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Internet Engineering Task Force                      Anura P. Jayasumana
INTERNET-DRAFT                                 Colorado State University
draft-jayasumana-reorder-density-06.txt               Nischal M. Piratla
                                                   Deutsche Telekom Labs
                                                         Abhijit A. Bare
                                                             Tarun Banka
                                               Colorado State University
                                                            Rick Whitner
                                                          Jerry McCollom
                                                    Agilent Technologies
                                                              March 2006
                                                 Expires: September 2006


   Reorder Density and Reorder Buffer-occupancy Density -  Metrics for
                   Packet Reordering Measurements

Status of this Memo

   By submitting this Internet-Draft, each author represents that any
   applicable patent or other IPR claims of which he or she is aware
   have been or will be disclosed, and any of which he or she becomes
   aware will be disclosed, in accordance with Section 6 of BCP 79.

   This document is an Internet-Draft and is subject to all provisions
   of section 3 of BCP 78.

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Copyright Notice

   Copyright (C) The Internet Society (2006).

Abstract

   Out-of-order arrival of packets is a common occurrence on the
   Internet, and it will become more widespread as link speeds increase.
   A good reorder metric will capture the occurrence and characteristics
   of reordering comprehensively, and will have broader utility than
   merely distinguishing among different reordered sequences.


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   Two metrics for packet reordering are presented, namely, Reorder
   Density (RD) and Reorder Buffer-occupancy Density (RBD).  A threshold
   is used to clearly define when a packet is considered lost, to bound
   computational complexity at O(N), and to keep the memory requirement
   for evaluation independent of N, where N is the length of the packet
   sequence.  RD is a comprehensive metric that captures the
   characteristics of reordering, while RBD evaluates the sequences from
   the point of view of recovery from reordering.  These metrics are
   simple to compute yet comprehensive in their characterization of
   packet reordering.  The measures are robust and orthogonal to packet
   loss and duplication.


Table of Contents

   1.  Introduction and Motivation  . . . . . . . . . . . . . . . . .  3
   2.  Attributes of Packet Reordering Metrics. . . . . . . . . . . .  3
   3.  Reorder Density and Reorder Buffer-occupancy Density . . . . .  5
     3.1  Receive_index (RI)  . . . . . . . . . . . . . . . . . . . .  6
     3.2  Out-of-order Packet . . . . . . . . . . . . . . . . . . . .  6
     3.3  Displacement (D)  . . . . . . . . . . . . . . . . . . . . .  7
     3.4  Displacement Threshold (DT) . . . . . . . . . . . . . . . .  7
     3.5  Displacement Frequency (FD) . . . . . . . . . . . . . . . .  7
     3.6  Reorder Density (RD)  . . . . . . . . . . . . . . . . . . .  8
     3.7  Expected Packet (E) . . . . . . . . . . . . . . . . . . . .  8
     3.8  Buffer Occupancy (B)  . . . . . . . . . . . . . . . . . . .  8
     3.9  Buffer Occupancy Threshold (BT) . . . . . . . . . . . . . .  8
     3.10 Buffer Occupancy Frequency (FB) . . . . . . . . . . . . . .  8
     3.11 Reorder Buffer-Occupancy Density (RBD)  . . . . . . . . . .  8
   4.  Representation of Packet Reordering and Reorder Density  . . .  9
   5.  Selection of DT  . . . . . . . . . . . . . . . . . . . . . . . 10
   6.  Detection of Lost and Duplicate Packets  . . . . . . . . . . . 10
   7.  Algorithms to Compute RD and RBD . . . . . . . . . . . . . . . 11
     7.1  RD Algorithm  . . . . . . . . . . . . . . . . . . . . . . . 11
     7.2  RBD Algorithm . . . . . . . . . . . . . . . . . . . . . . . 13
   8.  Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
   9.  Comparison with Other Metrics  . . . . . . . . . . . . . . . . 18
   10. Security Considerations  . . . . . . . . . . . . . . . . . . . 18
   11. IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 18
   12. Normative References . . . . . . . . . . . . . . . . . . . . . 18
   13. Author's Address . . . . . . . . . . . . . . . . . . . . . . . 20
   Full Copyright Statement . . . . . . . . . . . . . . . . . . . . . 20
   Intellectual Property  . . . . . . . . . . . . . . . . . . . . . . 21









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1. Introduction and Motivation

   Packet reordering is a phenomena that occurs in Internet Protocol
   (IP) networks.  Major causes of packet reordering include, but are
   not limited to, packet stripping at layers 2 and 3 [1,2], priority
   scheduling (e.g., Diffserv), and route fluttering [3,4]. Reordering
   leads to degradation of the performance of many applications [1,6,7].
   For example, perceived voice quality degrades if a Voice over IP
   (VoIP)application receives packets out-of-order packets. Increased
   link speeds, increased parallelism within routers and switches, QoS
   support, and load balancing among links all point to increased packet
   reordering in future networks.

   Effective metrics for reordering are required to measure and quantify
   reordering. A good metric or a set of metrics capturing the nature of
   reordering can be expected to provide insight into the reordering
   phenomenon in networks. It may be possible to use such metrics to
   predict the effects of reordering on applications that are sensitive
   to packet reorder, and perhaps even to compensate for reordering. A
   metric for reordered packets, may also help evaluate network
   protocols and implementations with respect to their impact on packet
   reordering.

