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Traffic Engineering Working Group                           Wai Sum Lai
Internet Draft - Updates RFC 3564                             AT&T Labs
Document: <draft-wlai-tewg-bcmodel-06.txt>                 January 2005
Category: Informational


                     Bandwidth Constraints Models for
          Differentiated Services-aware MPLS Traffic Engineering:
                          Performance Evaluation


Status of this Memo

   By submitting this Internet-Draft, I certify that any applicable
   patent or other IPR claims of which I am aware have been disclosed,
   or will be disclosed, and any of which I become aware will be
   disclosed, in accordance with RFC 3668.

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Copyright Notice

   Copyright (C) The Internet Society (2004).  All Rights Reserved.


Abstract

   The Differentiated Services (Diffserv)-aware MPLS Traffic
   Engineering Requirements RFC 3564 specifies the requirements and
   selection criteria for Bandwidth Constraints Models.  Two such
   models, the Maximum Allocation and the Russian Dolls, are described
   therein.  This document complements RFC 3564 by presenting the
   results of a performance evaluation of these two models under
   various operational conditions: normal load, overload, preemption
   fully or partially enabled, pure blocking, or complete sharing.



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Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC-2119.


Table of Contents

   Status of this Memo................................................1
   Copyright Notice...................................................1
   Abstract...........................................................1
   1. Introduction....................................................2
   2. Bandwidth Constraints Models....................................4
   3. Performance Model...............................................5
   3.1 LSP Blocking and Preemption....................................5
   3.2 Example Link Traffic Model.....................................7
   3.3 Performance Under Normal Load..................................8
   4. Performance Under Overload......................................9
   4.1 Bandwidth Sharing Versus Isolation.............................9
   4.2 Improving Class 2 Performance at the Expense of Class 3.......10
   4.3 Comparing Bandwidth Constraints of Different Models...........12
   5. Performance Under Partial Preemption...........................13
   5.1 Russian Dolls Model...........................................14
   5.2 Maximum Allocation Model......................................14
   6. Performance Under Pure Blocking................................15
   6.1 Russian Dolls Model...........................................15
   6.2 Maximum Allocation Model......................................15
   7. Performance Under Complete Sharing.............................16
   8. Implications on Performance Criteria...........................17
   9. Conclusions....................................................18
   10. Security Considerations.......................................19
   11. IANA Considerations...........................................19
   12. References....................................................19
   12.1 Normative References.........................................19
   12.2 Informative References.......................................19
   13. Acknowledgments...............................................20
   14. Author's Address..............................................20
   15. Intellectual Property Considerations..........................21
   Copyright Notice and Disclaimer...................................21


1. Introduction

   Differentiated Services (Diffserv)-aware MPLS Traffic Engineering
   (DS-TE) mechanisms operate on the basis of different Diffserv
   classes of traffic to improve network performance.  Requirements for
   DS-TE and the associated protocol extensions are specified in
   references [1, 2], respectively.

   To achieve per-class traffic engineering, rather than on an
   aggregate basis across all classes, DS-TE enforces different
   Bandwidth Constraints (BCs) on different classes.  Reference [1]

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   specifies the requirements and selection criteria for Bandwidth
   Constraints Models (BCMs) for the purpose of allocating bandwidth to
   individual classes.

   This document presents a performance analysis for the two BCMs
   described in [1]:

   (1) Maximum Allocation Model (MAM) - the maximum allowable bandwidth
   usage of each class, together with the aggregate usage across all
   classes, are explicitly specified.
   (2) Russian Dolls Model (RDM) - specification of maximum allowable
   usage is done cumulatively by grouping successive priority classes
   recursively.

   The following criteria are also listed in [1] for investigating the
   performance and trade-offs of different operational aspects of BCMs:

   (1) addresses the scenarios in Section 2 (of [1])
   (2) works well under both normal and overload conditions
   (3) applies equally when preemption is either enabled or disabled
   (4) minimizes signaling load processing requirements
   (5) maximizes efficient use of the network
   (6) minimizes implementation and deployment complexity

   The use of any given BCM has significant impacts on the capability
   of a network to provide protection for different classes of traffic,
   particularly under high load, so that performance objectives can be
   met [3].  This document complements [1] by presenting the results of
   a performance evaluation of the above two BCMs under various
   operational conditions: normal load, overload, preemption fully or
   partially enabled, pure blocking, or complete sharing.  Thus, our
   focus is only on the performance-oriented criteria and their
   implications for a network implementation.  In other words, we are
   only concerned with criteria (2), (3), and (5); we will not address
   criteria (1), (4), or (6).

   Related documents in this area include [4, 5, 6, 7, 8].

   In the rest of this document, the following DS-TE acronyms are used:
   BC         Bandwidth Constraint
   BCM        Bandwidth Constraints Model
   MAM        Maximum Allocation Model
   RDM        Russian Dolls Model

   There may be differences between the quality of service expressed
   and obtained with Diffserv without DS-TE and with DS-TE.  Because
   DS-TE uses Constraint Based Routing, and because of the type of
   admission control capabilities it adds to Diffserv, DS-TE has
   capabilities for traffic that Diffserv does not: Diffserv does not
   indicate preemption, by intent, whereas DS-TE describes multiple
   levels of preemption for its Class-Types.  Also, Diffserv does not
   support any means of explicitly controlling overbooking, while DS-TE

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   allows this.  When considering a complete quality of service
   environment, with Diffserv routers and DS-TE, it is important to
   consider these differences carefully.


2. Bandwidth Constraints Models

   To simplify our presentation, we use the informal name "class of
   traffic" for the terms Class-Type and TE-Class defined in [1].  We
   assume that (1) there are only three classes of traffic, and (2) all
   label-switched paths (LSPs), regardless of class, require the same
   amount of bandwidth.  Furthermore, the focus is on the bandwidth
   usage of an individual link with a given capacity; routing aspects
   of LSP setup are not considered.

