Internet DRAFT - draft-anjali-ippm-avail-band-measurement


IP Performance Metrics Working Group                         T. Anjali
Internet Draft                                              C. Scoglio
Expiration Date: April 2003                             I. F. Akyildiz
                                       Georgia Institute of Technology
                                                                G. Uhl
							     A. Sciuto
                                                      Swales Aerospace
                                                           J. A. Smith
							  NASA Goddard

              Available Bandwidth Measurement in IP Networks


Status of this Memo

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   Available bandwidth along a path is an important metric that can be
   useful to provide insight into the state and performance of the
   network. Some methods have been proposed in literature for
   measurement of available bandwidth but they face the problems of
   scalability and high intrusiveness.  In this draft, we propose a
   method to measure path available bandwidth using an active approach
   that probes the path. The probing mechanism used is based on TCP
   New Reno. Once the samples for the available bandwidth measurement
   are obtained, more accurate results are calculated using a
   prediction filter.

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Table of Contents

   1.  Introduction						2
       1.1 Overview of available bandwidth measurement		3
       1.2 Available tools					3
   2.  Description						4
       2.1 TCP measurement phase				4
       2.2 Estimation and prediction phase			5
   3.  Enhancements and Evaluations				6
   4.  References						7
   5.  Authors' Addresses					7

1. Introduction

   The efficiency of the QoS provided by an IP network depends on
   effective traffic management, performed at both small and large
   time-scales.  Traffic management encompasses the need for dynamic
   configuration of network devices to adjust to the evolving
   traffic. This feedback loop is based on measurement of network
   characteristics. But observability of Internet traffic is difficult
   because of the network size, large traffic volumes, and distributed

   Some QoS metrics can be measured in the core of the network and
   others at the edges. Some have local significance at each router
   while others are end-to-end metrics. They can be obtained by
   measurements from the various network elements. To obtain measured
   statistics from each network element is possible if individual
   users can monitor each such device. Due to security reasons, this
   is not possible in a network. Thus, common users can only measure
   the end-to-end metrics.

   The approaches to monitor a network are active or passive. The
   active approach has the advantage of measuring the desired
   quantities at the desired time. Passive measurements are carried
   out by observing normal network traffic, without the extra load.
   But the amount of data accumulated can be substantial because the
   network will be polled often for information.

   Available bandwidth in a network is an important metric that can be
   indicative of the network performance.  The available bandwidth of
   a link can be defined as the unused portion of the link capacity.
   Then, the available bandwidth along a path is defined as the
   minimum of the available bandwidths of the links in the path. Based
   on the bandwidth available in various segments of the network, the
   network operator can obtain information about the congestion in the
   network, perform the admission control, routing, capacity
   provisioning etc.

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1.1 Overview of available bandwidth measurement

   Most approaches to available bandwidth measurement are active.
   However, SNMP based approaches are passive since they obtain router
   statistics from the router and do not observe the treatment
   received by the injected pseudo traffic. The active approaches have
   the advantage of being faster and they operate independently of any
   network support. SNMP based approaches need cooperation from the
   network as they need access to the network element MIBs.

   The available bandwidth can be measured for individual links of the
   network. The end-to-end performance of the network is what the
   users and network operators are really interested in. The
   end-to-end available bandwidth information can be obtained by a
   concatenation of the available bandwidth measurements of the
   individual links comprising the path. However, this approach can be
   very inefficient as the amount of data collected grows as the path
   size increases and a central data analysis station will be
   required. Thus, tools have to be devised that can measure the
   end-to-end available bandwidth directly and accurately from the

1.2 Available tools

   Currently, various tools and products are available that can be
   used to measure the total capacity in the links of a path in the
   network. They can be split into two families: pathchar-based and
   packet pair-based. Examples include Nettimer [1], Network Weather
   Service approach [2] etc.

   Among the tools to measure the available bandwidth of a path is an
   active approach given in [3]. It is based on transmission of
   self-loading periodic measurement streams. This scheme sends
   traffic at increasing rates from the source to the destination
   until the rate finally reaches the available bandwidth of the tight
   link after which the packets start experiencing increasing amounts
   of delay. Thus this scheme can be highly intrusive. Also, it takes
   a long time to converge to a measurement estimate. Another active
   approach to measure the throughput of a path is Iperf from NLANR
   that sends streams of TCP/UDP flows. It is another highly intrusive
   approach that sends traffic at high speeds and displays the
   throughput achieved. Cisco has introduced the NetFlow technology
   that provides IP flow information for a network. NetFlow provides
   detailed data collection with minimal impact on the performance on
   the routing device and no external probing device. But in a
   DiffServ environment, the core of a network is interested in
   aggregate rather than per-flow statistics, due to the scalability
   issues. MRTG is a tool, based on SNMP, that gives periodic
   measurements of the utilization of a particular link along the
   path. To obtain statistics of a link via MRTG, the SNMP query needs
   access to the router. Also, MRTG obtains available bandwidth
   estimates over periods of length 5 minutes.