   The percentage of out-of-order packets is often used as a metric for
   characterizing reordering.  However, this metric is vague and lacks
   in detail.  Further, there is no uniform definition for the degree of
   reordering of an arrived packet [8,9].  For example, consider the two
   packet sequences (1, 3, 4, 2, 5) and (1, 4, 3, 2, 5).  It is possible
   to interpret the reordering of packets in these sequences
   differently. For example,

   (i)  Packets 2, 3 and 4 are out-of-order in both cases.
   (ii) Only packet 2 is out-of-order in the first sequence, while
        packets 2 and 3 are out-of-order in the second.
   (iii)Packets 3 and 4 are out-of-order in both the sequences.
   (iv) Packets 2, 3 and 4 are out-of-order in the first sequence,
        while packets 4 and 2 are out-of-order in the second sequence.

   In essence, the percentage of out-of-order packets as a metric of
   reordering is subject to interpretation and cannot capture the
   reordering unambiguously and hence, accurately.

   Other metrics attempt to overcome this ambiguity by defining only the
   late packets or only the early packets as being reordered.  However,
   measuring reordering based only on late or only on early packets is
   not always effective.  Consider, for example the sequence (1, 20, 2,
   3,..,19, 21, 22, ...); the only anomaly is that packet 20 is
   delivered immediately after packet 1.  A metric based only on
   lateness will indicate a high degree of reordering, even though in



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   this example it is a single packet arriving ahead of others.
   Similarly, a metric based only on earliness does not accurately
   capture reordering caused by a late arriving packet.  A complete
   reorder metric must account for both earliness and lateness, and must
   be able to differentiate between the two. The inability to capture
   both the earliness and the lateness precludes a metric from being
   useful for estimating end-to-end reordering based on reordering in
   constituent subnets.

   The sensitivity to packet reordering can vary significantly from one
   application to the other. Consider again the packet sequence (1, 3,
   4, 2, 5).  If buffers are available to store packets 3 and 4 while
   waiting for packet 2, an application can recover from reordering.
   However, with certain real-time applications, the arrival of packet 2
   out of order may render it useless. While one can argue that a good
   packet reordering measurement scheme should capture
   application-specific effects, a counter argument can also be made
   that packet reordering should be measured strictly with respect to
   the order of delivery independent of the application.


2. Attributes Packet Reordering Metrics

   The first and foremost requirement of a packet reorder metric is its
   ability to capture the amount and extent of reordering in a sequence
   of packets. The fact that a measure varies with reordering of packets
   in a stream does not make it a good metric. The usefulness and
   effectiveness of a metric will depend on a number of attributes:

   a) Simplicity

      An ideal metric is one that is simple to understand and evaluate,
      and  yet informative, i.e.,  able to provide a complete picture of
      reordering. Percentage of  packets reordered is the simplest
      singleton metric; But the ambiguity in its definition as discussed
      earlier, and its failure to carry the extent of reordering make it
      less informative. On the other hand, keeping track of the
      displacements of each and every packet without compressing the
      data will contain all the information about reordering, but it is
      not simple to evaluate or use.

      A simpler metric may be preferred in some cases even though it
      does not capture reordering completely, while other cases may
      demand more complex, yet complete metric.

      In striving to strike a balance, the lateness based metrics
      consider only the late packets  as reordered, and earliness based
      metrics only the early packets as reordered. A metric based only
      on earliness or only on lateness however captures only a part of



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      information associated with reordering. Consider the sequence (1,
      15, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14). An earliness
      based metric is considers one out of 15 packets as  out of order,
      while a lateness-based metric would indicate all the packets from
      2 to 14 to be out of order, even though it is more likely that a
      single packet arrived early. A metric capturing both early and
      late arrivals in contrast provides a complete picture of
      reordering in a sequence.

   b) Low Sensitivity to Packet Loss and Duplication

      A reorder metric should treat only an out-of-order packet as
      reordered, i.e., if a packet is lost during transit then this
      should not result in its following packets, which arrive in order,
      to be classified as out of order. Consider the sequence (1, 3, 4,
      5, 6). If packet 2 has been lost, the sequence should not be
      considered to contain any out-of-order packets. Similarly, if
      multiple copies of a packet (duplicates) are delivered, this must
      not result in a packet being classified as out of order, as long
      as one copy arrives in the proper position. For example, sequence
      (1, 2, 3, 2, 4, 5) has no reordering. The lost and duplicate
      packet counts may be tracked using metrics specifically to measure
      those, e.g., percentage of lost packets, and percentage of
      duplicate packets.

   c) Low evaluation complexity

      Memory and time complexities associated with evaluating a metric
      play a vital role in implementation and real-time measurements.
      Spatial/memory complexity corresponds to the amount of buffers
      required for the overall measurement process, whereas
      time/computation complexity refers to the number of computation
      steps involved in  computing the amount of reordering in
      a sequence. On-the-fly evaluation of the metric for large streams
      of packets requires the computational complexity to be O(N), where
      N is the number of packets. This allows the metric to be updated
      in constant-time as each packet arrives. In the absence of a
      threshold defining losses or the number of sequence numbers to
      buffer for detection of duplicates, the worst-case complexity of
      loss and duplication detection will increase with N. The rate of
      increase will depend among other things on the value of N and the
      implementation of duplicate detection scheme.

   d) Robustness

      Reorder measurement should be robust against different network
      phenomena and peculiarities in measurement or sequences  such as a
      very late arrival of a duplicate packet, or even a rogue packet
      due to an  error or sequence number wrap-around. The impact due to



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      an event associated with a single or a small number of packets
      should have a sense of proportionality on the reorder measure.
      Consider for example, the arrival sequence: (1, 5430, 2, 3, 4,
      5,..) where packet 5430 appears to be very early; it may be either
      due to sequence rollover in test streams or some unknown reason.

   e) Broad applicability

      A framework for IP performance metrics [5] states: "The metrics
      must aid users and providers in understanding the performance
      they experience or provide."

