In-band Network Telemetry (INT) has emerged as a promising network
measurement technology. However, existing network telemetry systems lack the
flexibility to meet diverse telemetry requirements and are also difficult to
adapt to dynamic network environments. In this paper, we propose AdapINT, a
versatile and adaptive in-band network telemetry framework assisted by
dual-timescale probes, including long-period auxiliary probes (APs) and
short-period dynamic probes (DPs). Technically, the APs collect basic network
status information, which is used for the path planning of DPs. To achieve full
network coverage, we propose an auxiliary probes path deployment (APPD)
algorithm based on the Depth-First-Search (DFS). The DPs collect specific
network information for telemetry tasks. To ensure that the DPs can meet
diverse telemetry requirements and adapt to dynamic network environments, we
apply the deep reinforcement learning (DRL) technique and transfer learning
method to design the dynamic probes path deployment (DPPD) algorithm. The
evaluation results show that AdapINT can redesign the telemetry system
according to telemetry requirements and network environments. AdapINT can
reduce telemetry latency by 75\% in online games and video conferencing
scenarios. For overhead-aware networks, AdapINT can reduce control overheads by
34\% in cloud computing services.Comment: 14 pages, 19 figure