In-band Network Telemetry(INT) enables fine-grained telemetry by editing the packet header with the capability of programmable data plane to carry network status. However, INT could cause significant telemetry overhead without an effective system design. Existing measurement systems attempt to reduce this overhead by employing fixed-frequency INT sampling. Nonetheless, these methods lead to frequent measurements of network ports with large flows while neglecting ports with small flows for extended periods. In this paper, we introduce a lightweight passive telemetry system based on INT, called INTSelection. The core idea is to use a flow selection algorithm at the centralized controller so as to measure all active ports. Compared with the current method, INT-Selection reduces the bandwidth overhead by 58.2% and 2.7%.

In-band Network Telemetry (INT) technology enables fine-grained network monitoring by encapsulating intra-switch network status into INT probes, which is essential in data center networks for ensuring Quality of Service (QoS). Existing INT-based telemetry systems leverage centralized controllers to compute non-overlapping probe paths, thereby facilitating lightweight and network-wide measurements. However, these systems fail to adapt effectively to network topology changes caused by link or device failures, primarily due to inflexible path planning under dynamic conditions. To address this problem, we propose INT-Source, a unified policy-based network-wide telemetry system for probing and forwarding. First, we design a data plane forwarding mechanism for INT probes to ensure telemetry coverage during topology changes and reduce telemetry overhead. Second, we design a probe packet structure and introduce a switch-based probe verification and discard mechanism to prevent redundant link probing. Third, we introduce two algorithms for INT-Source: a Single-Source algorithm to facilitate deployment and a Multi-Source algorithm to enable lightweight and scalable telemetry. Our evaluation shows that INT-Source reduces bandwidth overhead to 12.5% compared to existing methods across three network topologies. Even with a 10% link failure rate, INT-Source is able to monitor 93.1% of network ports, demonstrating strong robustness.

With the expansion of production networks, new challenges arise in scaling telemetry systems to accommodate the massive number of network devices. In-band Network Telemetry (INT) is widely adopted for its fine-grained and accurate measurements. However, system robustness and performance scalability remain key challenges for INT-based network-wide telemetry systems. Existing INT-based measurements manage the network as a whole and rely on a centralized controller for path planning and telemetry data collection. As networks scale and the probability of failures increases, frequent re-planning leads to prolonged telemetry interruptions. In this work, we propose INT-Partition, a hierarchical and fault-tolerant in-band network telemetry system with a divide-and-conquer paradigm. INT-Partition conducts telemetry in two stages: network partitioning and telemetry within each partition, enabling scalability for mega-scale networks. Our evaluations, including mega-scale simulations using BMv2 software switches and small-scale validations on Tofino hardware switches, demonstrate the effectiveness of our approach. With 1% of network equipment out of order, INT-Partition covers over 89.67% of the area and accurately locates faults. As the network scales, telemetry planning and deployment time is reduced by 75.62% to 96.02%, and hot reloading enables seamless switching of telemetry deployment.

Nowadays, autonomous vehicles (AVs) have become an excellent solution for alleviating the burden of the traffic system and improving safety. However, the large-scale application of AVs is significantly restrained by the high-power cost of their numerous sensors and processors, which can reach hundreds of watts and occupy most of the vehicular power consumption. To meet such tremendous computation requirements, most AVs rely on vehicle-to-everything (V2X) communication with mobile edge computing (MEC) techniques to relocate their tasks to external computing. In this paper, the problem of optimizing the task and path assignment process in the V2X-MEC system is tackled for further reducing vehicular power consumption with the constraint of transmission latency. We incorporate the Nash Equilibrium (NE) concept for generating the optimal assignment of tasks and paths, and the attractor selection model (ASM) is introduced to flexibly allocate tasks with consideration of different energy-saving requirements. With comprehensive simulation experiments, we show that the proposed method can save approximately 85% energy compared with the method without task assignment, and it is over 9% and 16% better than the shortest path assignment method and the minimum average delay method, respectively. Besides, the power consumption in AVs with low remaining electricity can be further reduced by over 43%.