      Rather than being a mere value or a set of values that changes
      with the reordering of packets in a stream, a reorder metric
      should be useful for a variety of purposes. An application or a
      transport protocol implementation, for example, may be able to use
      the reordering information to allocate resources to recover from
      reordering. A metric may be  useful for  TCP flow control, buffer
      resource allocation for recovery  from reordering  and /or network
      diagnosis.

      The ability to combine the reorder metrics of constituent subnets
      to provide the end to end reordering would be an extremely useful
      property. In the absence of this property, no amount of individual
      network measurements short of measuring the reordering for the
      pair of endpoints of interest would be useful in predicting the
      end-to-end reordering.


3. Reorder Density and Reorder Buffer-occupancy Density

   In this memo, we define two discrete density functions, Reorder
   Density (RD) and Reorder Buffer-occupancy Density (RBD), that capture
   the nature of reordering in a packet stream.  These two metrics can
   be used individually or collectively to characterize the reordering
   in a packet stream. Also presented are algorithms for real-time
   evaluation of these metrics for an incoming packet stream.

   RD is defined as the distribution  of displacements of packets from
   their original positions, normalized with respect to the number of
   packets. An early packet corresponds to a negative displacement and
   a late packet to a positive displacement. Lost and duplicate packets
   are accounted for when evaluating these displacements.

   The ability of RD to capture the nature and properties of reordering
   in a comprehensive manner has been demonstrated in [9, 10, 15]. The
   RD observed at the output port of a subnet when the input is an
   in-order packet stream, can be viewed as a "reorder response" of a
   network, a concept somewhat similar to the "system response"  or



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   "impulse response" used in traditional system theory. For a subnet
   under stationary conditions, RD is the probability density of the
   packet displacement. RD measured on individual subnets can be
   combined, using the convolution operation,  to predict the end-to-end
   reorder characteristics of the network formed by the cascade of
   subnets under a fairly broad set of conditions [10]. RD also shows
   significant promise as a tool for analytical modeling of reordering,
   as demonstrated with a load-balancing scenario in [17].  Use of a
   threshold to define the condition under which a packet is considered
   lost, makes the metric robust, efficient and adaptable for different
   network and stream characteristics.

   RBD is the normalized histogram of the occupancy of a hypothetical
   buffer that would allow the recovery from out-of-order delivery of
   packets.  If an arriving packet is early, it is added to a
   hypothetical buffer until it can be released in order [8].  The
   occupancy of this buffer after each arrival is used as the measure of
   reordering.  A threshold, used to declare a packet as lost, keeps the
   complexity of computation within bounds. The threshold may be
   selected based on application requirements in  situations where the
   late arrival of a packet makes it  useless, e.g., a real-time
   applications.  In [8], this metric was called RD and buffer occupancy
   was known as displacement.

   RD and RBD are simple, yet useful,  metrics that for measurement and
   evaluation of reordering.  These metrics are robust against many
   peculiarities, such as those discussed previously , and have a
   computational complexity of O(N), where N is  the received sequence
   size.  RD is orthogonal to loss and duplication, whereas RBD is
   orthogonal to duplication.

   A detailed comparison of these and other proposed metrics for
   reordering is presented in [15].

   The following terms are used to formally define RD, RBD, and the
   measurement algorithms.  Wraparound of sequence numbers is not
   explicitly addressed in this document, but with the use of modulo-N
   arithmetic, all claims made here remain valid in the presence of
   wraparound.


3.1 Receive_index (RI)

   Consider a sequence of packets (1, 2, ..., N) transmitted over a
   network.  A receive_index RI (1, 2, ...), is a value assigned
   to a packet as it arrives at its destination according to the order
   of arrival.  A receive_index is not assigned to duplicate packets,
   and the receive_index value skips the value corresponding to a lost
   packet. (The detection of loss and duplication for this purpose is
   described in section 6.)  In the absence of reordering, the sequence


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   number of the packet and the receive_index are the same for each
   packet.

   RI is used to compute earliness and lateness of an arriving packet.
   Below are two examples of received sequences with receive_index
   values for a sequence of 5 packets (1, 2, 3, 4, 5) arriving out of
   order:

   Example 1:
   Arrived sequence:    2   1   4   5    3
   Receive_index:       1   2   3   4    5

   Example 2:
   Arrived sequence:    1   4   3   5    3
   Receive_index:       1   3   4   5    -

   In Example 1, there is no loss or duplication.  In Example 2, the
   packet with sequence number 2 is lost, thus 2 is not assigned as an
   RI; packet 3 is duplicated, thus the second copy is not assigned an
   RI.

