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Trajectory sampling for direct traffic observation

Published: 01 June 2001 Publication History

Abstract

Traffic measurement is a critical component for the control and engine ering of communication networks. We argue that traffic measurement should make it possible to obtain the spatial flow of traffic through the domain, i.e., the paths followed by packets between any ingress and egress point of the domain. Most resource allocation and capacity planning tasks can benefit from such information. Also, traffic measurements should be obtained without a routing model and without knowledge of network state. This allows the traffic measurement process to be resilient to network failures and state uncertainty. We propose a method that allows the direct inference of traffic flows through a domain by observing the trajectories of a subset of all packets traversing the network. The key advantages of the method are that 1) it does not rely on routing state; 2) its implementation cost is small; and 3) the measurement reporting traffic is modest and can be controlled precisely. The key idea of the method is to sample packets based on a hash function computed over the packet content. Using the same hash function will yield the same sample set of packets in the entire domain, and enables us to reconstruct packet trajectories.

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Information

Published In

cover image IEEE/ACM Transactions on Networking
IEEE/ACM Transactions on Networking  Volume 9, Issue 3
June 2001
149 pages
ISSN:1063-6692
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IEEE Press

Publication History

Published: 01 June 2001
Published in TON Volume 9, Issue 3

Author Tags

  1. Hash functions
  2. Internet traffic measurement
  3. packet sampling
  4. traffic engineering

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  • (2024)RD-Probe: Scalable Monitoring With Sufficient Coverage In Complex Datacenter NetworksProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672256(258-273)Online publication date: 4-Aug-2024
  • (2024)Scheduling periodic sensors for instantaneous aggregated traffic minimizationWireless Networks10.1007/s11276-024-03722-430:5(3257-3268)Online publication date: 1-Jul-2024
  • (2023)ChameleMon: Shifting Measurement Attention as Network State ChangesProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3604850(881-903)Online publication date: 10-Sep-2023
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