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   Most of the current bandwidth measurement techniques face the
   problems of poor accuracy, scalability, statistical robustness,
   agility in adapting to bandwidth changes and flexibility.

   In this draft, we propose a method to measure the available
   bandwidth along a path that is accurate, scalable, easy to
   implement and does not need any network support. The method can be
   enhanced under scenarios where network support is available to a
   highly accurate measurement procedure.

2. Description

   Our proposed method for available bandwidth measurement can be
   spilt into two phases. In the first phase, a TCP application is
   deployed to obtain rough estimates of the available bandwidth along
   the path. We call this the TCP measurement phase. The second phase
   analyzes the gathered estimates of the available bandwidth to
   obtain a more accurate and reliable measurement value. This is
   called the prediction phase. Next, we explain the individual phases
   in detail.

2.1 TCP measurement phase

   Our method employs a TCP application to detect and estimate the
   congestion level in the network. The basic functioning of TCP is
   described in [4] with modifications in [5]. TCP starts in slow
   start where the congestion window size is increased by one segment
   for each ACK for a successful packet delivery. The sender can
   transmit segments upto the congestion window size. Once the sender
   observes a packet loss (from a missing acknowledgement), the sender
   enters congestion avoidance state. A fast retransmit of the missing
   segment is performed if triple duplicate ACKs are received in a row
   and then the sender enters fast recovery (and not slow start). TCP
   NewReno suggests a modification to the fast recovery algorithm to
   incorporate a response to partial acknowledgements received during
   fast recovery.

   Thus, TCP constantly probes the network to find out the available
   capacity, and when it encounters congestion, it backs off. We use
   this feature of TCP in our proposed available bandwidth measurement

   If the TCP session is allowed to run for long durations, it fully
   occupies the unused portion of the path bandwidth, thus changing
   the network state. Thus, we define a time interval T for which the
   TCP session is run and then terminated. During this time interval,
   we gather various samples of bandwidth, which are then fed to the
   prediction phase. The bandwidth samples are calculated as follows.
   When the TCP session is started, it starts with the slow start
   algorithm. When the first signs of congestion are discovered, the
   TCP session goes into congestion avoidance. We note the value of
   congestion window just before it is reduced. That determines the
   rate at which the sender was transmitting and constitutes the first

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   sample of available bandwidth estimate. Then the sender goes into
   congestion avoidance and reduces its transmission rate. The
   transmission rate increases again, faster due to the NewReno
   modifications to fast retransmit and fast recovery algorithms, till
   the next sign of congestion. At this point, the value of the
   congestion window parameter is noted again, along with the
   timestamp at which this happens. Continuing like this, we gather
   multiple samples during the time T of the TCP session. These
   samples provide an insight into the unused capacity on the path
   that is available to be used by new applications. These samples
   will be input to the prediction phase where we try to derive
   accurate measurements of the path available bandwidth.

   Note that this TCP measurement phase relies only on information
   readily available at the sender, using the current TCP header, and
   does not require any support from receivers or any network

2.2 Estimation and prediction phase

   Once the bandwidth estimates are obtained by the TCP measurement
   phase, we have to obtain more accurate values for the bandwidth
   samples. For this purpose, we propose to use a filter. Note that
   the samples gathered during the TCP measurement phase are not
   uniformly spaced in time. This is because the samples were gathered
   whenever the congestion was detected by the TCP sender. Assume that
   we obtained N samples of bandwidth estimates from the first
   phase. To use these samples in our filter, we interpolate them to
   obtain a sample distribution of M samples that is uniform in
   time. We denote by T_s the desired time between bandwidth
   samples. The first sample is assumed to be at time 0 and the
   successive (interpolated) samples at time i*T_s are obtained by
   linear interpolation between the two actual samples around the
   timestamp i*T_s, i = 1, ..., M. For example, suppose that we have a
   bandwidth estimate of x at timestamp 3*T_s - 5 and another estimate
   of y at timestamp 3*T_s + 10. Then the sample at 3*T_s will be
   interpolated as x + 5*(y-x)/15. The number of samples M after this
   interpolation will be less than or equal to N depending on the
   timestamps of the actual samples collected during the TCP
   measurement phase. These M samples will correspond to timestamps 0
   to (M-1)*T_s and are denoted by U(i), i = 1 to M,
   respectively. Once we have these uniformly distributed samples, we
   apply a linear filter to them to forecast the next M samples. They
   are given as follows. U(M+i) is given as the sum of U(M-n)*c_i(n)
   for n from 0 to M-1, for i = 1 to M. We find the filter
   coefficients c_i(n) by using the covariance equations method. The
   covariance of the sequence U(i), i = 1 to M can be estimated from
   the samples. The covariance equations can be solved by Gaussian
   elimination or some other faster methods such as triangular
   decomposition etc. Substituting the c_i's back into the linear
   prediction, we find the next M samples, U(M+i), i = 1 to M. From
   these samples, we can obtain the estimate of the available
   bandwidth on the path. The estimate depends on the conservativeness

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   requirements of the application needing the measurement. If the
   application is highly conservative, it needs an upper bound on the
   available bandwidth and then we estimate the available bandwidth as
   the minimum among U(M+i), i = 1 to M. On the other hand, if the
   application is not very conservative, we estimate the available
   bandwidth as the mean of U(M+i), i = 1 to M. This single value is
   the final measurement provided by our scheme. It is not just a
   measurement but also incorporates a prediction and this value will
   be valid for sometime in the future. An extended version of this
   prediction method based on MRTG measurements is given in [6].