3.2 Out-of-Order Packet

   When the sequence number of a packet is not equal to the RI assigned
   to it, it is considered an out-of-order packet.  Duplicates for which
   an RI is not defined are ignored.

3.3 Displacement (D)

   Displacement (D) of a packet is defined as the difference between RI
   and the sequence number of the packet, i.e., the displacement of
   packet i is RI[i] - i.  Thus, a negative displacement indicates the
   earliness of a packet and a positive displacement to the lateness.
   In example 3 below, an arrived sequence with displacements of each
   packet is illustrated.

   Example 3:
   Arrived sequence:    1   4   3   5   3   8   7   6
   Receive_index:       1   3   4   5   -   6   7   8
   Displacement:        0  -1   1   0   -  -2   0   2

3.4 Displacement Threshold (DT)

   The displacement threshold is a threshold on the displacement of
   packets that allows the metric to classify a packet as lost or
   duplicate.  Determining when to classify a packet as lost is
   difficult because there is no point in time at which a packet can
   definitely be classified as lost; the packet may still arrive after
   some arbitrarily long delay.  However, from a practical point of
   view, a packet may be classified as lost if it has not arrived within


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   a certain administratively defined displacement threshold, DT.
   Similarly, to identify a duplicate packet, it is theoretically
   necessary to keep track of all the arrived (or missing) packets.
   Again, however, from a practical point of view, missing packets
   within a certain window of sequence numbers suffice.  Thus, DT is
   used as a practical means for declaring a packet as lost or
   duplicated.  DT  makes the metric more robust, keeps the
   computational complexity for long sequences within O(N), and keeps
   storage requirements independent of N.

   If DT is selected too small, reordered packets might be classified as
   lost.  A large DT will increase both the size of memory required to
   keep track of sequence numbers and the length of computation time
   required to evaluate the metric.  Indeed, it is possible to use two
   different thresholds for the two cases.  The selection of DT is
   further discussed in section 5.

3.5 Displacement Frequency (FD)

   Displacement Frequency FD[k] is the number of arrived packets having
   a displacement of k, where k takes values from -DT to DT.

3.6 Reorder Density (RD)

   RD is defined as the distribution of the Displacement Frequencies
   FD[k], normalized with respect to N', where N'is the length of the
   received sequence, ignoring lost and duplicate packets. N' is equal
   to the sum(FD[k]) for k in [-DT, DT].

3.7 Expected Packet (E)

   A packet with sequence number E is expected if E is the largest
   number such that all the packets with sequence numbers less than E
   have already arrived or have been determined to be lost.

3.8 Buffer Occupancy (B)

   An arrived packet with a sequence number greater than that of an
   expected packet is considered to be stored in a hypothetical buffer
   sufficiently long to permit recovery from reordering.  At any packet
   arrival instant, the buffer occupancy is equal to the number of
   out-of-order packets in the buffer, including the newly arrived
   packet. One buffer location is assumed for each packet, although it
   is possible to extend the concept to the case where the number of
   bytes is used for buffer occupancy.  For example, consider the
   sequence of packets (1, 2, 4, 5, 3) with expected order (1, 2, 3, 4,
   5). When packet 4 arrives the buffer occupancy is 1 because packet 4
   arrived early.  Similarly, the buffer occupancy becomes 2 when packet
   5 arrives.  When packet 3 arrives, recovery from reordering occurs
   and the buffer occupancy reduces to zero.


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3.9 Buffer Occupancy Threshold (BT)

   Buffer occupancy threshold is a threshold on the maximum size of the
   hypothetical buffer that is used for recovery from reordering.  As
   with the case of DT for RD, BT is used for loss and duplication
   classification for Reorder Buffer-occupancy Density (RBD) computation
   (see section 3.11). BT provides robustness, and limits the
   computation complexity of RBD.

3.10 Buffer Occupancy Frequency (FB)

   At the arrival of each packet the buffer occupancy may take any value
   k ranging from 0 to BT.  The buffer occupancy frequency FB[k] is the
   number of arrival instances after which the occupancy takes the value
   of k.

3.11 Reorder Buffer-Occupancy Density (RBD)

   Reorder buffer-occupancy density is the buffer occupancy frequencies
   normalized by the total number of non-duplicate packets, i.e.,
   RBD[k] = FB[k]/N' where N' is the length of the received sequence,
   ignoring excessively delayed (deemed lost) and duplicate packets.  N'
   is also the sum(FB[k]) for all k such that k belongs to [0, BT].


4. Representation of Packet Reordering and Reorder Density

   Consider a sequence of packets (1, 2, ..., N).  Let the RI assigned
   to packet m be the sequence number m plus some non-negative offset
   dm, i.e., (m + dm).  A reorder event of packet m is represented by
   r(m, dm).  When dm is not equal to zero, a reorder event is said to
   have occurred.  A packet is late if dm > 0 and early if dm < 0.
   Thus, packet reordering of a sequence of packets is completely
   represented by the union of reorder events, R, referred to as the
   reorder set:
                R = {r(m,dm)| dm not equal to 0 for all m}

   If there is no reordering in a packet sequence then R is the null
   set.

