Evaluation of Building Blocks for Pure Passive One-way-delay
measurements
Georg Carle, Sebastian Zander, Tanja Zseby
GMD FOKUS; Global Networking (GloNe)
Traffic engineering and validation of service level agreements (SLAs)
require measurement of quality of service parameters such as delay in
specific sections of the network. Due to frequent asymmetries of forward
and backward paths, in many cases the measurement of one-way metrics is
required.
Active measurement methods, which allow to survey large parts of the
Internet with a high precision, is an active research area (see, e.g.,
[PaAM00], [KaZe99], [PaMA98], [UiKo97]). Active measurements rely on
sending of test traffic through the network. The approach has the
disadvantage of generating additional load on network links and routers,
which can significantly affect the measurement results. Active
measurements have additional disadvantages, as test traffic may bother
intermediate providers especially if test traffic is not recognizable as
such, and as it may lead to additional costs in case of usage-based
charging.
In contrast, passive measurements are based on already existing
traffic in the network. While passive methods may lead to additional
traffic as a result of the measurement, these methods avoid the negative
impact of test traffic. At the same time, passive measurements lack the
advantages of test traffic generated by active methods, as the test
traffic leads to controllable experiments, where pre-defined traffic
patterns suitable for the measurement goals are sent over the path of
interest. Nevertheless, for some applications (e.g. SLA validation,
traffic engineering) the measurement goal is to provide performance
results for the treatment of real customer traffic. The traffic of
interest is already present in the network. In this cases the measurement
goal can be achieved efficiently by passive measurements avoiding the
effort and the disadvantages of sending test traffic.
An approach to passively measure one-way-delay is to generate a
timestamp and a unique packet ID for each packet of interest at the
involved measurement points. In [GrDM98], a solution based on this
approach is presented that uses with dedicated hardware and a 32 bit CRC
as packet ID. In [DuGr00], an approach identifying paths followed by
packets of certain IP flows for is presented, where a simple hash function
over the first 40 bytes is used for the generation of a packet ID.
We have implemented a modular passive meter based on an extended Linux
netfilter classifier which is suitable for passive measurement of
one-way-delays. The meter has a modular structure which allows to use and
compare different packet id generation functions. In this paper, we
investigate requirements of a passive measurement system for one-way-delay
measurements, i.e. classification demands, required accuracy etc. We first
define the following major building blocks for the system: packet
capturing, timestamping, classification, packet-id generation and result
data transfer (Figure 1). Based on these building blocks, we investigate
alternative methods for implementing them, in order to find the most
efficient way to fulfill specific tasks. After capturing of a packet
(pkt), a timestamp (t1) is applied. If measurements are done for multiple
flow specifications simultaneously, the flow identifier (identifying
traffic aggregate or microflow) can be provided as an additional
information element after classification. Subsequently, a packet
identifier (packet-ID) is generated. From both measurement points involved
in a one-way-delay measurement, the packet-ID (id) and the timestamp (t1)
are transferred together with an measurement point identifier (c.f. MP1 or
MP2 in figure 1) to a analysis application where the delay is calculated.
Based on this model we investigate how the selection of algorithms and
parameters of the different building blocks influence the performance
(resource consumption, speed) of the overall system. In the paper we focus
on the analysis and comparison of different packet ID generation
functions. For this we investigate the usage of a simple combination of
highly variable header bytes, a CRC function, the scheme used in [DuGr00]
and the MD5 message-digest algorithm [RFC1321, Touc95a]. We present the
influence of this functions to the performance (speed and accuracy) and
the resource consumption of the system.
References
- [DuGr00] Nick Duffield, Matthias Grossglauser: "Trajectory Sampling
for Direct Traffic Observation", Proceedings of ACM SIGCOMM 2000,
Stockholm, Sweden, August 28 - September 1, 2000.
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MARTENS, John G. CLEARY: "Nonintrusive and Accurate Measurement of
Unidirectional Delay and Delay Variation on the Internet", INET'98,
Geneva, Switzerland, 21-24 July, 1998
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for Large-Scale Internet Measurement", IEEE Communications Vol 36 No 8, p.
48-54, August 1998
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April 1992
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of ACM SIGCOMM'95, Cambridge, Massachusetts, USA, August 28 - September 1,
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