The In-band Network Telemetry (INT) provides unprecedented network visibility by encapsulating fine-grained device-internal status into packet headers. Existing efforts proposed to reduce the telemetry cost can either monitor a small part of flows and links, or require complex calculation supported by programmable switches. In this paper, we propose SD-INT (Self-Driving INT), a lightweight network-wide passive INT system, which can be readily deployed based on commodity switches. The key idea of SD-INT is to reduce two dimensions of redundancy in the INT data which we have observed. Furthermore, the optimized INT is conducted in a self-driving system, adjusting the INT strategy according to the feedback of INT result history. In addition to the mechanism, we design the latency-hiding technology and a suite of efficient algorithms for flow selection and sampling ratio adaptation. These designs enable us to keep the monitoring overhead under a reasonable level, while still collecting network-wide fine-grained telemetry data. Further, we prototype and evaluate SD-INT with both large-scale simulations and testbed deployment. Experiments for both TCP(Transmission Control Protocol) and RDMA(Remote Direct Memory Access) flows show that SD-INT reduces orders of magnitude data volume while achieves similar link coverage compared with INT. Besides, compared with the relevant technologies based on programmable switches, SD-INT is competitive in reducing the data volume.

Service-level objectives (SLOs), as network performance requirements for delay and packet loss typically, should be guaranteed for increasing high-performance applications, e.g., telesurgery and cloud gaming. However, SLO violations are common and destructive in today’s network operation. Detection and diagnosis, meaning monitoring performance to discover anomalies and analyzing causality of SLO violations respectively, are crucial for fast recovery. Unfortunately, existing diagnosis approaches require exhaustive causal information to function. Meanwhile, existing detection tools incur large overhead or are only able to provide limited information for diagnosis. This paper presents DOVE, a diagnosis-driven SLO detection system with high accuracy and low overhead. The key idea is to identify and report the information needed by diagnosis along with SLO violation alerts from the data plane selectively and efficiently. Network segmentation is introduced to balance scalability and accuracy. Novel algorithms to measure packet loss and percentile delay are implemented completely on the data plane without the involvement of the control plane for fine-grained SLO detection. We implement and deploy DOVE on Tofino and P4 software switch (BMv2) and show the effectiveness of DOVE with a use case. The reported SLO violation alerts and diagnosis-needing information are compared with ground truth and show high accuracy (>97%). Our evaluation also shows that DOVE introduces up to two orders of magnitude less traffic overhead than NetSight. In addition, memory utilization and required processing ability are low to be deployable in real network topologies.

Network telemetry is to collect information (e.g., hop latency, throughput) from network devices. Network-wide telemetry is critical for operators to understand the quality of network performance and to diagnose on-going failures. The state-of-the-art telemetry approaches are far from ideal as they are unable to fully satisfy diverse requirements of operators, specifically for on-demand, full coverage, and scalable telemetry. In this paper, we provide a new framework of network telemetry for data center networks, called NetView. NetView can support various telemetry applications and frequencies on demand, monitoring each device via proactively sending dedicated probes. Technically, NetView leverages source routing to forward probes, achieving full coverage. Besides, a series of probe generation algorithms largely reduce probe number, providing high scalability. The evaluation shows that NetView reduces the bandwidth occupancy by more than two orders of magnitude compared with Pingmesh and INT-path, and conducts network-wide telemetry for large-scale data center network using only one vantage server, without bringing about resources bottleneck.

P4 and programmable data planes bring significant flexibility to network operation but are inevitably prone to various faults. Some faults, like P4 program bugs, can be verified statically, while some faults, like runtime rule faults, only happen to running network devices, and they are hardly possible to handle before deployment. Existing network testing systems can troubleshoot runtime rule faults via injecting probes, but are insufficient for programmable data planes due to large overheads or limited fault coverage. In this paper, we propose P4Tester, a new network testing system for troubleshooting runtime rule faults on programmable data planes. First, P4Tester proposes a new intermediate representation based on Binary Decision Diagram, which enables efficient probe generation for various P4-defined data plane functions. Second, P4Tester offers a new probe model that uses source routing to forward probes. This probe model largely reduces rule fault detection overheads, i.e. requiring only one server to generate probes for large networks and minimizing the number of probes. Moreover, this probe model can test all table rules in a network, achieving full fault coverage. Evaluation based on real-world data sets indicates that P4Tester can efficiently check all rules in programmable data planes, generate 59% fewer probes than ATPG and Pronto, be faster than ATPG by two orders of magnitude, and troubleshoot multiple rule faults within one second on BMv2 and Tofino.

In-band Network Telemetry (INT) can provide fine-grained and accurate device-level telemetry metrics. Nonetheless, INT can track only a small ratio of devices and links and embedding telemetry data into normal packets brings high overhead and high operation complexity. Hence, we present NetVision, a powerful proactive network telemetry platform with high coverage and high scalability.