   Examples 4 and 5 illustrate the reorder set:

   Example 4. No losses or duplicates

   Arrived Sequence     1       2       3       5       4       6
   Receive_index        1       2       3       4       5       6
   Displacement         0       0       0      -1       1       0
   R = {(4,1), (5,-1)}




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   Example 5. Packet 4 is lost and 2 is duplicated

   Arrived Sequence     1       2       5       3       6       2
   Receive_index        1       2       3       5       6       -
   Displacement         0       0       -2      2       0       -
   R = {(3, 2), (5, -2)}


   RD is defined as the discrete density of the frequency of packets
   with respect to their displacements, i.e., the lateness and earliness
   from the original position.  Let S[k] denote the set of reorder
   events in R with displacement equal to k, i.e.,

               S[k]= {r(m, dm)| dm = k}

   Let |S[k]| be the cardinality of set S[k].  Thus, RD[k] is defined as
   |S[k]| normalized with respect to the total number of received
   packets (N').  Note that N' does not include duplicates or lost
   packets.

              RD[k]  = |S[k]| / N' for k not equal to zero.

   RD[0] corresponds to the packets for which RI is the same as the
   sequence number:

              RD[0] = 1 - sum(|S[k]| / N')

   As defined previously, FD[k] is the measure that keeps track of
   |S[k]|.

5. Selection of DT

   Although assigning a threshold for determining lost and duplicate
   packets might appear to introduce error into the reorder metrics, in
   practice this need not be the case.  Applications, protocols, and the
   network itself operate within finite resource constraints which
   introduce practical limits beyond which the choice of certain values
   become irrelevant.  In the case of DT, it is common to find a value
   above which DT does not have an impact on the reorder metrics.  For
   example, in case of a VoIP application with a bit-rate of 128kbps and
   packet size of 200 bytes, a practical DT value can be determined as
   follows.  Assume that the application can wait a maximum of 50
   ms for an expected packet and that the packets arrive at constant
   rate. Within 50 ms, the application can receive
   (128*1000*0.05)/(200*8), i.e., 4 packets.  Since packets arriving
   after this duration are effectively lost, the DT value could be set
   at 4.  If the operational nature of an application is such that a DT
   can be defined, then using DT in the computation of reorder metrics
   will not invalidate nor limit the effectiveness of the metrics, i.e.,
   increasing DT does not provide any benefit.  In the case of TCP, the


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   transmit and receive window sizes impose a natural limit on the
   useful value of DT. Sequence number wraparound may provide a useful
   upper bound for DT in some instances.

   If there are no operational constraints imposed by factors as
   described above, or if one is purely interested in a more complete
   picture of reordering, then DT can be made as large as required.  If
   DT is equal to the length of the packet sequence (worst case
   scenario), a complete picture of reordering is seen. Any metric that
   does not rely on a threshold to declare a packet as lost, implicitly
   makes one of two assumptions: a) A missing packet is not considered
   lost until the end of the sequence, or b) the packet is considered
   lost until it arrives.  Former corresponds to the case where DT is
   set to the length of the sequence.   Latter leads to many problems
   related to complexity and robustness.

6. Detection of Lost and Duplicate Packets

   In RD, a packet is considered lost if it is late beyond DT.
   Non-duplicate arriving packets do not have a copy in the buffer and
   do not have a sequence number less (earlier) than E.  In RBD, a
   packet is considered lost if the buffer is filled to its threshold
   BT. A packet is considered a duplicate when the sequence number is
   less than the expected packet, or if the sequence number is already
   in the buffer.

   Since RI skips the sequence number of a lost packet, the question
   arises as to how to assign an RI to   subsequent packets that arrive
   before it is known that the packet is lost.  This problem arises only
   when reorder metrics are calculated in real-time for an incoming
   sequence, and not with offline computations.  This concern can be
   handled in one of two ways:

   a) Go-back Method:  RD is computed as packets arrive.  When a packet
   is deemed lost, RI values are corrected and displacements recomputed.
   The Go-back Method is only invoked when a packet is lost, and
   re-computing involves at most DT packets.

   b) Stay-back Method:  RD evaluation lags the arriving packets so that
   the correct RI and E values can be assigned to each packet as it
   arrives.  Here, RI is assigned to a packet only once, and the value
   assigned is guaranteed to be correct.  In the worst case, the
   computation lags arriving packet  by DT.  The lag associated with the
   Stay-back Method is incurred only when a packet is missing.

   Another issue related to a metric and its implementation is the
   robustness against peculiarities that may occur in a sequence as
   discussed in Section 2.  Consider for example, the arrival sequence:
   (1, 5430, 2, 3, 4, 5,...).  With RD, a sense of proportionality is
   maintained easily using the concept of threshold (DT) and to limit


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   the effect of a rogue packet to this threshold.  With RD, for
   example, as its displacement is greater than threshold, it is
   discarded.   This way the impact due to the rogue packet, 5430, is
   limited at most to DT packets, thus imposing a limit on the amount of
   error it can cause in results. Note also that a threshold different
   from DT can be used for this purpose. By limiting the time a packet
   to remain in the buffer according to a prespecified threshold, RBD
   can be made robust against rogue packets as well.


