One document matched: draft-zhang-pcn-performance-evaluation-00.txt
Network Working Group J. Zhang
Internet-Draft Cisco Systems, Inc. and Cornell
Intended status: Informational University
Expires: April 18, 2007 A. Charny
V. Liatsos
F. Le Faucheur
Cisco Systems, Inc.
October 15, 2006
Performance Evaluation of CL-PHB Admission and pre-emption Algorithms
draft-zhang-pcn-performance-evaluation-00.txt
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Abstract
Pre-Congestion Notification [I-D.briscoe-tsvwg-cl-architecture]
approach proposes Admission Control to limit the amount of real-time
PCN traffic to a configured level during the normal operating
conditions, and Preemption use to tear-down some of the flows to
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bring the PCN traffic level down to a desirable amount during
unexpected events such as network failures, with the goal of
maintaining the QoS assurances to the remaining flows. Preliminary
performance evaluation results on example admission and Preemption
mechanisms were presented in [I-D.briscoe-tsvwg-cl-phb]. This draft
presents the results of a follow-up simulation study and identifies a
number of open issues.
Requirements Language
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 [RFC2119].
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
2. Simulation Setup and Environment . . . . . . . . . . . . . . . 5
2.1. Network and Signaling Model . . . . . . . . . . . . . . . 5
2.2. Traffic Models . . . . . . . . . . . . . . . . . . . . . . 6
2.2.1. Voice CBR . . . . . . . . . . . . . . . . . . . . . . 7
2.2.2. VBR Voice . . . . . . . . . . . . . . . . . . . . . . 7
2.2.3. High Peak-to-Mean Ratio VBR ("Video") Traffic . . . . 7
2.3. Simulation Environment . . . . . . . . . . . . . . . . . . 8
3. Admission Control . . . . . . . . . . . . . . . . . . . . . . 8
3.1. Parameter Settings . . . . . . . . . . . . . . . . . . . . 8
3.1.1. Virtual queue settings . . . . . . . . . . . . . . . . 8
3.1.2. Egress measuments . . . . . . . . . . . . . . . . . . 9
3.2. Basic Bottleneck Aggregation Results . . . . . . . . . . . 9
3.3. Sensitivity to Call Arrival Assumptions . . . . . . . . . 11
3.4. Sensitivity to Marking Parameters at the Bottleneck . . . 12
3.4.1. Ramp vs Step Marking . . . . . . . . . . . . . . . . . 13
3.4.2. Sensitivity to Virtual Queue Marking Thresholds . . . 13
3.5. Sensitivity to RTT . . . . . . . . . . . . . . . . . . . . 14
3.6. Sensitivity to EWMA weight and CLE . . . . . . . . . . . . 14
3.7. Effect of Ingress-Egress Aggregation . . . . . . . . . . . 17
4. Pre-Emption . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1. Pre-emption Model and Key Parameters . . . . . . . . . . . 18
4.2. Pre-emption experiments . . . . . . . . . . . . . . . . . 19
4.2.1. Ingress-Egress Aggregation Experiments . . . . . . . . 19
4.2.2. Effect of RTT Difference . . . . . . . . . . . . . . . 25
5. Summary of Results . . . . . . . . . . . . . . . . . . . . . . 27
5.1. Summary of Admission Control Results . . . . . . . . . . . 27
5.2. Summary and Discussion of Pre-emption Results . . . . . . 28
6. Future work . . . . . . . . . . . . . . . . . . . . . . . . . 28
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 29
8. Security Considerations . . . . . . . . . . . . . . . . . . . 29
9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 29
9.1. Normative References . . . . . . . . . . . . . . . . . . . 29
9.2. Informative References . . . . . . . . . . . . . . . . . . 29
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 30
Intellectual Property and Copyright Statements . . . . . . . . . . 31
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1. Introduction
Pre-Congestion Notification [I-D.briscoe-tsvwg-cl-architecture]
approach proposes Admission Control to limit the amount of real-time
PCN traffic to a configured level during the normal operating
conditions, and Preemption use to tear down some of the flows to
bring the PCN traffic level down to a desirable amount during
unexpected events such as network failures, with the goal of
maintaining the QoS assurances to the remaining flows. In
[I-D.briscoe-tsvwg-cl-architecture], Admission and Preemption use two
different markings and two different metering mechanisms in the
internal nodes of the PCN region.
An initial simulation study was reported in
[I-D.briscoe-tsvwg-cl-phb], where it was shown that both admission
and Preemption mechanism discussed there have reasonable performance
in a limited set of experiments performed here. This draft reports
the next installment of the simulation results. For completeness and
convenience of exposition, most of the results earlier presented in
[I-D.briscoe-tsvwg-cl-phb] have been moved into this draft.
The new results presented in the current draft further confirm that
admission and Preemption algorithms of [I-D.briscoe-tsvwg-cl-phb]
perform well under a range of operating conditions and are relatively
insensitive to parameter variations around a chosen operation range.
Perhaps the most interesting (and quite unexpected) conclusion that
can be drawn from these results is that both Admission and Preemption
algorithms appear to be not as sensitive to low per ingress-egress-
pair aggregation as one might fear. This result is quite
encouraging: while it seems reasonable to assume sufficient
bottleneck link aggregation, it is not very clear whether one can
safely assume high levels of aggregation on a per ingress-egress-pair
basis. However, more work is necessary to evaluate whether this
moderate sensitivity to ingress-egress aggregation can be safely
relied upon under a broader range of conditions. Other conclusions
and a discussion of issues are presented in Section 5.
