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Traffic Engineering Working Group Wai Sum Lai
Internet Draft AT&T Labs
Document: <draft-wlai-tewg-bcmodel-03.txt> September 2003
Category: Informational
Bandwidth Constraints Models for
Differentiated Services-aware MPLS Traffic Engineering:
Performance Evaluation
Status of this Memo
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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 describing in more
details some of the selection criteria and their implications.
Results of a performance evaluation of the two models are also
included.
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
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Status of this Memo................................................1
Abstract...........................................................1
1. Introduction....................................................2
2. Bandwidth Constraints Models....................................3
3. Performance Model...............................................4
3.1 LSP Blocking and Preemption....................................5
3.2 Example Link Traffic Model.....................................6
3.3 Performance Under Normal Load..................................7
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...........11
5. Performance Under Partial Preemption...........................13
5.1 Russian Dolls.................................................14
5.2 Maximum Allocation............................................14
6. Performance Under Pure Blocking................................15
6.1 Russian Dolls.................................................15
6.2 Maximum Allocation............................................16
7. Performance Under Complete Sharing.............................17
8. Implications on Selection Criteria.............................17
9. Conclusions....................................................18
10. Security Considerations.......................................19
11. References....................................................19
12. Acknowledgments...............................................20
13. Author's Address..............................................20
Full Copyright Statement..........................................20
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 on different classes. Reference [1] specifies
the requirements and selection criteria for bandwidth constraints
models for the purpose of allocating bandwidth to individual
classes.
Two bandwidth constraints models are 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 selection criteria are also listed in [1]:
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(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 bandwidth constraints model 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]. Therefore, the criteria used
to select a model must enable us to evaluate how a particular model
delivers its performance, relative to other models.
This document complements [1] by describing in more details the
performance-oriented selection criteria and their implications in a
network implementation. Thus, our focus is only on criteria (2),
(3), and (5); we will not address criteria (1), (4), and (6). Also
included are the results of a performance evaluation of the above
two models under various operational conditions: normal load,
overload, preemption fully or partially enabled, pure blocking, or
complete sharing.
Related documents in this area include [4, 5, 6, 7, 8].
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 are denoted as 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 bandwidth constraints models for the
purpose of our analysis in accordance with the above assumptions,
let
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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 the maximum allocation model, 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 the Russian Dolls model, the bandwidth constraints 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
In [8], a 3-class Markov-chain performance model is presented to
analyze a general class of bandwidth constraints models. The models
that can be analyzed include, besides the maximum allocation and the
Russian Dolls, also models 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
bandwidth constraints model by itself is evaluated. As the effect
would be common for all bandwidth constraints models, we ignore it
for simplicity in the comparative analysis of the relative
performance of different models. 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].
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The model in [8] assumes the use of admission control to allocate
link bandwidth to LSPs of different classes in accordance with their
respective bandwidth constraints. 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 bandwidth constraints 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 bandwidth
constraints.
To understand the implications of using criteria (2), (3), and (5)
in the Introduction Section to select a bandwidth constraints model,
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 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 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 bandwidth constraints
established for the different classes, according to the rules of the
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bandwidth constraints model adopted. That is, the bandwidth
constraints 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 bandwidth constraints model can therefore be
measured by how well the given model 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
bandwidth constraints model can be expressed in terms of *LSP
blocking and preemption probabilities*.
Different models have different strengths and weaknesses. Depending
on the bandwidth constraint values chosen for a given load, a model
may perform well in one operating region and poorly in another
region. Service providers are mainly concerned with the utility of
a model to meet their operational needs. Regardless of which model
is deployed, the foremost consideration is that the model 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 model handles overload "reasonably" well. Thus,
for comparison, the common operating point we choose for each model
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.
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 bandwidth constraints are selected as follows:
For the explicit maximum allocation model:
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 the Russian Dolls model:
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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
bandwidth constraints model. The above bandwidth constraints 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 bandwidth
constraint values may be as follows. Class 1 bandwidth constraint
is the same (6) for both models, as class 1 is treated the same way
under either model with preemption. However, the maximum allocation
and the Russian Dolls models operate in fundamentally different ways
and give different treatments to classes with lower preemption
priorities. In can be seen from Section 2 that while the Russian
Dolls model imposes a strict ordering of the different bandwidth
constraint values (B1 < B2 < B3) and a hard boundary (B3 = Nmax),
the maximum allocation model uses a soft boundary (B1+B2+B3 >= Nmax)
with no specific ordering. As to be explained in Section 4.3, this
allows the Russian Dolls model to have a higher degree of sharing
among different classes. Such a higher degree of coupling means
that the numerical values of the bandwidth constraints can be
relatively smaller when compared with those for the maximum
allocation model, to meet given performance requirements under
normal load. Thus, in the above example, the bandwidth constraints
of (6, 11, 15) in the Russian Dolls model may be thought of as
roughly corresponding to the bandwidth constraints of (6, 6+7,
6+7+15) for the maximum allocation model. (The intent here is just
to point out that the design parameters for the two models need to
be different as they operate differently - strictly speaking, the
numerical correspondence is incorrect.) Of course, both models are
bounded by the same aggregate constraint of the link capacity (15).