7. Algorithms to evaluate RD and RBD

   The algorithms to compute RD and RBD are given below.  These
   algorithms are applicable for on-line computation of an incoming
   packet stream, and provide an up-to-date metric for the packet stream
   so far.  For simplicity, the sequence numbers are considered to start
   from 1 and continue in increments of 1. Only the Stay-back Method of
   loss detection is presented here, hence the RD values lag by a
   maximum of DT. Algorithm for go-back method is given in [14]. Perl
   scripts for these algorithms are posted in [11].

7.1 Algorithm for RD

   Variables used:
   -------------------------------------------------------------------
    RI: receive_index.
    S: Arrival under consideration for lateness/earliness computation.
    D: Lateness or earliness of the packet being processed.
    FD[ -DT..DT]: Frequency of lateness and earliness.
    window[1..DT+1]: List of incoming sequence numbers.
    buffer[1..DT]: Array to hold sequence numbers of early arrivals.
    window[] and buffer[] are empty at the beginning.
   ===================================================================

   Step 1. Initialize:

        Store first unique DT+1 sequence numbers in arriving order into
        window;
        RI = 1;

   Step 2. Repeat (until window is empty):

        If (window or buffer contains sequence number RI)
        {
           Copy first sequence number in window to S;
           Delete first sequence number from window;
           D = RI - S; # compute displacement




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           If (absolute(D) <= DT) # Apply threshold
           {
              FD[D]++; # Update frequency

              If (buffer contains sequence number RI)
                 Delete RI from buffer;

              If (D < 0) # Early Arrival
                 add S to empty slot in buffer;
              RI++; # Update RI value
           }

           Else # Displacement beyond threshold.
           {
              Discard S;
           }
           # Get next incoming non-duplicate sequence number, if any.
           newS = get_next_arrival(); # subroutine called*
           if (newS != null)
           {
                add newS to window;
           }
           if (window is empty) go to step 3;
        }
        Else # RI not found. Get next RI value.
        {
           # Next RI is the minimum among window and buffer contents.
           m = minimum (minimum (window), minimum (buffer));
           If (RI < m)
              RI = m;
           Else
              RI++;
        }

   Step 3. Normalize FD to get RD;

# Get a new sequence number from packet stream, if any
   subroutine get_next_arrival()
   {
        do   # get non-duplicate next arrival
        {
              newS = new sequence from arriving stream;
              if (newS == null) # End of packet stream
                 return null;
        } while (newS < RI or newS in buffer or newS in window);

        return newS;
   }




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7.2 RBD Algorithm

   Variables used:
   ---------------------------------------------------------------------
   # E : Next expected sequence number.
   # S : Sequence number of the packet just arrived.
   # B : Current buffer occupancy.
   # BT: Buffer Occupancy threshold.
   # FB[i]: Frequency of buffer occupancy i  (0 <= i <= BT).
   # in_buffer(N) : True if the packet with sequence number N is
     already stored in the buffer.
   =====================================================================

   1.  Initialize E = 1, B = 0 and FB[i] = 0 for all values of i.

   2.  Do the following for each arrived packet.

          If (in_buffer(S) || S < E) /*Do nothing*/;
          /* Case a: S is a duplicate or excessively delayed packet.
          Discard the packet.*/
          Else
          {

             If (S == E)
             /* Case b: Expected packet has arrived.*/
             {
                E = E + 1;
                While (in_buffer(E))
                {
                   B = B - 1; /* Free buffer occupied by E.*/
                   E = E + 1; /* Expect next packet.*/
                }
                FB[B] = FB[B] + 1; /*Update frequency for buffer
                occupancy B.*/
             } /* End of ElseIf (S == E)*/

          ElseIf (S > E)
             /* Case c: Arrived packet has a sequence number higher
                than expected.*/
             {
                If (B < BT)
                /* Store the arrived packet in a buffer.*/
                   B = B + 1;
                Else
                /* Expected packet is delayed beyond the BT.
                Treat it as lost.*/
                {
                   Repeat
                   {



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                      E = E + 1;
                   }
                   Until (in_buffer(E) || E == S);

                   While (in_buffer(E) || E == S)
                   {
                      if (E != S) B = B - 1;
                      E = E + 1;
                   }
                 }
                 FB[B] = FB[B] + 1; /*Update frequency for buffer
                 occupancy B.*/
             } /* End of ElseIf (S > E)*/

          }

   3. Normalize FB[i] to obtain RBD[i], for all values of i using

                            FB[i]
      RBD[i] = ----------------------------------
                  Sum(FB[j] for 0 <= j <= BT)

8. Examples

   a. Scenario with no packet loss

   Consider the sequence of packets (1, 4, 2, 5, 3, 6, 7, 8) with
   DT = BT = 4.

   Tables 1 and 2 show the computational steps when the RD algorithm is
   applied to the above sequence.

   ------------------------------------------------------
   Table 1: Late/Early-packet Frequency computation steps
   ------------------------------------------------------
   S         1     4     2     5     3     6   7    8
   RI        1     2     3     4     5     6   7    8
   D         0    -2     1    -1     2     0   0    0
   FD[D]     1     1     1     1     1     2   3    4
   ------------------------------------------------------
   (S, RI,D and FD[D] as described in section 7.1)
   ------------------------------------------------------

   The last row (FD[D]) represents the current frequency of occurrence
   of the displacement D, e.g., column 3 indicates FD[1] = 1 while
   column 4 indicates FD[-1] = 1.  The final set of values for RD are
   shown in Table 2.