Section 2 describes simulation environment and models, Admission and
Preemption simulation results are presented in sections 3 and 4, and
section 5 summarizes the results of the simulations so far and lists
areas for further study.
1.1. Terminology
o Pre-Congestion Notification (PCN): two algorithms that determine
when a PCN-enabled router Admission Marks and Preemption Marks a
packet, depending on the traffic level.
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o Admission Marking condition- the traffic level is such that the
router Admission Marks packets. The router provides an "early
warning" that the load is nearing the engineered admission control
capacity, before there is any significant build-up in the queue of
packets belonging to the specified real-time service class.
o Preemption Marking condition- the traffic level is such that the
router Preemption Marks packets. The router warns explicitly that
Preemption may be needed.
o Configured admission rate - the reference rate used by the
admission marking algorithm in a PCN-enabled router.
o Configured preemption rate - the reference rate used by the
Preemption marking algorithm in a PCN-enabled router.
o CLE - congestion level estimate computed by the egress node by
estimating as the fraction of admission-marked packets it receives
2. Simulation Setup and Environment
2.1. Network and Signaling Model
In some simulations, the network is modelled as a single link between
an ingress and an egress node, all flows sharing the same link.
Figure 2.1 shows the modelled network. A is the ingress node and B
is the egress node.
Fig.
A-----B
Fig. 2.1 Simulated Single Link Network (Referred to as Single Link
Topology)
A subset of simulations uses a network structured similarly to the
network shown on Figure 2.2. A set of ingresses (A,B,C) connected to
an interior node in the network (D) with links of different
propagation delay. This node in turn is connected to the egress (F).
In this topology, different sets of flows between each ingress and
the egress converge on the single link, where Pre-congestion
notification algorithm is enabled. The ingress link capacity is
assumed to be sufficiently large so that neither admission nor
Preemption mechanisms have any effect on Them. All links are
assigned a propagation delay. The point of congestion (link (D-F)
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connecting the interior node to the egress node) is modelled with a
1ms or 10ms propagation delay. In our simulations, the network has a
range from 2 to 600 ingress nodes, each connected to the interior
node with a range of propagation delay (1ms to 100ms). In some
experiments all ingress links have the same propagation delay, and in
some experiments the delay of different ingresses vary in the range
from 1 to 100 ms.
A
\
B - D - F
/
C
Fig. 2.2. Simulated Multi-Link Network (Referred to as RTT Topology)
Simulations on more sophisticated topologies are not reported in this
draft, and remain the area for future investigation. Our simulations
concentrated primarily on the range of capacities of 'bottleneck'
links with sufficient aggregation - above 10 Mbps for voice and 622
Mbps for "video", up to 1 Gbps. But we also investigated slower
'bottleneck' links down to 512 Kbps in some experiments. In the
simulation model of admission control, a call request arrives at the
ingress and immediately sends a message to the egress. The message
arrives at the egress after the propagation time plus link processing
time (but no queuing delay). When the egress receives this message,
it immediately responds to the ingress with the current Congestion
Level Estimate. If the Congestion Level Estimate is below the
specified CLE- threshold, the call is admitted, otherwise it is
rejected.
For preemption, once the ingress node of a PCN region decides to
preempt a call, that call is preempted immediately and sends no more
packets from that time on. The life of a call outside the domain
described above is not modelled. Propagation delay from source to
the ingress and from destination to the egress is assumed negligible
and is not modelled.
2.2. Traffic Models
Three types of traffic were simulated (CBR voice, on-off traffic
approximating voice with silence compression, and on-off traffic with
higher peak and mean rates (we termed the latter "video" as the
chosen peak and mean rate was similar to that of an MPEG video
stream, although no attempt was made to match any other parameters of
this traffic to those of a video stream). The distribution of flow
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duration was chosen to be exponentially distributed with mean 2min,
regardless of the traffic type. In most of the experiments flows
arrived according to a Poisson distribution. In addition, some
experiments investigated a batch Poisson model. Here the batch
represented a set of calls arriving at almost the same time. The
batch arrival process was Poisson, and the batch size was
geometrically distributed with a mean of up to 5 calls per batch.
For on-off traffic, on and off periods were exponentially distributed
with the specified mean. Traffic parameters for each flow are
summarized below.
2.2.1. Voice CBR
This traffic is intended to closely approximates CBR voice codex, and
is referred to in the simulation study as "CBR". Its parameters are:
o Average rate 64 Kbps,
o Packet length 160 bytes
o packet inter-arrival time 20ms
2.2.2. VBR Voice
This traffic is intended to approximate voice with silence
compression. It is referred to in the simulation study as "VBR", and
uses the following parameters:
o Packet length 160 bytes
o Long-term average rate 21.76 Kbps
o On Period mean duration 340ms; during the on period traffic is
sent with the CBR voice parameters described above
o Off Period mean duration 660ms; no traffic is sent during the off
period
2.2.3. High Peak-to-Mean Ratio VBR ("Video") Traffic
This model is on-off traffic with video-like mean-to-peak ratio and
mean rate approximating that of an MPEG video stream. No attempt is
made to simulate any other aspects of a video stream, and this model
is merely that of on-off traffic. Although there is no claim that
this model represents the performance of video traffic under the
algorithms in question adequately, intuitively, this model should be
more challenging for a measurement-based algorithm than the actual
MPEG video, and as a result, 'good' or "reasonable" performance on
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this traffic model indicates that video traffic should perform at
least as well. Nevertheless, for brevity this traffic is labeled as
"video" in the simulation reports below.
