end (E2E) network slicing for 6G. In addition, by introducing artificial intelligence (AI), the performance of E2E network slicing can be improved with limited radio resources. Therefore, we propose a holistic service-based E2E network slicing. Firstly, the E2E network slicing is abstracted into four layers and three planes, i.e., infrastructure, virtualization, function, and application layer at the horizontal, as well as control, AI, and MANO plane at the vertical. Especially, with reference to service-based architecture (SBA) in 5G core network (5GC), all the three planes are decoupled into independent functions, which are connected through a uniform service-based interface (SBI). Secondly, we design the network slicing templates for some typical applications and instantiate the templates to provide customized services for users. Finally, the experimental results show that our proposed holistic service-based E2E network slicing can ensure the isolation among network slices effectively and reduce the service response time, as well as improve the reliability of the system.

In modern multi-tenant data centers, each tenant desires reassuring dependability from the virtualized network fabric – bandwidth guarantee with work conservation, bounded tail latency and resilient reachability. However, the slow convergence of prior works under network dynamics and uncertainties can hardly provide the dependability for tenants. Further, state-of-the-art load balance schemes are guarantee-agnostic and bring great risks on breaking bandwidth guarantee, which is overlooked in prior works. In this paper, we propose vFab, a dependable virtualized fabric framework which can (1) quickly detect network failure in data plane, (2) explicitly select proper paths for all flows, and (3) converge to ideal bandwidth allocation at sub-millisecond. The core idea of vFab is to leverage the programmable data plane to build a fusion of an active edge (e.g., NIC) and an informative core (e.g., switch), where the core sends link status and tenant information to the edge via telemetry to help the latter make a timely and accurate decision on path selection and traffic admission. We fully implement vFab with commodity SmartNICs and programmable switches. Extensive evaluations show that vFab can keep bandwidth guarantee with high bandwidth utilization, low and bounded latency, and resilient reachability under various network scenarios with limited overhead. Application-level experiments show that vFab can improve QPS by$2.4\times $and cut tail latency by$10\times $compared to the alternatives.

5G communication is making much progress in achieving the Internet of Things and improving the quality of user experience in large bandwidth scenarios. By introducing a variety of new technologies, the performance of 5G has been greatly improved. However, emerging applications put forward more stringent requirements in terms of latency, reliability, peak data rate, service continuity, etc. Communication technology still needs to be further developed. In this article, the next generation of core networks is conceptualized. Inspired by the nervous system of the octopus, we propose a new cognitive service architecture. Cognitive service architecture is a new architecture designed for the 6G core network. It is proposed to enhance the core network so that it is qualified for the increasingly high requirement for quality of service and complicated scenarios. We first give a short vision of the 6G core network. Then cognitive service architecture is demonstrated in detail. A case study is demonstrated to show how cognitive service architecture enhances the performance of the system. Enabling technologies for 6G cognitive service architecture are discussed at last.