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   -------------------------------------------------
   Table 2: Reorder Density (RD)
   -------------------------------------------------
     D       -2       -1      0     1       2
   FD[D]      1        1      4     1       1
   RD[D]     0.125   0.125   0.5   0.125   0.125
   -------------------------------------------------
   (D,FD[D] and RD[D] as described in section 7.1)
   -------------------------------------------------

   Tables 3 and 4 illustrate the computational steps for RBD for the
   same example.

   ------------------------------------------------------------
   Table 3: Buffer occupancy frequencies (FB) computation steps
   ------------------------------------------------------------
   S         1     4     2     5     3     6     7     8
   E         1     2     2     3     3     6     7     8
   B         0     1     1     2     0     0     0     0
   FB[B]     1     1     2     1     2     3     4     5
   ------------------------------------------------------------
   (E,S,B and FB[B] as described in section 7.2)
   ------------------------------------------------------------

   ------------------------------------------------------------------
   Table 4: Reorder Buffer-occupancy Density
   ------------------------------------------------------------------
   B           0        1     2
   FB[B]       5        2     1
   RBD[B]     0.625   0.25  0.125
    -----------------------------------------------------------------
   (B,FB[B] and RBD[B] as discussed in section 7.2)
   ------------------------------------------------------------------

  Graphical representations of the densities are as follows:


                ^                            ^
                |                            |
                |                            _
    ^       0.5 _                   ^ 0.625 | |
    |          | |                  |       | |
               | |                          | |
   RD[D]       | |                RBD[B]    | | - o.25
          _  _ | | _  _ 0.125               | || | - 0.125
         | || || || || |                    | || || |
        --+--+--+--+--+--+-->             ---+--+--+--
         -2 -1  0  1  2                      0  1  2
                D  -->                        B -->



Jayasumana, et al.                                             [Page 17]

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   b. Scenario with packet loss

   Consider a sequence of 6 packets (1, 2, 4, 5, 6, 7) with DT = BT = 3.
   Table 5 shows the computational steps when the RD algorithm is
   applied to the above sequence to obtain FD[D].

   ------------------------------------------------------
   Table 5: Late/Early-packet Frequency computation steps
   ------------------------------------------------------
   S         1     2     4     5     6     7
   RI        1     2     4     5     6     7
   D         0     0     0     0     0     0
   FD[D]     1     2     3     4     5     6
   ------------------------------------------------------
   (S,RI,D and FD[D] as described in section 7.1)
   ------------------------------------------------------

   Table 6 illustrates the FB[B] for the above arrival sequence.

   -------------------------------------------------
   Table 6: Buffer occupancy computation steps
   -------------------------------------------------
   S        1     2     4     5     6     7
   E        1     2     3     3     3     7
   B            0     0     1     2     3     0
   FB[B]    1     2     1     1     1     3
   -------------------------------------------------
   (E,S,B and FB[B] as described in section 7.2)
   -------------------------------------------------

   Graphical representations of RD and RBD for the above sequence are as
   follows.

             ^                        ^
             |                        |
       1.0   _                        |
   ^        | |                ^      |
   |        | |                | 0.5  _
            | |                      | |
 RD[D]      | |               RBD[B] | | _  _  _ 0.167
            | |                      | || || || |
        --+--+--+-->                --+--+--+--+-->
         -1  0  1                     0  1  2  3
             D  -->                      B -->

   c.  Scenario with duplicate packets

   Consider a sequence of 6 packets (1, 3, 2, 3, 4, 5) with DT = 2.
   Tables 7 shows the computational steps when the RD algorithm is
   applied to the above sequence to obtain FD[D].


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   ------------------------------------------------------
   Table 7: Late/Early-packet Frequency computation steps
   ------------------------------------------------------
   S         1     3     2     3     4     5
   RI        1     2     3     -     4     5
   D         0    -1     1     -     0     0
   FD[D]     1     1     1     -     2     3
   ------------------------------------------------------
   (S, RI,D and FD[D] as described in section 7.1)
   ------------------------------------------------------

   Table 8 illustrates the FB[B] for the above arrival sequence.

   ------------------------------------------------------
   Table 8: Buffer Occupancy Frequency computation steps
   ------------------------------------------------------
   S     1     3     2     3     4     5
   E     1     2     2     -     4     5
   B     0     1     0     -     0     0
   FB[B] 1     1     2     -     3     4
   ------------------------------------------------------
   (E,S,B and FB[B] as described in section 7.2)
   ------------------------------------------------------

   Graphical representations of RD and RBD for the above sequence
   are as follows:


              ^                            ^
              |                            |
  ^           |                   ^   0.8  _
  |       0.6 _                   |       | |
             | |                          | |
 RD[D]       | |                RBD[B]    | |
       0.2 _ | | _ 0.2                    | | _ 0.2
          | || || |                       | || |
      --+--+--+--+--+--+-->             ---+--+--+--
       -2 -1  0  1  2                      0  1  2
              D  -->                        B -->


9. Comparison with Other Metrics

   RD and RBD are compared to other metrics that are being proposed [12]
   in [15]. This section is for review purposes only and will be removed
   from the final draft.






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10. Security Considerations

   This document does not define any protocol. The metric definition per
   se is believed to have no security implications.

11. IANA Considerations

   This document requires nothing from the IANA.

