AdapINT: 基于深度强化学习的灵活自适应带内网络遥测系统

IF 4.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Penghui Zhang;Hua Zhang;Yibo Pi;Zijian Cao;Jingyu Wang;Jianxin Liao
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引用次数: 0

摘要

带内网络遥测技术(INT)已成为一种前景广阔的网络测量技术。然而,现有的网络遥测系统缺乏灵活性,无法满足不同的遥测要求,也难以适应动态网络环境。本文提出的 AdapINT 是一种多功能自适应带内网络遥测框架,由长周期辅助探头(AP)和短周期动态探头(DP)等双时间尺度探头辅助。在技术上,AP 收集基本的网络状态信息,用于 DP 的路径规划。为了实现全网覆盖,我们提出了一种基于深度优先搜索(DFS)的辅助探针路径部署(APPD)算法。DP 为遥测任务收集特定的网络信息。为了确保 DP 能够满足多样化的遥测要求并适应动态网络环境,我们应用深度强化学习(DRL)技术和迁移学习方法设计了动态探针路径部署(DPPD)算法。评估结果表明,AdapINT 可以灵活定制遥测系统,以适应不同的需求和网络环境。在延迟感知网络中,AdapINT 有效地降低了遥测延迟;在开销感知网络中,AdapINT 显著降低了控制开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning
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 flexibly customize the telemetry system to accommodate diverse requirements and network environments. In latency-aware networks, AdapINT effectively reduces telemetry latency, while in overhead-aware networks, it significantly lowers the control overheads.
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来源期刊
IEEE Transactions on Network and Service Management
IEEE Transactions on Network and Service Management Computer Science-Computer Networks and Communications
CiteScore
9.30
自引率
15.10%
发文量
325
期刊介绍: IEEE Transactions on Network and Service Management will publish (online only) peerreviewed archival quality papers that advance the state-of-the-art and practical applications of network and service management. Theoretical research contributions (presenting new concepts and techniques) and applied contributions (reporting on experiences and experiments with actual systems) will be encouraged. These transactions will focus on the key technical issues related to: Management Models, Architectures and Frameworks; Service Provisioning, Reliability and Quality Assurance; Management Functions; Enabling Technologies; Information and Communication Models; Policies; Applications and Case Studies; Emerging Technologies and Standards.
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