The values 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 bandwidth constraint values
chosen, the blocking and preemption probabilities for LSP setup
requests under normal conditions for the two models are given in
Table 1. Remember that the bandwidth constraint values 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
Model PB1 PB2 PB3 PP2 PP3 PB2+PP2 PB3+PP3
MaxAll 0.03692 0.03961 0.02384 0 0.02275 0.03961 0.04659
RussDoll 0.03692 0.02296 0.02402 0.01578 0.01611 0.03874 0.04013
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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 models.
Then, we observe that the (PB1, PB2+PP2, PB3+PP3) values for the
maximum allocation model are very similar to the (PB1, PB2+PP2,
PB3+PP3) values for the Russian Dolls model. This indicates that,
in this scenario, both models 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 models. This should be obvious
since both allocate up to 6 simultaneous LSPs for use by class 1
only. Slightly better results are obtained from the Russian Dolls
model, as shown by the last two columns in Table 1. This comes
about because the cascaded bandwidth separation in the Russian Dolls
design 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 bandwidth constraints for the maximum allocation model 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 that 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
model 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.
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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 models:
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 models: being negligible in the
maximum allocation and significant in the Russian Dolls.
1. While class 2 sees little improvement (no improvement in this
particular example) in performance when class 1 traffic is below
normal when the explicit maximum allocation algorithm is used, it
sees better than normal performance under the Russian Dolls
algorithm.
2. Class 2 sees no degradation in performance when class 1 traffic is
above normal when the explicit maximum allocation algorithm is
used. In this example, with bandwidth constraints 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
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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 MaxAll.
3. This is not the case in the Russian Dolls algorithm. 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 bandwidth constraint 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 the Russian Dolls algorithm
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, the bandwidth
constraint for class 2 is appropriately increased. As an example,
bandwidth constraints (6, 9, 15) are now used for the maximum
allocation, and (6, 13, 15) for the Russian Dolls. For both models,
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 models 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
Model PB1 PB2 PB3 PP2 PP3 PB2+PP2 PB3+PP3
MaxAll 0.03692 0.00658 0.02733 0 0.02709 0.00658 0.05441
RussDoll 0.03692 0.00449 0.02759 0.00272 0.02436 0.00721 0.05195 6 0.00721 0.05195
Under overload, the observations in Section 4.1 regarding the
difference in the general behavior between the two models still
apply, as shown in Figures 1bis and 2bis.
Some frequently asked questions about the operation of bandwidth
constraints models are as follows. For a link capacity of 15, would
a bandwidth constraint of 6 for class 1 and a bandwidth constraint
of 9 for class 2 in the maximum allocation model 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
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and 9 class 2 LSP requests) will not cause a lockout of class 3 with
the Russian Dolls model having a bandwidth constraint 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 the Russian Dolls would perform
very poorly when compared with the maximum allocation model with
bandwidth constraints of (6, 9, 15). Should the maximum allocation
model with bandwidth constraints of (6, 7, 15) be used instead so as
to make the performance of the Russian Dolls 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
bandwidth constraints 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 bandwidth constraints models should be designed to operate under
realistic network conditions. For any bandwidth constraints model
to work properly, the selection of values for different bandwidth
constraints must therefore be based on the projected bandwidth needs
of each class, as well as the bandwidth allocation rules of the
model itself. This is to ensure that the model 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 bandwidth constraints model under
overload is essential to see how well the model can cope with
traffic surges or network failures. Reflecting this view, the basis
for comparison of two bandwidth constraints model 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 bandwidth constraints 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 pointed out in Section 3.2, the higher degree of sharing among
the different classes in the Russian Dolls model means that the
numerical values of the bandwidth constraints could be relatively
smaller, when compared with those for the maximum allocation model.
We now examine this aspect in more details by considering the
following scenario. We set the bandwidth constraints so that, (1)
for both models, the same value is used for class 1, (2) the same
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minimum *deterministic* guarantee of bandwidth for class 3 is
offered by both models, 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 bandwidth constraint values. It is
done here to investigate the *statistical* behavior of such a
deterministic mechanism.
For illustration, we use bandwidth constraints (6, 7, 15) for the
maximum allocation, and (6, 13, 15) for the Russian Dolls. In this
case, both models 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 models under normal conditions is shown
in Table 3. It is clear that the maximum allocation model with (6,
7, 15) gives fairly comparable performance objectives across the
three classes, while the Russian Dolls model 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
Model PB1 PB2 PB3 PP2 PP3 PB2+PP2 PB3+PP3
MaxAll 0.03692 0.03961 0.02384 0 0.02275 0.03961 0.04659
RussDoll 0.03692 0.00449 0.02759 0.00272 0.02436 0.00721 0.05195 6 0.00721 0.05195
By comparing Figures 1 and 2bis, it can be seen that, when being
subjected to the same set of bandwidth constraints, the Russian
Dolls model gives class 2 much better performance than the maximum
allocation model, 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 bandwidth constraints for the Russian Dolls
can be relatively smaller than those for the maximum allocation
model. This should not be surprising in view of the hard boundary
(B3 = Nmax) in the Russian Dolls versus the soft boundary (B1+B2+B3
>= Nmax) in maximum allocation. The strict ordering of bandwidth
constraints (B1 < B2 < B3) gives the Russian Dolls model 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 bandwidth
constraints 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 bandwidth constraints
in the maximum allocation model 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 bandwidth constraints (6, 9, 15) for the maximum allocation
model.