3. Enhancements and Evaluations

   Our proposed method, detailed in the previous section, does not
   need any support from the receiver and the network. It probes the
   network on its own. On the other hand, if network support is
   available for a path, we can modify our method to obtain more
   accurate measurements. The method can become less intrusive and
   more fast and accurate. If network support is available, we replace
   the TCP measurement phase with a network probe phase. In this
   phase, we send measurement packets from the source of the path to
   the destination that collect hop information along the path. More
   specifically, they collect the counter information about the number
   of octets that have crossed that hop till then, for each hop. This
   information can be obtained from the MIBs stored in the routers on
   the path. In future networks, information sharing seems desirable
   as the networks can provide the QoS support that users request by
   cooperation. We need to send a small number of such packets to
   obtain repeated samples to calculate the path available
   bandwidth. These samples can then be input to the prediction
   phase. Only modification is that the samples represent the
   utilization of the bottleneck link in the path, unlike the
   available bandwidth samples in the original proposal. Thus, for a
   conservative application, we estimate the available bandwidth of
   the path as the minimum among the set of link available
   bandwidths. The link available bandwidths can be calculated as the
   difference between the link capacity and the maximum among the
   predicted utilization samples for that link.

   The measurement samples can be obtained by utilizing MRTG instead
   of probing the network. But MRTG has the problem that it obtains a
   utilization sample every 5 minutes. This can be too long an
   averaging interval for some applications. For such cases, we have
   developed MRTG++ [6], our own enhanced version of MRTG. MRTG++
   obtains utilization estimates by averaging over intervals of length
   anytime above 10 seconds. 

   The proposed method is accurate, scalable, easy to implement and
   does not need any network support. It is accurate as it obtains
   measurements from the network using a TCP session. If the
   application desiring the measurement needs highly accurate
   information, the TCP measurement phase can be repeated a few times

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   to obtain more samples. Our approach is scalable as the measurement
   samples are obtained by a simple TCP session between the
   end-points. The overhead will not be different if the number of
   hops in the path is increased. Our scheme is easy to implement as
   it involves a simple TCP session initiation, followed by the linear
   prediction. Also, our scheme does not require any network support
   for its operation. 

   It has to be noted that our scheme will have difficulty in
   measuring bandwidth for paths with high capacity links. This is
   because of the limitations in TCP operation like buffer size,
   segment size etc. For paths with high capacity links, our method
   can be extended to include multiple TCP sessions to be initiated in
   parallel. These multiple sessions can probe the network and obtain
   fair share estimates of the path available bandwidth. These
   sub-estimates can then be added to obtain proper estimates for the

4. References

   [1] Lai, K. and Baker, M., "Nettimer: a tool for measuring
       bottleneck link bandwidth," USENIX symposium on Internet
       technologies and systems, 2001.

   [2] Wolski, R., "Dynamically forecasting network performance using
       the network weather service," Journal of cluster computing,
       January 1998.

   [3] Jain, M. and Dovrolis, C., "End-to-end available bandwidth:
       measurement methodology, dynamics and relation with TCP
       throughput," ACM Sigcomm, 2002.

   [4] Stevens, W., "TCP slow start, congestion avoidance, fast
       retransmit, and fast recovery algorithms," RFC 2001, January

   [5] Allman, M., Paxson, V. and Stevens, W., "TCP congestion
       control," RFC 2581, April 1999.

   [6] Anjali, T., Scoglio, C., Chen, L. C., Akyildiz, I. F. and Uhl,
       G., "ABEst: an available bandwidth estimator within an
       autonomous system," IEEE Globecom 2002.

5. Author's Addresses

   Tricha Anjali
   Georgia Institute of Technology
   250 14th Street, Suite 556
   Atlanta, GA 30318 USA
   Phone: +1 404 894 6616

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Available Bandwidth Measurement                  October 2002 

   Caterina Scoglio
   Georgia Institute of Technology
   250 14th Street, Suite 556
   Atlanta, GA 30318 USA
   Phone: +1 404 894 6616

   Ian F. Akyildiz
   Georgia Institute of Technology
   250 14th Street, Suite 568
   Atlanta, GA 30318 USA
   Phone: +1 404 894 5141

   George Uhl
   Swales Aerospace
   Phone: +1 301 614 5155

   Agatino Sciuto
   Swales Aerospace
   Phone: +1 301 614 5155

   Jeffrey A. Smith
   NASA Goddard Space Flight Center
   Phone: +1 301 614 5155

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