   The concept of reserved bandwidth is also defined in [1] to account
   for the possible use of overbooking.  Rather than getting into these
   details, we assume that each LSP is allocated 1 unit of bandwidth on
   a given link after establishment.  This allows us to express link
   bandwidth usage simply in terms of the *number of simultaneously
   established LSPs*.  Link capacity can then be used as the aggregate
   constraint on bandwidth usage across all classes.

   Suppose that the three classes of traffic assumed above for the
   purpose of this document are denoted by class 1 (highest priority),
   class 2, and class 3 (lowest priority).  When preemption is enabled,
   these are the preemption priorities.  To define a generic class of
   BCMs for the purpose of our analysis in accordance with the above
   assumptions, let

   Nmax = link capacity, i.e., the maximum number of simultaneously
        established LSPs for all classes together,
   Nc = the number of simultaneously established class c LSPs, for c =
        1, 2, and 3, respectively.

   For MAM, let

   Bc = maximum number of simultaneously established class c LSPs.

   Then, Bc is the Bandwidth Constraint for class c, and we have

   Nc <= Bc <= Nmax, for c = 1, 2, and 3,
   N1 + N2 + N3 <= Nmax,
   B1 + B2 + B3 >= Nmax.

   For RDM, the BCs are specified as:

   B1 = maximum number of simultaneously established class 1 LSPs,
   B2 = maximum number of simultaneously established LSPs for classes 1
        and 2 together,
   B3 = maximum number of simultaneously established LSPs for classes
        1, 2, and 3 together.


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   Then, we have the following relationships:

   N1 <= B1,
   N1 + N2 <= B2,
   N1 + N2 + N3 <= B3,
   B1 < B2 < B3 = Nmax.


3. Performance Model

   Reference [8] presents a 3-class Markov-chain performance model to
   analyze a general class of BCMs.  The BCMs that can be analyzed
   include, besides MAM and RDM, also BCMs with privately reserved
   bandwidth that cannot be preempted by other classes.

   The Markov-chain performance model in [8] assumes Poisson arrivals
   for LSP requests with exponentially distributed lifetime.  The
   Poisson assumption for LSP requests is relevant since we are not
   dealing with the arrivals of individual packet within an LSP.  Also,
   LSP lifetime may exhibit heavy-tail characteristics.  This effect
   should be accounted for when the performance of a particular BCM by
   itself is evaluated.  As the effect would be common for all BCMs, we
   ignore it for simplicity in the comparative analysis of the relative
   performance of different BCMs.  In principle, a suitably chosen
   hyperexponential distribution may be used to capture some aspects of
   heavy tail.  However, this will significantly increase the
   complexity of the non-product-form preemption model in [8].

   The model in [8] assumes the use of admission control to allocate
   link bandwidth to LSPs of different classes in accordance with their
   respective BCs.  Thus, the model accepts as input the link capacity
   and offered load from different classes.  The blocking and
   preemption probabilities for different classes under different BCs
   are generated as output.  Thus, from a service provider's
   perspective, given the desired level of blocking and preemption
   performance, the model can be used iteratively to determine the
   corresponding set of BCs.

   To understand the implications of using criteria (2), (3), and (5)
   in the Introduction Section to select a BCM, we present some
   numerical results of the analysis in [8].  This is to gain some
   insight to facilitate the discussion of the issues that can arise.
   The major performance objective is to achieve a balance between the
   need for bandwidth sharing so as to gain bandwidth efficiency, and
   the need for bandwidth isolation so as to protect bandwidth access
   by different classes.

3.1 LSP Blocking and Preemption

   As described in Section 2, the three classes of traffic used as an
   example are class 1 (highest priority), class 2, and class 3 (lowest
   priority).  Preemption may or may not be used and we will examine
   the performance of each scenario.  When preemption is used, the

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   priorities are the preemption priorities.  We consider cross-class
   preemption only, with no within-class preemption.  In other words,
   preemption is enabled so that, when necessary, class 1 can preempt
   class 3 or class 2 (in that order), and class 2 can preempt class 3.

   Each class offers a load of traffic to the network that is expressed
   in terms of the arrival rate of its LSP requests and the average
   lifetime of an LSP.  A unit of such a load is an erlang.  (In
   packet-based networks, traffic volume is usually measured by
   counting the number of bytes and/or packets that are sent or
   received over an interface, during a measurement period.  Here we
   are only concerned with bandwidth allocation and usage at the LSP
   level.  Hence, the erlang as a measure of resource utilization in a
   link-speed independent manner is an appropriate unit for our purpose
   [9].)

   To prevent Diffserv QoS degradation at the packet level, the
   expected number of established LSPs for a given class should be kept
   in line with the average service rate that the Diffserv scheduler
   can provide to that class.  Because of the use of overbooking, the
   actual traffic carried by a link may be higher than expected, and
   hence QoS degradation may not be totally avoidable.

   However, the use of admission control at the LSP level helps to
   *minimize* QoS degradation by enforcing the BCs established for the
   different classes, according to the rules of the BCM adopted.  That
   is, the BCs are used to determine the number of LSPs that can be
   simultaneously established for different classes under various
   operational conditions.  By controlling the number of LSPs admitted
   from different classes, this in turn ensures that the amount of
   traffic submitted to the Diffserv scheduler is compatible with the
   targeted packet-level QoS objectives.

   The performance of a BCM can therefore be measured by how well the
   given BCM handles the offered traffic, under normal or overload
   conditions, while maintaining packet-level service objectives.
   Thus, assuming the enforcement of Diffserv QoS objectives by
   admission control as a given, the performance of a BCM can be
   expressed in terms of *LSP blocking and preemption probabilities*.