Parameters used for this traffic models are:
o Long term average rate 4 Mbps
o On Period mean duration 340ms; during the on-period the packets
are sent at 12 Mbps
o 1500 byte packets, packet inter-arrival: 1ms
o Off Period mean duration 660ms
2.3. Simulation Environment
The simulation study reported here used purpose built discrete-event
simulator implemented in ECLiPSe Language
(http://eclipse.crosscoreop.com/eclipse). The latter is intended for
general programming tasks, and is especially suitable for rapid
prototyping. Simulations were run on Enterprise Linux Red Hat, IBM
eServer x335, 3.2GHz Intel Xeon, 4GB RAM.
3. Admission Control
3.1. Parameter Settings
3.1.1. Virtual queue settings
Unless otherwise specified, most of the simulations were run with the
following Virtual Queue thresholds:
o min-marking-threshold: 5ms at virtual queue rate
o max-marking-threshold: 15ms at lvirtual queue rate
o virtual-queue-upper-limit: 20ms at virtual queue rate
The virtual-queue-upper-limit puts an upper bound on how much the
virtual queue can grow. Note that the virtual queue is drained at a
configured rate smaller than the link speed. Most of the simulations
were set with the configured-admission-rate of the virtual queue at
half the link speed. Note that as long as there is no packet loss,
the admission control scheme successfully keeps the load of admitted
flows at the desired level regardless of the actual setting of the
configured-admission- limit. However, it is not clear if this
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remains true when the configured-admission-rate is close to the link
speed/actual queue service rate. Further work is necessary to
quantify the performance of the scheme with smaller service rate/
virtual queue rate ratio, where packet loss may be an issue.
3.1.2. Egress measuments
The CLE is computed as an exponential weighted moving average (EWMA)
with a weight of 0.01. In the simulation results presented in
sections 3.2 and 3.3 the CLE is computed on a per-packet basis as it
is that setting that was used in [I-D.briscoe-tsvwg-cl-phb], from
which these results are taken. For those experiments the CLE value
0.5 and EWMA weight of 0.01 are used unless otherwise specified. Our
subsequent study indicated that there is no significant difference
between the observed performance of interval-based and per-packet
egress measurements. Since interval based measurements for a large
number of ingresses are substantially easier for hardware
implementations, subsequent studies (reported in sections ???)
concentrated on the interval based egress measurement. The
measurement interval was chosen to be 100ms, and a range of CLE
values and EWMA weights was explored, as specified in specific
experiment descriptions.
3.2. Basic Bottleneck Aggregation Results
One of the assumptions in [I-D.briscoe-tsvwg-cl-architecture] is that
there is sufficient aggregation on the "bottleneck" links. Our first
set of experiments revolved around getting some preliminary intuition
of what constitutes "enough bottleneck aggregation" for the traffic
models. To that end we fixed configured admission rate at half the
link speed in the range of T1 (1.5 Mbps) through 1Gbps, and examined
the level of aggregation at different link speeds for different
traffic models corresponding to the chosen configured admission rate
at those speeds. Further, to eliminate the issue of whether ingress-
egress pair aggregation has any significant effect, in the
experiments performed in this section we used single link topology
only, so that all flows shared the same ingress-egress pair.
We found that on links of capacity from 10Mbps to OC3, admission
control for CBR voice and ON_OFF voice traffic work reliably with the
range of parameters we simulated, both with Poisson and Batch call
arrivals. As the performance of the algorithm was quite good at
these speeds, and generally becomes the better the higher the degree
of aggregation of traffic, we chose to not investigate higher link
speeds for CBR and on-off voice, within the time constraints of this
effort. The performance at lower link speeds was substantially
worse, and these results are not presented here. These results
indicate that a rule of thumb, admission control algorithm described
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in [I-D.briscoe-tsvwg-cl-architecture] should not be used at
aggregations substantially below 5 Mbps of aggregate rate even for
voice traffic (with or without silence compression). For higher-rate
on-off "video" traffic, due to time limitations we simulated 1Gbps
and OC12 (622 Mbps) links and Poisson arrivals only. Note that due
to the high mean and peak rates of this traffic model, slower links
are unlikely to yield sufficient level of aggregation of this type of
traffic to satisfy the flow aggregation assumptions of
[I-D.briscoe-tsvwg-cl-architecture]. Our simulations indicated that
this model also behaved quite well at these levels of aggregation,
although the deviation from the configured-admission-rate is slightly
higher in this case than for the less bursty traffic models.
Recalling that simulated "video" model is in fact just on-off traffic
with high peak rate and video-like peak ratio, we believe that the
actual video will behave only better, and hence it follows that with
bottleneck aggregation of the order of 150 video flows the admission
control algorithm is expected to perform reasonably well. Note
however that this statement assumes sufficient per ingress-egress
pair aggregation as well.
