Adaptive Provisioning In-band Network Telemetry at Computing Power Network [invited]

Mingtao Ji, Chenwei Su, Yuting Yan, Zhuzhong Qian, Sheng Zhang, Yu Chen, Tuo Cao, Xiaohang Shi, Luis Vasquez, Baoliu Ye

Abstract: In-band Network Telemetry (INT) is proposed to detect networks via injecting specific probes to collect the hop-by-hop metadata within programmable switches. But there exist multiple challenges to conducting INT at Computing Power Network, such as control decisions of different INT frequencies, and the unforeseeable INT query workloads. In this study, we formulate an online non-linear time-varying integer programming problem that aims to maximize the overall quality of service through both frequency selection and INT query workload distribution. To achieve this, we propose an online learning, INTService, which utilizes a primal-dual mechanism to make fractional decisions. At last, extensive evaluations show that our proposed INTService exhibits up-lift performance 40% on average over other state-of-the-art algorithms.