   Different BCMs have different strengths and weaknesses.  Depending
   on the BCs chosen for a given load, a BCM may perform well in one
   operating region and poorly in another region.  Service providers
   are mainly concerned with the utility of a BCM to meet their
   operational needs.  Regardless of which BCM is deployed, the
   foremost consideration is that the BCM works well under the
   engineered load, such as the ability to deliver service-level
   objectives for LSP blocking probabilities.  It is also expected that
   the BCM handles overload "reasonably" well.  Thus, for comparison,
   the common operating point we choose for each BCM is that they meet
   specified performance objectives in terms of blocking/preemption
   under given normal load.  We then observe how their performance


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   varies under overload.  More will be said about this aspect later in
   Section 4.2.

3.2 Example Link Traffic Model

   As an example, consider a link with a capacity that allows a maximum
   of 15 LSPs from different classes to be established simultaneously.
   All LSPs are assumed to have an average lifetime of 1 time unit.
   Suppose that this link is being offered a load of
   2.7 erlangs from class 1,
   3.5 erlangs from class 2, and
   3.5 erlangs from class 3.

   We now consider a scenario whereby the blocking/preemption
   performance objectives for the three classes are desired to be
   comparable under normal conditions (other scenarios are covered in
   later sections).  To meet this service requirement under the above
   given load, the BCs are selected as follows:

   For MAM:
   up to 6 simultaneous LSPs for class 1,
   up to 7 simultaneous LSPs for class 2, and
   up to 15 simultaneous LSPs for class 3.

   For RDM:
   up to 6 simultaneous LSPs for class 1 by itself,
   up to 11 simultaneous LSPs for classes 1 and 2 together, and
   up to 15 simultaneous LSPs for all three classes together.

   Note that the driver is service requirement, independent of BCM.
   The above BCs are not arbitrarily picked; they are chosen to meet
   specific performance objectives in terms of blocking/preemption
   (detailed in the next section).

   An intuitive "explanation" for the above set of BCs may be as
   follows.  Class 1 BC is the same (6) for both models, as class 1 is
   treated the same way under either model with preemption.  However,
   MAM and RDM operate in fundamentally different ways and give
   different treatments to classes with lower preemption priorities.
   In can be seen from Section 2 that while RDM imposes a strict
   ordering of the different BCs (B1 < B2 < B3) and a hard boundary (B3
   = Nmax), MAM uses a soft boundary (B1+B2+B3 >= Nmax) with no
   specific ordering.  As to be explained in Section 4.3, this allows
   RDM to have a higher degree of sharing among different classes.
   Such a higher degree of coupling means that the numerical values of
   the BCs can be relatively smaller when compared with those for MAM,
   to meet given performance requirements under normal load.

   Thus, in the above example, the RDM BCs of (6, 11, 15) may be
   thought of as roughly corresponding to the MAM BCs of (6, 6+7,
   6+7+15).  (The intent here is just to point out that the design
   parameters for the two BCMs need to be different as they operate
   differently - strictly speaking, the numerical correspondence is

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   incorrect.)  Of course, both BCMs are bounded by the same aggregate
   constraint of the link capacity (15).

   The BCs chosen in the above example are not intended to be regarded
   as typical values used by any service provider.  They are used here
   mainly for illustrative purposes.  The method we used for analysis
   can easily accommodate another set of parameter values as input.

3.3 Performance Under Normal Load

   In the example above, based on the BCs chosen, the blocking and
   preemption probabilities for LSP setup requests under normal
   conditions for the two BCMs are given in Table 1.  Remember that the
   BCs have been selected for this scenario to address the service
   requirement to offer comparable blocking/preemption objectives for
   the three classes.

   Table 1.  Blocking and preemption probabilities
    BCM     PB1     PB2     PB3     PP2     PP3   PB2+PP2 PB3+PP3
    MAM   0.03692 0.03961 0.02384    0    0.02275 0.03961 0.04659
    RDM   0.03692 0.02296 0.02402 0.01578 0.01611 0.03874 0.04013

   In the above table,

   PB1 = blocking probability of class 1
   PB2 = blocking probability of class 2
   PB3 = blocking probability of class 3

   PP2 = preemption probability of class 2
   PP3 = preemption probability of class 3

   PB2+PP2 = combined blocking/preemption probability of class 2
   PB3+PP3 = combined blocking/preemption probability of class 3

   First, we observe that, indeed, the values for (PB1, PB2+PP2,
   PB3+PP3) are very similar one to another.  This confirms that the
   service requirement (of comparable blocking/preemption objectives
   for the three classes) has been met for both BCMs.

   Then, we observe that the (PB1, PB2+PP2, PB3+PP3) values for MAM are
   very similar to the (PB1, PB2+PP2, PB3+PP3) values for RDM.  This
   indicates that, in this scenario, both BCMs offer very similar
   performance under normal load.

   From column 2 of the above table, it can be seen that class 1 sees
   exactly the same blocking under both BCMs.  This should be obvious
   since both allocate up to 6 simultaneous LSPs for use by class 1
   only.  Slightly better results are obtained from RDM, as shown by
   the last two columns in Table 1.  This comes about because the
   cascaded bandwidth separation in RDM effectively gives class 3 some
   form of protection from being preempted by higher-priority classes.



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   Also, note that PP2 is zero in this particular case, simply because
   the BCs for MAM happen to have been chosen in such a way that class
   1 never has to preempt class 2 for any of the bandwidth that class 1
   needs.  (This is because class 1 can, in the worst case, get all the
   bandwidth it needs simply by preempting class 3 alone.)  In general,
   this will not be the case.

   It is interesting to compare these results with those for the case
   of a single class.  Based on the Erlang loss formula, a capacity of
   15 servers can support an offered load of 10 erlangs with a blocking
   probability of 0.0364969.  Whereas the total load for the 3-class
   BCM is less with 2.7 + 3.5 + 3.5 = 9.7 erlangs, the probabilities of
   blocking/preemption are higher.  Thus, there is some loss of
   efficiency due to the link bandwidth being partitioned to
   accommodate for different traffic classes, thereby resulting in less
   sharing.  This aspect will be examined in more details later in the
   section on Complete Sharing.