For these link speeds and traffic models, we investigated the demand
overload of 2x-5x. Performance at lower levels of overload is
expected to be only better, and higher levels of overloads have not
been studied due to time limitations. Table 3.1 below summarizes the
worst case difference between the admitted load vs. Configured
admission rate (which we refer to as over-admisison-perc). The worst
case difference was taken over all experiments with the corresponding
range of link speeds and demand overloads. In general, the higher
the demand, the more challenging it is for the admission control
algorithm due to a larger number of near-simultaneous arrivals at
higher overloads, and as a result the worst case results in Table 3.1
correspond to the 5x demand overload experiments.
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----------------------------------------------------------------------
| | | | overadmission | standard |
| Link type | traffic | call | percent | deviation to |
| | type | arrival | | conf-adm-rate|
| | | process | | ratio |
----------------------------------------------------------------------
|T3,100Mbps,OC3 | CBR | POISSON | 0.5% | 0.005 |
----------------------------------------------------------------------
|T3,100Mbps,OC3 |ON-OFF V | POISSON | 2.5% | 0.025 |
----------------------------------------------------------------------
|T3,100Mbps,OC3 | CBR | BATCH | 1.0% | 0.01 |
----------------------------------------------------------------------
|T3,100Mbps,OC3 |ON-OFF V | BATCH | 3.0% | 0.03 |
----------------------------------------------------------------------
| 1Gbps | "Video" | POISSON | 2.0% | 0.08 |
----------------------------------------------------------------------
| OC12 | "Video" | POISSON | 0.0% | 0.1 |
----------------------------------------------------------------------
Table 3.1. Summary of the admission control results for links above
T3 speeds. Note: T3 = 45Mbps, OC3 = 155Mbps, OC12 = 622Mbps.
3.3. Sensitivity to Call Arrival Assumptions
In the previous section we listed that at sufficient levels of
aggregation Poisson call arrivals assumption was not critical in the
sense that even a burstier, batch arrival process resulted in a
reasonable performance for all traffic models. In this section we
investigate to what extent the Poisson call arrival assumption affect
the accuracy of the admission control algorithm. The results
presented here show that the Poisson call arrival assumption matters
significantly at all levels of aggregation, while at lower levels of
aggregation it makes the difference between poor but possibly
tolerable performance to completely unacceptable (see below).
To that end we investigated the comparative performance of the
algorithm with Poisson and Batch call arrival processes for the CBR
and VBR voice traffic. The mean call arrival rate was the same for
both processes, with the demand overloads ranging from 2x to 5x.
Table 3.2 below summarizes the difference between the admitted load
and the configured-admission-rate for CBR Voice in the case of
Poisson and Batch arrivals. Table 3.3 provides a similar summary for
on-off traffic simulating voice with silence compression. The
results in the tables correspond to the worst case across all
overload factors (and when multiple links speeds are listed, across
all those link speeds).
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-------------------------------------------------------------
| Link type | arrival |overadmission | standard |
| | model |percent | deviation to |
| | | | conf-adm-rate|
| | | | ratio |
-------------------------------------------------------------
| 1Mbps, T1 | BATCH | 30.0% | 0.30 |
-------------------------------------------------------------
| 10 Mbps | BATCH | 5.0% | 0.08 |
-------------------------------------------------------------
|T3,100Mbps,OC3| BATCH | 1.0% | 0.01 |
-------------------------------------------------------------
| 1Mbps, T1 | POISSON | 5.0% | 0.10 |
-------------------------------------------------------------
| 10 Mbps | POISSON | 1.0% | 0.02 |
-------------------------------------------------------------
|T3,100Mbps,OC3| POISSON | 0.5% | 0.005 |
-------------------------------------------------------------
Table 3.2. Comparison of Poisson and Batch call arrival models for
CBR voice. Note: T1 = 1.5Mbps, T3 = 45Mbps, OC3 = 155Mbps, OC12 =
622Mbps
-------------------------------------------------------------
| Link type | arrival | overadmission | standard |
| | model | percent | deviation to |
| | | | conf-adm-rate|
| | | | ratio |
-------------------------------------------------------------
| 1Mbps, T1 | BATCH | 40.0% | 0.30 |
-------------------------------------------------------------
| 10 Mbps | BATCH | 8.0% | 0.06 |
-------------------------------------------------------------
|T3,100Mbps,OC3| BATCH | 3.0% | 0.03 |
-------------------------------------------------------------
| 1Mbps, T1 | POISSON | 15.0% | 0.20 |
-------------------------------------------------------------
| 10 Mbps | POISSON | 7.0% | 0.06 |
-------------------------------------------------------------
|T3,100Mbps,OC3| POISSON | 2.5% | 0.025 |
-------------------------------------------------------------
Table 3.3. Comparison of Poisson and Batch call arrival models for
VBR voice with silence compression. Note: T1 = 1.5Mbps, T3 = 45Mbps,
OC3 = 155Mbps, OC12 = 622Mbps.
3.4. Sensitivity to Marking Parameters at the Bottleneck
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3.4.1. Ramp vs Step Marking
Draft [I-D.briscoe-tsvwg-cl-architecture] gave an option of "ramp"
and "step" marking at the bottleneck. The behaviour of the
congestion control algorithm in all simulation experiments we
performed did not substantially differ depending on whether the
marking was "ramp", i.e. whether a separate min-marking-threshold and
max-marking-threshold were used, with linear marking probability
between these thresholds, or whether the marking was "step" with the
min-marking-threshold and max-marking-threshold collapsed at the max-
marking-threshold value, and marking all packets with probability 1
above this collapsed threshold. However, the difference between
"ramp" and "step" may be more visible in the multiple congestion
point case (recall that only a single congestion point experiments
were performed so far). Another possible reason for this apparent
lack of difference between "ramp" and "step" may relate to the choice
of the egress measurement parameters and a relatively high CLE
threshold of 0.5 Choosing a lower CLE-acceptance threshold and a
faster measurement timescale may result in a better sensitivity to
lower levels of marked traffic. Investigating the interaction
between settings of the marking thresholds, the CLE-threshold, and
the measurement parameters at the egress remains an area of future
investigation.
