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AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning by Zhang, Penghui; Zhang, Hua; Pi, Yibo; Cao, Zijian; Wang, Jingyu; Liao, Jianxin is a scholarly article available to read on EtoBox.

What is AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning about?

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 tele

Author

Zhang, Penghui; Zhang, Hua; Pi, Yibo; Cao, Zijian; Wang, Jingyu; Liao, Jianxin

Published

2023

Language

EN