12. References

   [1]  J. C. R. Bennett, C. Partridge and N. Shectman, "Packet
        Reordering is Not Pathological Network Behavior," IEEE/ACM
        Trans. on Networking , Dec. 1999, pp.789-798.

   [2]  S. Jaiswal, G. Iannaccone, C. Diot, J. Kurose and D. Towsley,
        "Measurement and Classification of Out-of-sequence Packets in
        Tier-1 IP Backbone," Proc. IEEE INFOCOM, Mar. 2003, pp. 1199-
        1209.

   [3]  V.Paxson, "Measurements and Analysis of End-to-End Internet
        Dynamics," Ph.D. Dissertation, U.C. Berkeley, 1997,
        ftp://ftp.ee.lbl.gov/papers/vp-thesis/dis.ps.gz.

   [4]  S. Bohacek, J. Hespanha, J. Lee, C. Lim and K.Obraczka, "TCP-PR:
        TCP for Persistent Packet Reordering," Proc. of the IEEE 23rd
        ICDCS, May 2003, pp.222-231.

   [5]  V. Paxson, G. Almes, J. Madhavi and M. Mathis, "Framework for IP
        Performance Metrics," RFC 2330.

   [6]  E. Blanton and M. Allman, "On Making TCP More Robust to Packet
        Reordering," ACM Computer Comm. Review, 32(1), Jan. 2002, pp.20-
        30.

   [7]  M. Laor and L. Gendel, "The Effect of Packet Reordering in a
        Backbone Link on Application Throughput," IEEE Network,
        Sep./Oct. 2002, pp.28-36.

   [8]  T. Banka, A. A. Bare, A. P. Jayasumana, "Metrics for Degree of
        Reordering in Packet Sequences", Proc. 27th IEEE Conference on
        Local Computer Networks, Tampa, FL, Nov. 2002.

   [9]  N. M. Piratla, "A Theoretical Foundation, Metrics and Modeling
        of Packet Reordering and Methodology of Delay Modeling using
        Inter-packet Gaps," Ph.D. Dissertation, Department of
        Electrical and Computer Engineering, Colorado State University,
        Fort Collins, CO, Fall 2005.




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   [10] N. M. Piratla, A. P. Jayasumana and A. A. Bare, "RD: A
        Formal, Comprehensive Metric for Packet Reordering," Proc. 4th
        International IFIP-TC6 Networking Conference (Networking 2005),
        Waterloo, Canada, May 2-6, 2005, LNCS 3462, pp: 78-89.

   [11] Perl Scripts for RLED and RBD,
        http://www.cnrl.colostate.edu/Reorder_Density.html,
        Last modified on Jul. 18, 2004.

   [12] A. Morton, L. Ciavattone, G. Ramachandran, S.Shalunov and
        J.Perser, "Packet Reordering Metric for IPPM", Internet Draft,
        <draft-ietf-ippm-reordering-08.txt>, December 2004.

   [13] M. Zhang, B. Karp, S. Floyd and L. Peterson, "RR-TCP: A
        Reordering-Robust TCP with DSACK," Proc. 11th IEEE International
        Conference on Networking Protocols (ICNP 2003), Atlanta, GA,
        Nov. 2003, pp. 95-106.

   [14] A. A. Bare, "Measurement and Analysis of Packet Reordering Using
        Reorder Density," Masters Thesis, Department of Computer
        Science, Colorado State University, Fort Collins, Colorado, Fall
        2004.

   [15] N. M. Piratla, A. P. Jayasumana and A. A. Bare, "A Comparative
        Analysis of Packet Reordering Metrics," Proc. COMSWARE,
        New Delhi, India, Jan. 2006.

   [16] N. M. Piratla, A. P. Jayasumana and T. Banka, "On Reorder
        Density and its Application to Characterization of Packet
        Reordering," Proc. 30th IEEE Local Computer Networks Conference
        (LCN 2005), Sydney, Australia, Nov. 2005.

   [17] N. M. Piratla and A. P. Jayasumana, "Reordering of Packets due
        to Multipath Forwarding - An Analysis," To appear in Proc.
        ICC 2006, Istanbul, Turkey, Jun. 2006.


13. Authors' Addresses

   Anura P. Jayasumana <Anura.Jayasumana@colostate.edu>
   Computer Networking Research Laboratory,
   Department of Electrical and Computer Engineering,
   1373 Colorado State University,
   Fort Collins, CO  80523, USA

   Nischal M. Piratla <Nischal.Piratla@telekom.de>
   Deutsche Telekom Laboratories
   Ernst-Reuter-Platz 7,
   D-10587 Berlin, Germany



Jayasumana, et al.                                             [Page 21]

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   Abhijit A. Bare <abhijit_bare@agilent.com>
   Tarun Banka <Tarun.Banka@colostate.edu>
   Computer Networking Research Laboratory,
   Department of Electrical and Computer Engineering,
   1373 Colorado State University,
   Fort Collins, CO  80523, USA

   Rick Whitner <rick_whitner@agilent.com>
   Jerry McCollom <jerry_mccollom@agilent.com>
   Agilent Technologies, 4380 Ziegler Rd.,
   Fort Collins, CO  80525, USA

   Expiration Date:  September 2006


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Jayasumana, et al.                                             [Page 22]

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