3.4.2. Sensitivity to Virtual Queue Marking Thresholds
The limited number of simulation experiments we performed indicate
that the choice of the absolute value of the min- marking-threshold,
the max-marking-threshold and the virtual-queue- upper-limit can have
a visible effect on the algorithm performance. Specifically,
choosing the min-marking-threshold and the max-marking- threshold too
small may cause substantial under-utilization, especially on the slow
links. However, at larger values of the min- marking-threshold and
the max-marking-threshold, preliminary experiments suggest the
algorithm's performance is insensitive to their values. The choice
of the virtual-queue-upper-limit affects the amount of over-admission
(above the configured-admission-rate threshold) in some cases,
although this effect is not consistent throughout the experiments.
The Table 3.4 below gives a summary of the difference between the
admitted load and the configured-admission-rate as a function of the
virtual queue parameters, for the 4 Mbps on-off traffic model. The
results in the table represent the worst case result among the
experiments with different degree of demand overloads in the range of
2x-5x. Typically, higher deviation of admitted load from the
configured-admission-rate occurs for the higher degree of demand
overload. The sensitivity of smoother CBR and VBR voice traffic
models to the variation of these parameters is not as significant
that presented in Table 3.4 for video.
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-------------------------------------------------------------
| | | | standard |
| Link type |min-threshold, | overadmission | deviation to |
| |max-threshold, | percent | conf-adm-rate|
| |upper-limit(ms)| | ratio |
------------------------------------------------------------
| 1Gbps |5, 15, 20 | 6.0% | 0.08 |
-------------------------------------------------------------
| 1Gbps |1, 5, 10 | 2.0% | 0.07 |
-------------------------------------------------------------
| 1Gbps |5, 15, 45 | 2.0% | 0.08 |
-------------------------------------------------------------
| OC12 |5, 15, 20 | 5.0% | 0.11 |
-------------------------------------------------------------
| OC12 |1, 5, 10 | 2.0% | 0.13 |
-------------------------------------------------------------
| OC12 |5, 15, 45 | 0.0% | 0.10 |
-------------------------------------------------------------
Table 3.4. Sensitivity of 4 Mbps on-off "video" traffic to the
virtual queue settings. Note: T1 = 1.5Mbps, T3 = 45Mbps, OC3 =
155Mbps, OC12 = 622Mbps
3.5. Sensitivity to RTT
We performed a limited amount of sensitivity analysis of the
admission control algorithm used to the range of round trip
propagation time (which is the dominant component of the control
delay in the typical environment using Pre-congestion notification).
We considered both the case when all flows in a given experiment had
the same RTT from this range, and also when RTT of different flows
sharing a single bottleneck link in a single experiment had a range
of round trip delays between 22 and 220 ms. The results were good
for all types of traffic tested, implying that the admission control
algorithm is not sensitive to the either the absolute value of the
round-trip propagation time or relative value of the round-trip
propagation time, at least in the range of values tested. We expect
this to remain true for a wider range of round-trip propagation
times.
3.6. Sensitivity to EWMA weight and CLE
This section represents the results of the investigation the combined
effect of the EWMA weight and CLE setting at the egress in two
settings: on a Single Link topology of Fig. 2.1 with all flows on the
bottleneck link sharing the same ingress and egress pair, and on a
RTT topology of Fig. 2.2 with 100 ingress links.
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As discussed earlier, the actual choice of RTT values of different
ingress links does not appear to have any significant effect on the
simulation results. We believe that any appreciable difference
between the two topologies relates to the fact that the degree of
aggregation of each ingress-egress pair is much larger (100 times) in
the Single Link topology than in the case of an RTT topology. This
is especially true for the case of video, where with the chosen
parameters the desired state after Preemption is only one flow per
ingress on the average.
Table 3.5 summarized the over-admission-percentage value from 32
experiments with different [weight, CLE threshold] settings over the
two topologies. The overload column represents the ratio of the
demand on the bottleneck link to the configured admission threshold.
While in our simulations we tested the range of overload from 0.95 to
5, we present here only the results of the endpoints of this overload
interval. For the intermediate values of overload the results are
even closer to the expected than at the two boundary loads.
These statistics show that over-admission-percentage values are
rather similar, with the admitted load staying within -2%+2% range of
the desired admission threshold, with quite limited variability.
Note that the load of 0.95 corresponds to the case when the demand is
below the configured admission rate, so the ideal performance of an
admission control algorithm would be admit all flows demanding
admission. Any negative value of the overload indicates that the
admission control erroneously blocks some number of flows under
underload.
