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Network Working Group W. Lai
Request for Comments: 4128 AT&T Labs
Category: Informational June 2005
Bandwidth Constraints Models for
Differentiated Services (Diffserv)-aware MPLS Traffic Engineering:
Performance Evaluation
Status of This Memo
This memo provides information for the Internet community. It does
not specify an Internet standard of any kind. Distribution of this
memo is unlimited.
Copyright Notice
Copyright (C) The Internet Society (2005).
IESG Note
The content of this RFC has been considered by the IETF (specifically
in the TE-WG working group, which has no problem with publication as
an Informational RFC), and therefore it may resemble a current IETF
work in progress or a published IETF work. However, this document is
an individual submission and not a candidate for any level of
Internet Standard. The IETF disclaims any knowledge of the fitness
of this RFC for any purpose, and in particular notes that it has not
had complete IETF review for such things as security, congestion
control or inappropriate interaction with deployed protocols. The
RFC Editor has chosen to publish this document at its discretion.
Readers of this RFC should exercise caution in evaluating its value
for implementation and deployment. See RFC 3932 for more
information.
Abstract
"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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Table of Contents
1. Introduction ....................................................3
1.1. Conventions used in this document ..........................4
2. Bandwidth Constraints Models ....................................4
3. Performance Model ...............................................5
3.1. LSP Blocking and Preemption ................................6
3.2. Example Link Traffic Model .................................8
3.3. Performance under Normal Load ..............................9
4. Performance under Overload .....................................10
4.1. Bandwidth Sharing versus Isolation ........................10
4.2. Improving Class 2 Performance at the Expense of Class 3 ...12
4.3. Comparing Bandwidth Constraints of Different Models .......13
5. Performance under Partial Preemption ...........................15
5.1. Russian Dolls Model .......................................16
5.2. Maximum Allocation Model ..................................16
6. Performance under Pure Blocking ................................17
6.1. Russian Dolls Model .......................................17
6.2. Maximum Allocation Model ..................................18
7. Performance under Complete Sharing .............................19
8. Implications on Performance Criteria ...........................20
9. Conclusions ....................................................21
10. Security Considerations .......................................22
11. Acknowledgements ..............................................22
12. References ....................................................22
12.1. Normative References ....................................22
12.2. Informative References ..................................22
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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] and [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] 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
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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], and [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 allows this.
When considering a complete quality of service environment, with
Diffserv routers and DS-TE, it is important to consider these
differences carefully.
1.1. 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.
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 that (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 get 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.
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Suppose that the three classes of traffic assumed above for the
purposes 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
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, BCMs with privately reserved bandwidth
that cannot be preempted by other classes.
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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 packets 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 intended to facilitate
discussion of the issues that can arise. The major performance
objective is to achieve a balance between the need for bandwidth
sharing (for increasing bandwidth efficiency) and the need for
bandwidth isolation (for protecting 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
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
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with bandwidth allocation and usage at the LSP level. Therefore, as
a measure of resource utilization in a link-speed independent manner,
the erlang 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 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 that the enforcement of Diffserv QoS objectives by admission
control is 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. 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 BCMs is that they meet specified performance objectives
in terms of blocking/preemption under given normal load. We then
observe how their performance varies under overload. More will be
said about this aspect later in Section 4.2.
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3.2. Example Link Traffic Model
For 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 wherein 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 picked arbitrarily; 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. It
can be seen from Section 2 that although 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 will 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 than those for MAM, to meet given
performance requirements under normal load.
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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 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, the following apply:
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.
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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 Table 1, 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.
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 detail later, in Section 7 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 are not shown separately for
each case; 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,
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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 increased
correspondingly.
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.
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: It is negligible in MAM
and significant in RDM.
1. Although 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 is 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, and 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
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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 although the cascaded bandwidth arrangement and
the resulting bandwidth sharing makes RDM work 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
increased appropriately. 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, although 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
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.
The following are two frequently asked questions about the operation
of BCMs.
(1) 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 established simultaneously. 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
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will result in class 2 LSPs being rejected. If class 2 traffic were
considered relatively more important than class 3 traffic, then RDM
would perform very poorly compared to MAM with BCs of (6, 9, 15).
(2) 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 a
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 on 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 that of 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.
4.3. Comparing Bandwidth Constraints of Different Models
As is 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 than those for MAM. We now examine
this aspect in more detail 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
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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, whereas
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 and its soft boundary
imply 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.
For illustration, Table 4 shows the performance under normal
conditions of RDM with BCs (6, 15, 15).