4. Performance Under Overload

   Overload occurs when the traffic on a system is greater than the
   traffic capacity of the system.  To investigate the performance
   under overload conditions, the load of each class is varied
   separately.  Blocking and preemption probabilities for each case are
   not shown separately: they are added together to yield a combined
   blocking/preemption probability.

4.1 Bandwidth Sharing Versus Isolation

   Figures 1 and 2 show the relative performance when the load of each
   class in the example of Section 3.2 is varied separately.  The three
   series of data in each of these figures are, respectively,

   class 1 blocking probability ("Class 1 B"),
   class 2 blocking/preemption probability ("Class 2 B+P"), and
   class 3 blocking/preemption probability ("Class 3 B+P").

   For each of these series, the first set of four points is for the
   performance when class 1 load is increased from half of its normal
   load to twice its normal.  Similarly, the next and the last sets of
   four points are when class 2 and class 3 loads are correspondingly
   increased.

   The following observations apply to both BCMs:

   1. The performance of any class generally degrades as its load
     increases.
   2. The performance of class 1 is not affected by any changes
     (increases or decreases) in either class 2 or class 3 traffic,
     because class 1 can always preempt others.
   3. Similarly, the performance of class 2 is not affected by any
     changes in class 3 traffic.

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   4. Class 3 sees better (worse) than normal performance when either
     class 1 or class 2 traffic is below (above) normal.

   In contrast, the impact of the changes in class 1 traffic on class 2
   performance is different for the two BCMs: being negligible in MAM
   and significant in RDM.

   1. While class 2 sees little improvement (no improvement in this
     particular example) in performance when class 1 traffic is below
     normal when MAM is used, it sees better than normal performance
     under RDM.
   2. Class 2 sees no degradation in performance when class 1 traffic is
     above normal when MAM is used.  In this example, with BCs 6 + 7 <
     15, class 1 and class 2 traffic are effectively being served by
     separate pools.  Therefore, class 2 sees no preemption, and only
     class 3 is being preempted whenever necessary.  This fact is
     confirmed by the Erlang loss formula: a load of 2.7 erlangs
     offered to 6 servers sees a 0.03692 blocking, a load of 3.5
     erlangs offered to 7 servers sees a 0.03961 blocking.  These
     blocking probabilities are exactly the same as the corresponding
     entries in Table 1: PB1 and PB2 for MAM.
   3. This is not the case in RDM.  Here, the probability for class 2 to
     be preempted by class 1 is nonzero because of two effects.  (1)
     Through the cascaded bandwidth arrangement, class 3 is protected
     somewhat from preemption.  (2) Class 2 traffic is sharing a BC
     with class 1.  Consequently, class 2 suffers when class 1 traffic
     increases.

   Thus, it appears that while the cascaded bandwidth arrangement and
   the resulting bandwidth sharing makes RDM works better under normal
   conditions, such interaction makes it less effective to provide
   class isolation under overload conditions.

4.2 Improving Class 2 Performance at the Expense of Class 3

   We now consider a scenario in which the service requirement is to
   give better blocking/preemption performance to class 2 than to class
   3, while maintaining class 1 performance at the same level as in the
   previous scenario.  (The use of minimum deterministic guarantee for
   class 3 is to be considered in the next section.)  So that the
   specified class 2 performance objective can be met, class 2 BC is
   appropriately increased.  As an example, BCs (6, 9, 15) are now used
   for MAM, and (6, 13, 15) for RDM.  For both BCMs, as shown in
   Figures 1bis and 2bis, while class 1 performance remains unchanged,
   class 2 now receives better performance, at the expense of class 3.
   This is of course due to the increased access of bandwidth by class
   2 over class 3.  Under normal conditions, the performance of the two
   BCMs is similar in terms of their blocking and preemption
   probabilities for LSP setup requests, as shown in Table 2.

   Table 2.  Blocking and preemption probabilities
     BCM      PB1     PB2     PB3     PP2     PP3   PB2+PP2 PB3+PP3


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     MAM    0.03692 0.00658 0.02733    0    0.02709 0.00658 0.05441
     RDM    0.03692 0.00449 0.02759 0.00272 0.02436 0.00721 0.05195

   Under overload, the observations in Section 4.1 regarding the
   difference in the general behavior between the two BCMs still apply,
   as shown in Figures 1bis and 2bis.

   Some frequently asked questions about the operation of BCMs are as
   follows.  For a link capacity of 15, would a class 1 BC of 6 and a
   class 2 BC of 9 in MAM result in the possibility of a total lockout
   for class 3?  This will certainly be the case when there are 6 class
   1 and 9 class 2 LSPs being simultaneously established.  Such an
   offered load (with 6 class 1 and 9 class 2 LSP requests) will not
   cause a lockout of class 3 with RDM having a BC of 13 for classes 1
   and 2 combined, but will result in class 2 LSPs being rejected.  If
   class 2 traffic were considered relatively more important then class
   3 traffic, then RDM would perform very poorly when compared with MAM
   with BCs of (6, 9, 15).  Should MAM with BCs of (6, 7, 15) be used
   instead so as to make the performance of RDM look comparable?

   The answer is that the above scenario is not very realistic when the
   offered load is assumed to be (2.7, 3.5, 3.5) for the three classes,
   as stated in Section 3.2.  Treating an overload of (6, 9, x) as
   normal operating condition is incompatible with the engineering of
   BCs according to needed bandwidth from different classes.  It would
   be rare for a given class to need so much more than its engineered
   bandwidth level.  But if the class did, the expectation based on
   design and normal traffic fluctuations is that this class would
   quickly release unneeded bandwidth toward its engineered level,
   freeing up bandwidth for other classes.

