Due to its superior performance, Remote Direct Memory Access (RDMA) has been widely deployed in data center networks. It provides applications with ultra-high throughput, ultra-low latency, and far lower CPU utilization than TCP/IP software network stack. However, the connection states that must be stored on the RDMA NIC (RNIC) and the small NIC memory result in poor scalability. The performance drops significantly when the RNIC needs to maintain a large number of concurrent connections. We propose StaR (Stateless RDMA), which solves the scalability problem of RDMA by transferring states to the other communication end in a trusted network. Leveraging the asymmetric communication pattern in data center applications, StaRlets the communication end with low NIC memory usage to save states for the other end with high NIC memory usage, thus making the RNIC on the bottleneck side stateless. We implemented StaR on an FPGA board with a 10Gbps network port and NS-3, evaluating its performance on a testbed with 9 machines, each equipped with StaR NICs, and verified its scalability stability by conducting a larger-scale simulation with 200 fully connected nodes using a 100Gbps link. The experimental results show that in high concurrency scenarios, the throughput of StaR can reach up to 4.13x and 1.35x of the original RNIC and the latest software-based solution, respectively.

Network Diagram Comprehension (NDC) is a vital task for networking professionals, offering essential insights into network topology and configurations. However, NDC remains a labor-intensive process heavily reliant on human expertise, with existing tools falling short in addressing this challenge. It is critical to develop an Automatic NDC (ANDC) system that ensures high faithfulness and completeness in information extraction while supporting practical, end-to-end NDC applications. Moreover, a comprehensive dataset and benchmark are necessary to systematically evaluate and drive the progress of ANDC.In this work, we introduce Layered Extractor of Network Diagrams (LEND), the first ANDC system designed to comprehensively and faithfully extract and utilize information from network diagrams. LEND employs a three-stage pipeline: (1) a layer extractor to decompose diagrams and identify key elements with a denoising cascade, (2) an inter-layer combiner to reconstruct entity relations with positional and domain knowledge, and (3) a task-specific interpreter for networking applications.To support this effort, we develop two extensive NDC datasets comprising over 4,000 network diagrams and icons from diverse sources, along with the first benchmark to evaluate ANDC systems across three distinct metrics. Empirical experiments demonstrate that LEND outperforms existing methods by achieving at 1.21– 5.10× better faithfulness and completeness, and improves its capability as a NetOps engineer by 30.5% on the Cisco Certified Network Associate (CCNA) exam.

Large Language Model (LLM) agents are capable of task execution across various domains by autonomously interacting with environments and refining LLM responses based on feedback. However, existing model serving systems are not optimized for the unique demands of serving agents. Compared to classic model serving, agent serving has different characteristics: predictable request pattern, increasing quality requirement, and unique prompt formatting. We identify a key problem for agent serving: LLM serving systems lack session-awareness. They neither perform effective KV cache management nor precisely select the cheapest yet competent model in each round. This leads to a cost-quality tradeoff, and we identify an opportunity to surpass it in an agent serving system. To this end, we introduce AgServe for AGile AGent SERVing. AgServe features a session-aware server that boosts KV cache reuse via Estimated-Time-of-Arrival-based eviction and in-place positional embedding calibration, a quality-aware client that performs session-aware model cascading through real-time quality assessment, and a dynamic resource scheduler that maximizes GPU utilization. With AgServe, we allow agents to select and upgrade models during the session lifetime, and to achieve similar quality at much lower costs, effectively transcending the tradeoff. Extensive experiments on real testbeds demonstrate that AgServe (1) achieves comparable response quality to GPT-4o at a 16.5\% cost. (2) delivers 1.8$\times$ improvement in quality relative to the tradeoff curve.

Remote Direct Memory Access (RDMA) has been widely deployed in data centers to improve application performance. However, the characteristic of RDMA to deliver messages in order cannot meet the emerging requirements of applications for scheduling messages within an RDMA connection, making RDMA unable to be fully utilized. Some works try to schedule the data to be transferred in specific applications before delivering to RDMA, or distribute messages to different connections. However, these approaches tightly couple scheduling logic with application logic and may result in high scheduling overhead.

Visualization of network simulation is an essential tool for network practitioners. However, the front-end of existing network simulators often fails to deliver satisfactory performance when dealing with modern network scales and interface speed. In this paper, we propose NetVision, an efficient visualization front-end of network simulation based on the Unity engine, which is commonly used for video game and virtual reality development. NetVision offers flow-level visualization of network behavior and performances. Then, through parallel optimization, NetVision supports real-time visualization for large-scale high-speed networks.

Discrete Event Simulation (DES) is an essential tool for network practitioners. Unfortunately, existing DES simulators cannot achieve satisfactory performance at the scale of modern networks. Recent work has attempted to address these challenges by reducing the traffic processed via novel approximation techniques; however, we argue in this paper that much of the slowdown of existing DES simulators is due to their underlying software architecture.

Due to its superior performance, Remote Direct Memory Access (RDMA) has been widely deployed in data center networks. It provides applications with ultra-high throughput, ultra-low latency, and far lower CPU utilization than TCP/IP software network stack. However, the connection states that must be stored on the RDMA NIC (RNIC) and the small NIC memory result in poor scalability. The performance drops significantly when the RNIC needs to maintain a large number of concurrent connections.We propose StaR (Stateless RDMA), which solves the scalability problem of RDMA by transferring states to the other communication end. Leveraging the asymmetric communication pattern in data center applications, StaR lets the communication end with low concurrency save states for the other end with high concurrency, thus making the RNIC on the bottleneck side to be stateless. We have implemented StaR on an FPGA board with 10Gbps network port and evaluated its performance on a testbed with 9 machines all equipped with StaR NICs. The experimental results show that in high concurrency scenarios, the throughput of StaR can reach up to 4.13x and 1.35x of the original RNIC and the latest software-based solution, respectively.

Because of small NIC on-chip memory, the massive connection states maintained on Remote Direct Memory Access (RDMA) NIC (RNIC) significantly limit its scalability. When the number of concurrent connections grows, RNICs have to frequently fetch connection states from host memory, leading to dramatic performance degradation. In this paper, we propose StaR, which fundamentally solves this scalability issue by making RNIC stateless. Leveraging the asymmetric communication pattern in data center applications, the StaR RNIC stores zero connection-related states by moving all the connection states to the other end. Through careful design, StaR RNICs can maintain unchanged RDMA semantics and avoid security issues even when processing traffic statelessly. Preliminary simulation results show that StaR can improve the aggregate throughput by more than 160x (stress test) and 4x (application) compared to original RNICs.