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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, 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, whereas 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, in which 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 in
Figures 6 through 8 for MAM, respectively. In all of these figures,
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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.
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: (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, the behavior of each class is as follows:
1. Class 1 generally sees a higher blocking probability. 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, as it is 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. In this case, when class 2 is not preemptor-
enabled, class 2 blocking/preemption is increased when class 3 load
is increased. (See 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.
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1. When class 1 load is increased. (See 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. (See 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 Figure 1 (for the case of fully enabled preemption) is compared
to Figures 6 through 8 (for partially enabled preemption), it can be
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 in which 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, whereas the "Russian Dolls (2)"
has the cascade of bandwidth arranged in reverse order of the
classes.
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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 it 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:
"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 comes, of course, with 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
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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 in 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 differences 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 its 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 there
would be if 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.
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, class 1 sees virtually 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, although the performance of both classes 1 and 2 is
far superior under Complete Sharing, class 3 performance is much
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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 in the previous sections, although RDM has a higher degree
of sharing than MAM, both ultimately converge 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 that
under 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 the loss probability of a single class as computed by
the Erlang loss formula. Yet Complete Sharing has the poorest class
protection capability. (Note 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.)
Increasing the degree of bandwidth sharing among the different
traffic classes helps 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 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
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be demonstrated by examining how many times the per-class blocking or
preemption probability under overload is worse 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 the trade-off between bandwidth sharing to achieve
greater efficiency under normal conditions, and to achieve robust
class protection/isolation under overload. The general properties of
the two BCMs are as follows:
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
- 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 to the overloading of other classes. This results
in a higher blocking/preemption for the overloaded class than that in
a pure blocking environment.
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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 those 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 setup requests either to be
blocked or to unnecessarily preempt LSPs already established.
Similarly, the preemption values of LSP setup requests must be
configured properly; otherwise, they may affect the operation of
existing LSPs.
11. Acknowledgements
Inputs from Jerry Ash, Jim Boyle, Anna Charny, Sanjaya Choudhury,
Dimitry Haskin, Francois Le Faucheur, Vishal Sharma, and Jing Shen
are much appreciated.
12. References
12.1. Normative References
[1] Le Faucheur, F. and W. Lai, "Requirements for Support of
Differentiated Services-aware MPLS Traffic Engineering", RFC
3564, July 2003.
12.2. Informative References
[2] Le Faucheur, F., Ed., "Protocol Extensions for Support of
Diffserv-aware MPLS Traffic Engineering", RFC 4124, June 2005.
[3] Boyle, J., Gill, V., Hannan, A., Cooper, D., Awduche, D.,
Christian, B., and W. Lai, "Applicability Statement for Traffic
Engineering with MPLS", RFC 3346, August 2002.
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[4] Le Faucheur, F. and W. Lai, "Maximum Allocation Bandwidth
Constraints Model for Diffserv-aware MPLS Traffic Engineering",
RFC 4125, June 2005.
[5] Le Faucheur, F., Ed., "Russian Dolls Bandwidth Constraints Model
for Diffserv-aware MPLS Traffic Engineering", RFC 4127, June
2005.
[6] Ash, J., "Max Allocation with Reservation Bandwidth Constraint
Model for MPLS/DiffServ TE & Performance Comparisons", RFC 4126,
June 2005.
[7] F. Le Faucheur, "Considerations on Bandwidth Constraints Models
for DS-TE", 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, S., Black, D., Carlson, M., Davies, E., Wang, Z., and W.
Weiss, "An Architecture for Differentiated Service", RFC 2475,
December 1998.
[11] Awduche, D., Malcolm, J., Agogbua, J., O'Dell, M., and J.
McManus, "Requirements for Traffic Engineering Over MPLS", RFC
2702, September 1999.
[12] Awduche, D., Berger, L., Gan, D., Li, T., Srinivasan, V., and G.
Swallow, "RSVP-TE: Extensions to RSVP for LSP Tunnels", RFC
3209, December 2001.
[13] Katz, D., Kompella, K., and D. Yeung, "Traffic Engineering (TE)
Extensions to OSPF Version 2", RFC 3630, September 2003.
[14] Smit, H. and T. Li, "Intermediate System to Intermediate System
(IS-IS) Extensions for Traffic Engineering (TE)", RFC 3784, June
2004.
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Author's Address
Wai Sum Lai
AT&T Labs
Room D5-3D18
200 Laurel Avenue
Middletown, NJ 07748
USA
Phone: +1 732-420-3712
EMail: wlai@att.com
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Full Copyright Statement
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