   Service providers engineer their networks based on traffic
   projections to determine network configurations and needed capacity.
   All BCMs should be designed to operate under realistic network
   conditions.  For any BCM to work properly, the selection of values
   for different BCs must therefore be based on the projected bandwidth
   needs of each class, as well as the bandwidth allocation rules of
   the BCM itself.  This is to ensure that the BCM works as expected
   under the intended design conditions.  In operation, the actual load
   may well turn out to be different from the design.  Thus, an
   assessment of the performance of a BCM under overload is essential
   to see how well the BCM can cope with traffic surges or network
   failures.  Reflecting this view, the basis for comparison of two
   BCMs is that they meet the same or similar performance requirements
   under normal conditions, and how they withstand overload.

   In operational practice, load measurement and forecast would be
   useful to calibrate and fine-tune the BCs so that traffic from
   different classes could be redistributed accordingly.  Dynamic
   adjustment of the Diffserv scheduler could also be used to minimize
   QoS degradation.



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4.3 Comparing Bandwidth Constraints of Different Models

   As pointed out in Section 3.2, the higher degree of sharing among
   the different classes in RDM means that the numerical values of the
   BCs could be relatively smaller, when compared with those for MAM.
   We now examine this aspect in more details by considering the
   following scenario.  We set the BCs so that, (1) for both BCMs, the
   same value is used for class 1, (2) the same minimum *deterministic*
   guarantee of bandwidth for class 3 is offered by both BCMs, and (3)
   the blocking/preemption probability is minimized for class 2.  We
   want to emphasize that this may not be the way service providers
   select BCs.  It is done here to investigate the *statistical*
   behavior of such a deterministic mechanism.

   For illustration, we use BCs (6, 7, 15) for MAM, and (6, 13, 15) for
   RDM.  In this case, both BCMs have 13 units of bandwidth for classes
   1 and 2 together, and dedicate 2 units of bandwidth for use by class
   3 only.  The performance of the two BCMs under normal conditions is
   shown in Table 3.  It is clear that MAM with (6, 7, 15) gives fairly
   comparable performance objectives across the three classes, while
   RDM with (6, 13, 15) strongly favors class 2 at the expense of class
   3.  They therefore cater to different service requirements.

   Table 3.  Blocking and preemption probabilities
     BCM      PB1     PB2     PB3     PP2     PP3   PB2+PP2 PB3+PP3
     MAM    0.03692 0.03961 0.02384    0    0.02275 0.03961 0.04659
     RDM    0.03692 0.00449 0.02759 0.00272 0.02436 0.00721 0.05195

   By comparing Figures 1 and 2bis, it can be seen that, when being
   subjected to the same set of BCs, RDM gives class 2 much better
   performance than MAM, with class 3 being only slightly worse.

   This confirms the observation in Section 3.2 that, when the same
   service requirements under normal conditions are to be met, the
   numerical values of the BCs for RDM can be relatively smaller than
   those for MAM.  This should not be surprising in view of the hard
   boundary (B3 = Nmax) in RDM versus the soft boundary (B1+B2+B3 >=
   Nmax) in MAM.  The strict ordering of BCs (B1 < B2 < B3) gives RDM
   the advantage of a higher degree of sharing among the different
   classes, i.e., the ability to reallocate the unused bandwidth of
   higher-priority classes to lower-priority ones, if needed.
   Consequently, this leads to better performance when an identical set
   of BCs is used as exemplified above.  Such a higher degree of
   sharing may necessitate the use of minimum deterministic bandwidth
   guarantee to offer some protection for lower-priority traffic from
   preemption.  The explicit lack of ordering of BCs in MAM together
   with its soft boundary implies that the use of minimum deterministic
   guarantees for lower-priority classes may not need to be enforced
   when there is a lesser degree of sharing.  This is demonstrated by
   the example in Section 4.2 with BCs (6, 9, 15) for MAM.




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   For illustration, Table 4 shows the performance under normal
   conditions of RDM with BCs (6, 15, 15).

   Table 4.  Blocking and preemption probabilities
     BCM      PB1     PB2     PB3     PP2     PP3   PB2+PP2 PB3+PP3
     RDM    0.03692 0.00060 0.02800 0.00032 0.02740 0.00092 0.05540

   Regardless of whether deterministic guarantees are used or not, both
   BCMs are bounded by the same aggregate constraint of the link
   capacity.  Also, in both BCMs, bandwidth access guarantees are
   necessarily achieved statistically because of traffic fluctuations,
   as explained in Section 4.2.  (As a result, service-level objectives
   are typically specified as monthly averages, under the use of
   statistical guarantees, rather than deterministic guarantees.)
   Thus, given the fundamentally different operating principles of the
   two BCMs (ordering, hard versus soft boundary), the dimensions of
   one BCM should not be adopted to design for the other.  Rather, it
   is the service requirements, and perhaps also the operational needs,
   of a service provider that should be used to drive how the BCs of a
   BCM are selected.


5. Performance Under Partial Preemption

   In the previous two sections, preemption is *fully enabled* in the
   sense that class 1 can preempt class 3 or class 2 (in that order),
   and class 2 can preempt class 3.  That is, both classes 1 and 2 are
   preemptor-enabled, while classes 2 and 3 are preemptable.  A class
   that is preemptor-enabled can preempt lower-priority classes
   designated as preemptable.  A class not designated as preemptable
   cannot be preempted by any other classes, regardless of relative
   priorities.

   We now consider the three cases shown in Table 5 when preemption is
   only partially enabled.

   Table 5.  Partial preemption modes
      preemption modes      preemptor-enabled      preemptable
   "1+2 on 3" (Fig. 3, 6)   class 1, class 2         class 3
    "1 on 3" (Fig. 4, 7)         class 1             class 3
   "1 on 2+3" (Fig. 5, 8)        class 1         class 3, class 2

   In this section, we evaluate how these preemption modes affect the
   performance of a particular BCM.  Thus, we are comparing how a given
   BCM performs when preemption is fully enabled versus how the same
   BCM performs when preemption is partially enabled.  The performance
   of these preemption modes is shown in Figures 3 to 5 for RDM, and
   Figures 6 to 8 for MAM, respectively.  In all of these figures, the
   BCs of Section 3.2 are used for illustration, i.e., (6, 7, 15) for
   MAM and (6, 11, 15) for RDM.  However, the general behavior is
   similar when the BCs are changed to those in Sections 4.2 and 4.3,
   i.e., (6, 9, 15) and (6, 13, 15), respectively.

