Task Scheduling for Probabilistic In -Band Network Telemetry

In-band Network Telemetry (INT) is a novel framework for monitoring network health in real-time, and its recent variant, Probabilistic INT (PINT), reduces its bandwidth consumption with a probabilistic approach. However, as we show in this paper, a PINT task can be successfully accomplished only when it is allocated a sufficient number of packets, and if there are many tasks executed in parallel, packets become a scarce resource. Meanwhile, today’s production network generally executes multiple measurement tasks for tracing different network states simultaneously. Therefore, in such a context, scheduling parallel PINT tasks on one single INT flow that has a limited number of packets becomes a critical problem. In this paper, we address this problem for the first time. We propose an algorithm that efficiently schedules multiple parallel PINT tasks on a flow by allocating the flow’s packets to the tasks and showing that the allocation is optimal. We realize the algorithm with a packet processing pipeline and implement it on software and hardware-programmable switches. Comprehensive evaluation on a FatTree testbed shows that at a low scheduling overhead, our algorithm can conduct parallel PINT tasks to detect various network faults in a timely and accurate manner. Additionally, the algorithm accomplishes more PINT tasks with higher quality than the alternative solutions.