In-band network telemetry (INT) allows for fine-grained network monitoring, without requiring communication with the controller at each hop. Existing INT-based network-wide telemetry systems achieve low-overhead monitoring with non-overlapping path planning algorithms. However, these systems do not constrain the length of the generated probing paths, which will lead to packet loss when the size of the packet with collected telemetry data exceeds the MTU limit. To address this issue, we propose MTU-adaptive path segmentation algorithms step by step in this paper. Initially, we present two single-path planning algorithms: the INT-optimize algorithm, which produces a single path that covers the entire network with the lowest southbound communication overhead, and the INT-low-cost algorithm, which further accelerates the INT-optimize. Next, to consider the MTU limit, we propose the single-MTU adaptive INT-Segment algorithm to divide the single long path generated in the previous step into multiple path segments. In addition, we generalize the MTU-adaptive network telemetry problem and propose a multi-MTU adaptive INT-Segment solution to achieve high-performance network telemetry in networks with multiple MTU settings. Extensive evaluations demonstrate that our proposed MTU-adaptive solutions can achieve sub-second network-wide telemetry for large-scale networks, with less than 2.9ms to calculate the probing paths for an 18-pod FatTree. Furthermore, our multi-MTU adaptive INT-Segment solution significantly reduces the number of INT Sinks and INT Sources by 13.25%-42.39% when deployed in multi-MTU networks while maintaining stable telemetry data collection time. Compared with the state-of-the-art INT-path, our solution adapts the probing path to the network MTU limit, producing a telemetry data collection efficiency improvement of 10%-94%.

In-band network telemetry (INT) enables hop-by-hop fine-grained network monitoring without interacting with the controller at every hop. Existing INT-based network-wide telemetry systems achieve low-overhead monitoring with the non-overlapped path planning algorithms. However, they do not bound the length of the generated probing paths, which may lead to packet loss when the collected telemetry data exceeds the MTU limit. In this paper, we propose an MTU-adaptive path segmentation algorithm to solve this problem. First, we provide two single-path planning algorithms: the INT-optimize algorithm produces a single path that covers the entire network with the lowest southbound communication overhead, and the INT-low-cost algorithm further improves the path planning efficiency of the INT-optimize. By taking the MTU limit into account, we further propose INT-Segment, a novel path segmentation algorithm to split the single long path produced from the previous step into multiple path segments. Extensive evaluations show that the proposed INT-Segment can realize sub-second network-wide telemetry for large-scale networks. It takes less than 2.9ms to calculate the probing paths for an 18-pod FatTree. Compared with the state-of-the-art INT-path, our solution makes the probing path well adapted to the network MTU limit and improves the telemetry efficiency by 10%-94%.

In-band network telemetry (INT) delivers high-precision network monitoring by collecting device-internal states entirely on the data plane. For rapid congestion awareness and network troubleshooting, it is necessary to conduct network-wide telemetry by generating multiple monitoring paths covering the entire network graph. Solving the optimal path planning problem used the eulerian trail initially at a time complexity of$O(k(3E+V-15k/2))$. For mega-scale data center networks, such a high complexity is unacceptable because the algorithm cannot adapt well to occasional topology changes. In this work, we propose improved INT-path and INT-react, two refined path planning algorithms with a much reduced time complexity of only$O(E)$. Furthermore, INT-react also considers balanced path generation to reduce the longest path length for synchronized collection of telemetry data from each monitoring path. The evaluation shows that on average it costs 2.10s for the improved INT-path to solve the optimal path planning for a network of 9500 switches, while the computation is completed within only 0.283s on average for INT-react. In addition, INT-react reduces the longest path length. INT-react's path planning is so fast that it promptly reacts to topology changes and is ready to be deployed in mega-scale production networks.