University of Illinois Urbana-Champaign

Scaling Data-Plane Logging in Large Scale Networks

Arefin, Md Ahsan; Khurshid, Ahmed; Caesar, Matthew; Nahrstedt, Klara

Content Files
Tech_Report_Rescue.pdf

Permalink

https://hdl.handle.net/2142/25928

Description

Title
Scaling Data-Plane Logging in Large Scale Networks
Author(s)
  • Arefin, Md Ahsan
  • Khurshid, Ahmed
  • Caesar, Matthew
  • Nahrstedt, Klara
Issue Date
2011-08-11
Keyword(s)
  • Network Management
  • Traffic Engineering
  • Measurement and Analysis
  • Network Monitoring
  • Data-plane Logging
Date of Ingest
2011-08-11T16:14:37Z
Abstract
Understanding and troubleshooting wide area networks (such as military backbone networks and ISP networks) are challenging tasks due to their large, distributed, and highly dynamic nature. Building a system that can record and replay fine-grained behaviors of such networks would simplify this problem by allowing operators to recreate the sequence and precise ordering of events (e.g., packet-level forwarding decisions, route changes, failures) taking place in their networks. However, doing this at large scales seems intractable due to the vast amount of information that would need to be logged. In this paper, we propose a scalable and reliable framework to monitor fine-grained data-plane behavior within a large network. We give a feasible architecture for a distributed logging facility, a tree-based data structure for log compression and show how this logged information helps network operators to detect and debug anomalous behavior of the network. Experimental results obtained through trace-driven simulations and Click software router experiments show that our design is lightweight in terms of processing time, memory requirement and control overhead, yet still achieves over 99%precision in capturing network events.
Publisher
IEEE
Type of Resource
text
Genre of Resource
Technical Report
Language
en
Permalink
http://hdl.handle.net/2142/25928
Sponsor(s)/Grant Number(s)
  • NSF CNS 1053781
  • NSF NeTSE 1012194
  • NSF NeTS 0964081
  • NSF CSR 0834480
  • NSF CSR 0720702
  • International Fulbright S&T Award

Owning Collections

Research and Tech Reports - Computer Science PRIMARY