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-------------------------------------------------------------------
| Over Admission Perc Stats | Over | Topo | Type |
| Min | Median | Mean | Max | SD | Load | | |
-------------------------------------------------------------------
| 0.007 | 0.007 | 0.007 | 0.007 | 0 | 0.95 | | |
|---------------------------------------------------| S.Link | |
| 0.224 | 0.792 | 0.849 | 1.905 | 0.275 | 5 | | |
|------------------------------------------------------------| CBR |
| 0.008 | 0.008 | 0.008 | 0.008 | 0 | 0.95 | | |
|---------------------------------------------------| RTT | |
| 0.200 | 0.857 | 0.899 | 1.956 | 0.279 | 5 | | |
|-------------------------------------------------------------------
| -1.45 | -0.96 | -0.98 | -0.86 | 0.117 | 0.95 | | |
|---------------------------------------------------| S.Link | |
| -0.07 | 1.507 | 1.405 | 1.948 | 0.421 | 5 | | |
|------------------------------------------------------------| VBR |
| -1.56 | -0.75 | -0.80 | -0.69 | 0.16 | 0.95 | | |
|---------------------------------------------------| RTT | |
| -0.11 | 1.577 | 1.463 | 2.199 | 0.462 | 5 | | |
-------------------------------------------------------------------
Table 3.5 Summarized performance for CBR and VBR across different
parameter settings and topologies
For Video-like high-rate VBR traffic, the algorithms does show
certain sensitivity to parameters. Table 3.6 records the over-
admission-percentage for each combination of weights and CLE
threshold.
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-- --------------------------------------------------------------------
| | EWMA Weights | Over | Topo |
| | 0.1 | 0.3 | 0.5 | 0.7 | 0.8 | Load | |
-- --------------------------------------------------------------------
| 0.05 | -4.87 | -3.05 | -2.92 | -2.40 | -2.40 | | |
| 0.15 | -3.67 | -2.99 | -2.40 | -2.40 | -2.40 | 0.95 | |
| 0.25 | -2.67 | -2.40 | -2.40 | -2.40 | -2.40 | | |
| C 0.5 | -0.24 | -1.60 | -2.40 | -2.40 | -2.40 | | Single |
| L ----------------------------------------------------------- Link |
| E 0.05 | -4.03 | 2.52 | 3.45 | 5.70 | 5.17 | | |
| 0.15 | -0.81 | 3.29 | 6.35 | 6.80 | 8.13 | 5 | |
| T 0.25 | 2.15 | 5.83 | 6.81 | 8.62 | 7.95 | | |
| H 0.5 | 6.55 | 9.35 | 9.38 | 8.96 | 8.41 | | |
| R --------------------------------------------------------------------
| E 0.05 | -11.77 | -8.35 | -5.23 | -2.64 | -2.35 | | |
| S 0.15 | -9.71 | -7.14 | -2.01 | -2.21 | -1.13 | 0.95 | |
| H 0.25 | -5.54 | -6.04 | -3.28 | -0.88 | -0.27 | | |
| O 0.5 | -2.00 | -2.56 | -1.52 | 0.53 | 0.39 | | |
| L ----------------------------------------------------------- RTT |
| D 0.05 | -5.04 | -0.65 | 4.21 | 6.65 | 9.90 | | |
| 0.15 | -1.02 | 1.58 | 7.21 | 8.24 | 10.07 | 5 | |
| 0.25 | -0.76 | 1.96 | 7.43 | 9.66 | 11.26 | | |
| 0.5 | 6.70 | 8.42 | 10.10 | 11.11 | 11.02 | | |
-- --------------------------------------------------------------------
Table 3.6 Over-admission-percentage for Video
It follows from these results that while choosing the CLE and EWMA
weights in the middle of the tested range appear to be more
beneficial for the overall performance across the chosen range of
overload, assuming the chosen values for the remaining parameters, at
the same time performance is tolerable across the entire tested range
of both values, even for very small ingress aggregation. The high
level conclusion that can be drawn from Table 3.6 is that
(predictably) high peak-to-mean ratio video-like traffic is
substantially more stressful to the queue-based admission control
algorithm, but a set of parameters exists that keeps the over-
admission within about -3% - +10% of the expected load.
3.7. Effect of Ingress-Egress Aggregation
One of the outcomes of the results presented in the previous section
is that the admission control algorithm of [I-D.briscoe-tsvwg-cl-phb]
seems relatively insensitive to the level of ingress-egress
aggregation. This result is not entirely intuitive, and requires
further exploration. Nevertheless, even if preliminary, these
results are very encouraging: while the assumption of reasonable
aggregation of PCN traffic at an internal bottleneck seems a
relatively safe one, it is much less clear that it is safe to assume
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that high per ingress-egress aggregation level is a safe assumption
in reality. In particular, the "video" setup with only ~100 "video"
flows taking up about 50% of a 1G bottleneck link bandwidth with all
100 flows coming from different ingresses seems entirely plausible.
It is therefore encouraging that the algorithm seems sufficiently
robust under these circumstances.
4. Pre-Emption
4.1. Pre-emption Model and Key Parameters
In all Preemption simulations we use an RTT topology of Figure 2.2
with a varying number of ingress links and a range of RTTs. In all
Preemption experiments presented in this document all but one of the
ingresses were generating average load of traffic so that the sum of
traffic from all ingresses was set to about 1/2 of the configured
preemption rate on the bottleneck link. We refer to these ingresses
as "base" ingresses and traffic generated by them as "base traffic".