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5.1 Russian Dolls Model

   Let us first examine the performance under RDM.  There are two sets
   of results, depending on whether class 2 is preemptable or not: (1)
   Figures 3 and 4 for the two modes when only class 3 is preemptable,
   and (2) Figure 2 in the previous section and Figure 5 for the two
   modes when both classes 2 and 3 are preemptable.  By comparing these
   two sets of results, the following impacts can be observed.
   Specifically, when class 2 is non-preemptable, and when compared
   with the case of class 2 being preemptable, then the behavior of
   each class is:

   1. Class 1 generally sees a higher blocking probability when class 2
     is non-preemptable.  As the class 1 space allocated by the class 1
     BC is shared with class 2, which is now non-preemptable, class 1
     cannot reclaim any such space occupied by class 2 when needed.
     Also, class 1 has less opportunity to preempt - being able to
     preempt class 3 only.
   2. Class 3 also sees higher blocking/preemption when its own load is
     increased, as it is being preempted more frequently by class 1,
     when class 1 cannot preempt class 2.  (See the last set of four
     points in the series for class 3 shown in Figures 3 and 4, when
     comparing with Figures 2 and 5.)
   3. Class 2 blocking/preemption is reduced even when its own load is
     increased, since it is not being preempted by class 1.  (See the
     middle set of four points in the series for class 2 shown in
     Figures 3 and 4, when comparing with Figures 2 and 5.)

   Another two sets of results are related to whether class 2 is
   preemptor-enabled or not.  In this case, when class 2 is not
   preemptor-enabled, class 2 blocking/preemption is increased when
   class 3 load is increased (the last set of four points in the series
   for class 2 shown in Figures 4 and 5, when comparing with Figures 2
   and 3).  This is because both classes 2 and 3 are now competing
   independently with each other for resources.

5.2 Maximum Allocation Model

   Turning now to MAM, the significant impact appears to be only on
   class 2, when it cannot preempt class 3, thereby causing its
   blocking/preemption to increase in two situations.

   1. When class 1 load is increased (the first set of four points in
     the series for class 2 shown in Figures 7 and 8, when comparing
     with Figures 1 and 6).
   2. When class 3 load is increased (the last set of four points in the
     series for class 2 shown in Figures 7 and 8, when comparing with
     Figures 1 and 6).  This is similar to RDM, i.e., class 2 and class
     3 are now competing with each other.

   When comparing Figure 1 (for the case of fully enabled preemption)
   with Figures 6 to 8 (for partially enabled preemption), it can be

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   seen that the performance of MAM is relatively insensitive to the
   different preemption modes.  This is because when each class has its
   own bandwidth access limits, the degree of interference among the
   different classes is reduced.

   This is in contrast with RDM, whose behavior is more dependent on
   the preemption mode in use.


6. Performance Under Pure Blocking

   This section covers the case when preemption is completely disabled.
   We continue with the numerical example used in the previous sections
   with the same link capacity and offered load.

6.1 Russian Dolls Model

   For RDM, we consider two different settings:

   "Russian Dolls (1)" BCs:
   up to 6 simultaneous LSPs for class 1 by itself,
   up to 11 simultaneous LSPs for classes 1 and 2 together, and
   up to 15 simultaneous LSPs for all three classes together.

   "Russian Dolls (2)" BCs:
   up to 9 simultaneous LSPs for class 3 by itself,
   up to 14 simultaneous LSPs for classes 3 and 2 together, and
   up to 15 simultaneous LSPs for all three classes together.

   Note that the "Russian Dolls (1)" set of BCs is the same as
   previously with preemption enabled, while the "Russian Dolls (2)"
   has the cascade of bandwidth arranged in *reverse* order of the
   classes.

   As observed in Section 4, the cascaded bandwidth arrangement is
   intended to offer lower priority traffic some protection from
   preemption by higher priority traffic.  This is to avoid starvation.
   In a pure blocking environment, such protection is no longer
   necessary.  As depicted in Figure 9, it actually produces the
   opposite, undesirable, effect: higher priority traffic sees higher
   blocking than lower priority traffic.  With no preemption, higher
   priority traffic should be protected instead to ensure that they
   could get through when under high load.  Indeed, when the reverse
   cascade is used in "Russian Dolls (2)," the required performance of
   lower blocking for higher priority traffic is achieved as shown in
   Figure 10.  In this specific example, there is very little
   difference among the performance of the three classes in the first
   eight data points for each of the three series.  However, the BCs
   can be tuned to get a bigger differentiation.

6.2 Maximum Allocation Model

   For MAM, we also consider two different settings:

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   "Exp. Max. Alloc. (1)" BCs:
   up to 7 simultaneous LSPs for class 1,
   up to 8 simultaneous LSPs for class 2, and
   up to 8 simultaneous LSPs for class 3.

   "Exp. Max. Alloc. (2)" BCs:
   up to 7 simultaneous LSPs for class 1, with additional bandwidth for
     1 LSP privately reserved
   up to 8 simultaneous LSPs for class 2, and
   up to 8 simultaneous LSPs for class 3.

   These BCs are chosen so that, under normal conditions, the blocking
   performance is similar to all the previous scenarios.  The only
   difference between these two sets of values is that the "Exp. Max.
   Alloc. (2)" algorithm gives class 1 a private pool of 1 server for
   class protection.  As a result, class 1 has a relatively lower
   blocking especially when its traffic is above normal, as can be seen
   by comparing Figures 11 and 12.  This is of course at the expense of
   a slight increase in the blocking of classes 2 and 3 traffic.

