In-band network telemetry (INT), empowered by programmable dataplanes such as P4, comprises a viable approach to network monitoring and telemetry analysis. However, P4-INT as well as other existing frameworks for INT yield a substantial transmission overhead, which grows linearly with the number of hops and the number of telemetry values. To address this issue, we present a deterministic and a probabilistic technique for lightweight INT, termed as DLINT and PLINT,respectively. In particular, DLINT exercises per-flow aggregation by spreading the telemetry values across the packets of a flow. DLINT relies on switch coordination through the use of per-flow telemetry states, maintained within P4 switches. Furthermore, DLINT utilizes Bloom Filters (BF) in order to compress the state lookup tables within P4 switches. On the other hand, PLINT employs a probabilistic approach based on reservoir sampling. PLINT essentially empowers every INT node to insert telemetry values with equal probability within each packet. Our evaluation results corroborate that both proposed techniques alleviate the transmission overhead of P4-INT, while maintaining a high degree of monitoring accuracy. In addition, we perform a comparative evaluation between DLINT and PLINT. DLINT is more effective in conveying path traces to the telemetry server, whereas PLINT detects more promptly path updates exploiting its more efficient INT header space utilization

翻译：带内网络遥测（INT）借助P4等可编程数据平面，构成了一种可行的网络监控与遥测分析方法。然而，P4-INT及现有其他INT框架会产生显著传输开销，且该开销随跳数和遥测值数量线性增长。为解决此问题，我们分别提出了两种轻量级INT技术——确定性DLINT与概率型PLINT。具体而言，DLINT通过将遥测值分散至流的数据包中实现基于流聚合。该技术利用P4交换机内维护的逐流遥测状态实现交换机协同，同时引入布隆过滤器（BF）压缩P4交换机中的状态查找表。另一方面，PLINT采用基于蓄水池抽样的概率方法，赋予每个INT节点以相等概率向数据包中插入遥测值的能力。评估结果表明，两种方法在保持较高监测精度的同时，均能降低P4-INT的传输开销。此外，我们对DLINT与PLINT进行对比评估：DLINT在向遥测服务器传输路径轨迹方面更具优势，而PLINT通过更高效的INT头部空间利用，能更及时地检测路径更新。