The remaining ingress generated traffic that was not initially sent
to the bottleneck link. At some point in the simulation, we emulated
a network "failure" event by taking the packets generated by that
ingress and directing it to the bottleneck link. In all simulations
presented here the "failure" traffic rate was about twice the total
"base" rate, and as a result, the bottleneck rate was 1.5 times the
configured Preemption threshold. Both "base" and "failure flows were
generated according to a Poisson distribution. In the simulation,
the router implementing PCN Preemption Marking operates as described
in [I-D.briscoe-tsvwg-cl-architecture], marking packets which find no
token in the token bucket. When an egress gateway receives a marked
packet from the ingress, it will start measuring its Sustainable-
Aggregate-Rate for this ingress, if it is not already in the pre-
emption mode. If a marked packet arrives while the egress is already
in the pre- emption mode, the packet is ignored. The measurement is
interval based, with 100ms measurement interval chosen in all
simulations. At the end of the measurement interval, the egress
sends the measured Sustainable-Aggregate-Rate to the ingress, and
leaves the Preemption mode. When the ingress receives the
sustainable rate from the egress, it starts its own interval
immediately (unless it is already in a measurement interval), and
measures its sending rate to that egress. Then at the end of that
measurement interval, it preempts the necessary amount of traffic.
The ingress then leaves the Preemption mode until the next time it
receives the sustainable rate estimate from the egress. In all our
simulations the ingress used the same length of the measurement
interval as the egress. The Configured preemption rate was set to
50% of link speed. CBR and VBR voice experiments used an OC3 link,
while "video" experiments used a OC48. Token bucket depth was set to
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256 packets in all experiments presented here.
4.2. Pre-emption experiments
4.2.1. Ingress-Egress Aggregation Experiments
4.2.1.1. Motivation for the Investigation
While sufficiently high bottleneck aggregation is listed as one of
the underlying assumptions of [I-D.briscoe-tsvwg-cl-architecture],
there remains a question of whether of not sufficient degree of
aggregation of traffic on a per ingress-egress pair is also
necessary. Assuming a large degree of aggregation on a per ingress-
egress pair is less attractive, as one can easily imagine that a
bottleneck link in a PCN region may carry traffic from hundreds or
thousands of ingresses, and so one can easily construct cases when
per-ingress-egress pair traffic is generated by a relatively small
number of flows. This is especially true for high-ratevideo flows.
If indeed the number of flows in an ingress-egress pair is small,
theoretically there exists concern that the granularity of preemption
(which can operate on integer number of flows only) will result in
large inaccuracies of the amount of traffic preempted in a per-
ingress-egress aggregate, and consequently a large amount of over-
preemption. As an example of a situation creating this problem
suppose that a bottleneck link is shared by 2N flows, each one of
them coming from a different ingress-egress pair. Suppose that only
N flows can be supported at the configured Preemption rate, so N out
of 2N flows must be preempted. This means that half of the packets
will get Preemption marked. If these marked packets are more or less
uniformly distributed among the flows sharing the bottleneck, one
should expect that every one of the 2N flows will have half of its
packets marked. That in turn would imply that each ingress would
need to preempt half of its traffic, and since it only has one flow,
it would have to preempt that flow (assuming that the number of flows
to preempt is rounded up to the nearest flow) or not preempt any flow
at all (if the rounding down to the nearest flow is done). In either
case the outcome is quite pessimistic- either all flows are
preempted, or the Preemption will not take any effect at all.
Clearly, a similar (although perhaps less drastic effect would be if
a few flows rather than one constitute an ingress-egress pair. The
effect quickly disappears when the rate of an individual flow is
sufficiently small compared to the total rate of the ingress-egress
aggregate.
While a number of possible changes to the ingress behavior could be
considered to solve or alleviate this problem, we set out to
investigate whether this problem does in fact occur in practice. The
key question in that respect is whether or not the packets do indeed
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get marked more or less uniformly among different flows sharing a
bottleneck. The results of this investigation are presented in the
following subsections.
4.2.1.2. Detailed results
To investigate the effect of small ingress-egress aggregation, we
performed the experiments with our three traffic types (CBR and VBR
voice and high-rate on-off "video"-like traffic at different degrees
of ingress aggregation. CBR and VBR voice used an OC3 link while
"video" used an OC48 link, with Preemption threshold set at 50% of
the link bandwidth in all cases. The bottleneck aggregation was
therefore quite high (with respect to the corresponding link
bandwidth), but the ingress-egress aggregation was varied from 2
flows to about 1/3 of the number of flows at the bottleneck. The
results are summarized in Table 4.1 below.
-------------------------------------------------------------------------
|Traffic|Bottleneck| Number | Flows per | Preempt | Preempt | Over-Pre.|
| Model |load at | Ingress | Ingress | Threshold | Perc | Perc |
| |failure | | | | | |
-------------------------------------------------------------------------
| CBR | 1789 | 2 | 582 | 1215 | 32.1% | 0.05% |
------------------------- -----------------------------------------------
| CBR | 1772 | 70 | 9 | 1215 | 32.8% | 1.41% |
-------------------------------------------------------------------------
| CBR | 1782 | 600 | 1 | 1215 | 33.6% | 1.85% |
-------------------------------------------------------------------------
| VBR | 5336 | 2 | 1759 | 3574 | 33.3% | 0.35% |
------------------------- -----------------------------------------------
| VBR | 5382 | 70 | 26 | 3574 | 36.4% | 2.84% |
-------------------------------------------------------------------------
| VBR | 5405 | 1800 | 1 | 3574 | 36.8% | 2.99% |
-------------------------------------------------------------------------
| Video | 402 | 2 | 135 | 305 | 37.5% | 8.95% |
------------------------- -----------------------------------------------
| Video | 417 | 70 | 2 | 305 | 35.2% | 8.39% |
Table 4.1 Effect of ingress-egress aggregation.