   When comparing the "Russian Dolls (2)" in Figure 10 with MAM in
   Figures 11 or 12, the difference between their behavior and the
   associated explanation are again similar to the case when preemption
   is used.  The higher degree of sharing in the cascaded bandwidth
   arrangement of RDM leads to a tighter coupling between the different
   classes of traffic when under overload.  Their performance therefore
   tends to degrade together when the load of any one class is
   increased.  By imposing explicit maximum bandwidth usage on each
   class individually, better class isolation is achieved.  The trade-
   off is that, generally, blocking performance in MAM is somewhat
   higher than RDM, because of reduced sharing.

   The difference in the behavior of RDM with or without preemption has
   already been discussed at the beginning of this section.  For MAM,
   some notable difference can also be observed from a comparison of
   Figures 1 and 11.  If preemption is used, higher-priority traffic
   tends to be able to maintain their performance despite the
   overloading of other classes.  This is not so if preemption is not
   allowed.  The trade-off is that, generally, the overloaded class
   sees a relatively higher blocking/preemption when preemption is
   enabled, than the case when preemption is disabled.


7. Performance Under Complete Sharing

   As observed towards the end of Section 3, the partitioning of
   bandwidth capacity for access by different traffic classes tends to
   reduce the maximum link efficiency achievable.  We now consider the
   case where there is no such partitioning, thereby resulting in full
   sharing of the total bandwidth among all the classes.  This is
   referred to as the Complete Sharing Model.


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   For MAM, this means that the BCs are such that up to 15 simultaneous
   LSPs are allowed for any class.

   Similarly, for RDM, the BCs are
   up to 15 simultaneous LSPs for class 1 by itself,
   up to 15 simultaneous LSPs for classes 1 and 2 together, and
   up to 15 simultaneous LSPs for all three classes together.

   Effectively, there is now no distinction between MAM and RDM.
   Figure 13 shows the performance when all classes have equal access
   to link bandwidth under Complete Sharing.

   With preemption being fully enabled, it can be seen that class 1
   virtually sees no blocking, regardless of the loading conditions of
   the link.  Since class 2 can only preempt class 3, class 2 sees some
   blocking and/or preemption when either class 1 load or its own load
   is above normal; otherwise, class 2 is unaffected by increases of
   class 3 load.  As higher priority classes always preempt class 3
   when the link is full, class 3 suffers the most with high
   blocking/preemption when there is any load increase from any class.
   A comparison of Figures 1, 2, and 13 shows that, while the
   performance of both classes 1 and 2 is far superior under Complete
   Sharing, class 3 performance is much better off under either MAM or
   RDM.  In a sense, class 3 is starved under overload as no protection
   of its traffic is being provided under Complete Sharing.


8. Implications on Performance Criteria

   Based on the previous results, a general theme is shown to be the
   trade-off between bandwidth sharing and class protection/isolation.
   To show this more concretely, let us compare the different BCMs in
   terms of the *overall loss probability*.  This quantity is defined
   as the long-term proportion of LSP requests from all classes
   combined that are lost as a result of either blocking or preemption,
   for a given level of offered load.

   As noted from the previous sections, while RDM has a higher degree
   of sharing then MAM, both converge ultimately to the Complete
   Sharing Model as the degree of sharing in each of them is increased.
   Figure 14 shows that, for a single link, the overall loss
   probability is the smallest under Complete Sharing and the largest
   under MAM, with RDM being intermediate.  Expressed differently,
   Complete Sharing yields the highest link efficiency and MAM the
   lowest.  As a matter of fact, the overall loss probability of
   Complete Sharing is identical to loss probability of a single class
   as computed by the Erlang loss formula.  Yet Complete Sharing has
   the poorest class protection capability.  (We want to point out
   that, in a network with many links and multiple-link routing paths,
   analysis in [6] showed that Complete Sharing does not necessarily
   lead to maximum network-wide bandwidth efficiency.)



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   Increasing the degree of bandwidth sharing among the different
   traffic classes helps to increase link efficiency.  Such increase,
   however, will lead to a tighter coupling between different classes.
   Under normal loading conditions, proper dimensioning of the link so
   that there is adequate capacity for each class can minimize the
   effect of such coupling.  Under overload conditions, when there is a
   scarcity of capacity, such coupling will be unavoidable and can
   cause severe degradation of service to the lower-priority classes.
   Thus, the objective of maximizing link usage as stated in criterion
   (5) of Section 1 must be exercised with care, with due consideration
   to the effect of interactions among the different classes.
   Otherwise, use of this criterion alone will lead to the selection of
   the Complete Sharing Model, as shown in Figure 14.

   The intention of criterion (2) in judging the effectiveness of
   different BCMs is to evaluate how they help the network to achieve
   the expected performance.  This can be expressed in terms of the
   blocking and/or preemption behavior as seen by different classes
   under various loading conditions.  For example, the relative
   strength of a BCM can be demonstrated by examining how many times
   the per-class blocking or preemption probability under overload is
   worse off than the corresponding probability under normal load.


9. Conclusions

   BCMs are used in DS-TE for path computation and admission control of
   LSPs by enforcing different BCs for different classes of traffic so
   that Diffserv QoS performance can be maximized.  Therefore, it is of
   interest to measure the performance of a BCM by the LSP
   blocking/preemption probabilities under various operational
   conditions.  Based on this, the performance of RDM and MAM for LSP
   establishment has been analyzed and compared.  In particular, three
   different scenarios have been examined: (1) all three classes have
   comparable performance objectives in terms of LSP
   blocking/preemption under normal conditions, (2) class 2 is given
   better performance at the expense of class 3, and (3) class 3
   receives some minimum deterministic guarantee.