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For illustration, Table 4 shows the performance under normal
conditions of a Russian Dolls model with bandwidth constraints (6,
15, 15).
Table 4. Blocking and preemption probabilities
Model PB1 PB2 PB3 PP2 PP3 PB2+PP2 PB3+PP3
RussDoll 0.03692 0.00060 0.02800 0.00032 0.02740 0.00092 0.05540
Regardless of whether deterministic guarantees are used or not, both
models are bounded by the same aggregate constraint of the link
capacity. Also, in both models, 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 models (ordering, hard versus soft boundary), the dimensions of
one model 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 bandwidth
constraints of a model 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 model. Thus, we are comparing how a
given model performs when preemption is fully enabled versus how the
same model performs when preemption is partially enabled. The
performance of these preemption modes is shown in Figures 3 to 5 for
the Russian Dolls, and Figures 6 to 8 for the maximum allocation
model, respectively. In all of these figures, the bandwidth
constraints of Section 3.2 are used for illustration, i.e., (6, 7,
15) for maximum allocation model and (6, 11, 15) for Russian Dolls
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model. However, the general behavior is similar when the bandwidth
constraints 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
Let us first examine the performance under the Russian Dolls model.
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
bandwidth constraint 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
Turning now to the maximum allocation model, 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
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Figures 1 and 6). This is similar to the Russian Dolls model,
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
seen that the performance of the maximum allocation model 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 the Russian Dolls model, 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
For the Russian Dolls model, we consider two different settings:
"Russian Dolls (1)" bandwidth constraints:
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)" bandwidth constraints:
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 bandwidth constraints 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
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eight data points for each of the three series. However, the
bandwidth constraints can be tuned to get a bigger differentiation.
6.2 Maximum Allocation
For the maximum allocation model, we also consider two different
settings:
"Exp. Max. Alloc. (1)" bandwidth constraints:
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)" bandwidth constraints:
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 bandwidth constraints 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 the
explicit maximum allocation algorithm 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 the
Russian Dolls algorithm 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 the explicit maximum
allocation algorithm is somewhat higher than the Russian Dolls
algorithm, because of reduced sharing.
The difference in the behavior of the Russian Dolls algorithm with
or without preemption has already been discussed at the beginning of
this section. For the explicit maximum allocation algorithm, 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.
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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
complete sharing of the total bandwidth among all the classes.
For the explicit maximum allocation model, this means that the
constraints are such that up to 15 simultaneous LSPs are allowed for
any class.
Similarly, for the Russian Dolls model, the constraints 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 the two models.
Figure 13 shows the performance when all classes have equal access
to link bandwidth under the complete sharing scheme.
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 the
explicit maximum allocation or Russian Dolls models. In a sense,
class 3 is starved under overload as no protection of its traffic is
being provided under complete sharing.
8. Implications on Selection 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 models 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 the Russian Dolls model
has a higher degree of sharing then explicit maximum allocation,
both converge ultimately to the complete sharing model as the degree
of sharing in each of them is increased. Figure 14 shows that, for
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a single link, the overall loss probability is the smallest under
complete sharing and the largest under explicit maximum allocation,
with Russian Dolls being intermediate. Expressed differently,
complete sharing yields the highest link efficiency and explicit
maximum allocation 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.)
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 selection
criterion (5) 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 scheme, as shown in Figure 14.
The intention of criterion (2) in judging the effectiveness of
different models 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 model 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
Bandwidth constraints models are used in DS-TE for path computation
and admission control of LSPs by enforcing different bandwidth
constraints for different classes of traffic so that Diffserv QoS
performance can be maximized. Therefore, it is of interest to
measure the performance of a bandwidth constraints model by the LSP
blocking/preemption probabilities under various operational
conditions. Based on this, the performance of the Russian Dolls and
the maximum allocation models 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.
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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 models are:
Russian Dolls model
. 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
Maximum allocation model
. 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 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
No new security considerations are raised by the bandwidth
constraints models presented in this document; they are the same as
in the DS-TE Requirements document [1].
11. References
Normative References
1 F. Le Faucheur and W.S. Lai, "Requirements for Support of
Differentiated Services-aware MPLS Traffic Engineering," RFC
3564, July 2003.
Informative References
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.
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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. (URL: http://www.columbia.edu/~ffl5/waisum/bcmodel.pdf)
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.
12. Acknowledgments
Inputs from Jerry Ash, Jim Boyle, Anna Charny, Sanjaya Choudhury,
Dimitry Haskin, Francois Le Faucheur, Vishal Sharma, and Jing Shen
are much appreciated.
13. 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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