   A general theme is shown to be the trade-off between bandwidth
   sharing to achieve greater efficiency under normal conditions, and
   robust class protection/isolation under overload.  The general
   properties of the two BCMs are:

   RDM
   . allows greater sharing of bandwidth among different classes
   . performs somewhat better under normal conditions
   . works well when preemption is fully enabled; under partial
     preemption, not all preemption modes work equally well

   MAM
   . does not depend on the use of preemption


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   . is relatively insensitive to the different preemption modes when
     preemption is used
   . provides more robust class isolation under overload

   Generally, the use of preemption gives higher-priority traffic some
   degree of immunity against the overloading of other classes.  This
   results in a higher blocking/preemption for the overloaded class,
   when compared with a pure blocking environment.


10. Security Considerations

   This document does not introduce additional security threats beyond
   those described for Diffserv [10] and MPLS Traffic Engineering [11,
   12, 13, 14] and the same security measures and procedures described
   in these documents apply here.  For example, the approach for
   defense against theft- and denial-of-service attacks discussed in
   [10], which consists of the combination of traffic conditioning at
   Diffserv boundary nodes along with security and integrity of the
   network infrastructure within a Diffserv domain, may be followed
   when DS-TE is in use.

   Also, as stated in [11], it is specifically important that
   manipulation of administratively configurable parameters (such as
   those related to DS-TE LSPs) be executed in a secure manner by
   authorized entities.  For example, as preemption is an
   administratively configurable parameter, it is critical that its
   values be set properly throughout the network.  Any misconfiguration
   in any label switch may cause new LSP set-up requests to be either
   blocked or to unnecessarily preempt LSPs already established.
   Similarly, the preemption values of LSP set-up requests must be
   configured properly; otherwise it may affect the operation of
   existing LSPs.


11. IANA Considerations

   This document has no actions for IANA.


12. References

12.1 Normative References

   1  F. Le Faucheur and W.S. Lai, "Requirements for Support of
      Differentiated Services-aware MPLS Traffic Engineering," RFC
      3564, July 2003.

12.2 Informative References





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   2  F. Le Faucheur (Editor), "Protocol extensions for support of
      Diff-Serv-aware MPLS Traffic Engineering," Internet-Draft, Work
      in Progress.
   3  J. Boyle, V. Gill, A. Hannan, D. Cooper, D. Awduche, B.
      Christian, and W.S. Lai, "Applicability Statement for Traffic
      Engineering with MPLS," RFC 3346, July 2002.
   4  F. Le Faucheur and W.S. Lai, "Maximum Allocation Bandwidth
      Constraints Model for Diff-Serv-aware MPLS Traffic Engineering,"
      Internet-Draft, Work in Progress.
   5  F. Le Faucheur (Editor), "Russian Dolls Bandwidth Constraints
      Model for Diff-Serv-aware MPLS Traffic Engineering," Internet-
      Draft, Work in Progress.
   6  J. Ash, "Max Allocation with Reservation Bandwidth Constraint
      Model for MPLS/DiffServ TE & Performance Comparisons," Internet-
      Draft, Work in Progress.
   7  F. Le Faucheur, "Considerations on Bandwidth Constraints Models
      for DS-TE," Internet-Draft, Work in Progress.
   8  W.S. Lai, "Traffic Engineering for MPLS," Internet Performance
      and Control of Network Systems III Conference, SPIE Proceedings
      Vol. 4865, Boston, Massachusetts, USA, 30-31 July 2002, pp. 256-
      267.
   9  W.S. Lai, "Traffic Measurement for Dimensioning and Control of IP
      Networks," Internet Performance and Control of Network Systems II
      Conference, SPIE Proceedings Vol. 4523, Denver, Colorado, USA,
      21-22 August 2001, pp. 359-367.
   10 Blake, et al., "An Architecture for Differentiated Services," RFC
      2475.
   11 Awduche, et al., "Requirements for Traffic Engineering Over
      MPLS," RFC 2702.
   12 Awduche, et al, "RSVP-TE: Extensions to RSVP for LSP Tunnels,"
      RFC 3209.
   13 Katz, et al., "Traffic Engineering (TE) Extensions to OSPF
      Version 2," RFC 3630.
   14 Smit, Li, "Intermediate System to Intermediate System (IS-IS)
      extensions for Traffic Engineering (TE)," RFC 3784.


13. Acknowledgments

   Inputs from Jerry Ash, Jim Boyle, Anna Charny, Sanjaya Choudhury,
   Dimitry Haskin, Francois Le Faucheur, Vishal Sharma, and Jing Shen
   are much appreciated.


14. Author's Address

   Wai Sum Lai
   AT&T Labs
   Room D5-3D18
   200 Laurel Avenue
   Middletown, NJ 07748, USA


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   Phone: +1 732-420-3712
   Email: wlai@att.com


15. Intellectual Property Considerations

   The IETF takes no position regarding the validity or scope of any
   Intellectual Property Rights or other rights that might be claimed
   to pertain to the implementation or use of the technology described
   in this document or the extent to which any license under such
   rights might or might not be available; nor does it represent that
   it has made any independent effort to identify any such rights.
   Information on the procedures with respect to rights in RFC
   documents can be found in BCP 78 and BCP 79.

   Copies of IPR disclosures made to the IETF Secretariat and any
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   attempt made to obtain a general license or permission for the use
   of such proprietary rights by implementers or users of this
   specification can be obtained from the IETF on-line IPR repository
   at http://www.ietf.org/ipr.

   The IETF invites any interested party to bring to its attention any
   copyrights, patents or patent applications, or other proprietary
   rights that may cover technology that may be required to implement
   this standard. Please address the information to the IETF at
   ietf-ipr@ietf.org.


Copyright Notice and Disclaimer

   Copyright (C) The Internet Society (2004).  This document is subject
   to the rights, licenses and restrictions contained in BCP 78, and
   except as set forth therein, the authors retain all their rights.

   This document and the information contained herein are provided on
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   WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.











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