In this table, bottleneck load at failure is represented as the
number of flow on the bottleneck after the simulated failure event
has occurred and before the preemption takes place. The "Number
Ingress" column shows the number of ingresses in the RTT topology.
In all cases, ideally, the algorithm should preempt roughly 1/3 of
the traffic after the failure event has occurred (the exact
percentage differs slightly from experiment to experiment due to load
generation implementation). The second to last column shows the
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actual preemption percentage in each experiment and the last column
shows how far it deviates from the optimal value in terms of over-
preemption percentage (where the optimal value is computed based on
the actual number of flows generated in each experiment).
The first conclusion that can be drawn from Table 4.1 is that in
these experiments Preemption worked quite well for CBR and VBR, and
even in the video case with just 2 flows per ingress the over-
preemption is quite bounded.
The second - far more unexpected - outcome of these results is that
for all traffic types in these experiments the result show no
appreciable effect of the ingress aggregation on the degree of
ingress aggregation, as all the preemption percentage do not differ
significantly. Given the discussion in the previous section that
predicted substantial inaccuracy of Preemption in the case of a small
number of flows per ingress, this result appears extremely
encouraging, but does require an explanation and discussion, to which
the next section is dedicated.
4.2.1.3. Analysis of the Ingress Aggregation Results
The results in the previous section were obtained for what seemed to
be reasonable set of parameters. However, the unexpectedness of any
appreciable degradation of performance with very small ingress-egress
aggregation levels called for questioning whether the results are
general enough and remain true for different parameter settings.
Further analysis of the simulation traces of CBR traffic of
experiments of Table 4.1 helped us identify the cause of this
phenomenon. It turned out that in all the simulation runs with CBR
traffic, contrary to our expectation that Preemption marking will be
more or less uniformly distributed among active flows, what actually
happens is that some flows get all their packets marked, while other
flows get no packets marked at all (we refer to this effect loosely
as "synchronization" in the rets of this document). It is this
phenomenon that, in the case of a single flow per ingress, made only
the ingresses whose flows were marked preempt these flows, resulting
in correct amount of preemption.
While our first instinct was to look for bugs in simulator and/or
simulation artefacts, further analysis showed that in fact this
effect is not a simulation artifact, and is a direct consequence of
periodicity of individual CBR flows in combination with a combination
of several parameters. As it happens, if the number of tokens
arriving in the token bucket in an inter-packet interval of a single
CBR flow is an integer multiple of a packet size, then if a packet of
a flow is marked once, all the subsequent packets will find the same
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number of tokens in the token bucket and will also be marked. The
proof of this fact is provided in the companion technical report
Verification of the simulation parameters we used revealed that in
fact that condition held precisely in our CBR simulations presented
in Table 4.1.
This observation implied that if we change the configured preemption
rate by small increments, it would change the token bucket rate, and
hence the number of tokens arriving within the packet inter-arrival
time of a CBR flow will no longer be the an integer multiple of the
packet size. In turn, that should break the synchronization.
However, when we tried to change the configured Preemption rate by
increments of 5%, it turned out that even though perfect
synchronisation was indeed no longer present, the state of the token
bucket encountered by the packets of the same flow was sufficiently
close in the interval relevant for preemption, and it still remained
the case that a large number of flows were either entirely marked or
entirely unmarked in the relevant time interval. In turn that
resulted in still near-perfect performance at the configured rate
intervals we tried!
It took substantial trial and error to find a setting of the
configured rate which finally broke synchronisation enough to see
substantial over-preemption, and even then the over-preemption was
around 7%, which was not even close to the theoretical worst case
described in the previous section. The difficulty we encountered in
finding the configured preemption rate that broke Voice CBR
synchronization can be appreciated by observing that the configured
rate that broke the synchronized marking pattern substantially was
0.050384757292833 of the link speed!
It seems clear that in general this synchronization cannot be relied
upon, and we expected that for the VBR case we will see much less of
it. Again, we were in for a surprise, as trace investigation of our
initial results reported in Table 4.1 revealed that even though the
token bucket state encountered by the packets of the same VBR flow
was not quite the same, it was close enough so that again a large
number of flows was either fully marked or fully unmarked. We
realized that the reason for that is that the number of flows which
are in the on-period during the relevant measurement intervals is
relatively stable, and hence much of the effects observed for the CBR
flows approximately holds for the on-off traffic we use for our VBR
model. Since the on period had the same rate as our CBR model, and
the packet size was the same for the two models, similar behavior was
observed in both sets of experiments.
We then repeated the VBR experiments with the same variation of
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Preemption rate thresholds. However, even in the cases where CBR
experiments did result in visible over-preemption, the VBR
experiments did not! Understanding the reasons for this unexpected
series of better-than expected results remains open at the moment,
and requires further investigation.
With the understanding that strict synchronisation of the token
bucket state with CBR theoretically occurs only when the parameters
are such that the inter-packet interval times the drain rate of the
token bucket is a multiple of the packet size, one should expect that
changing the packet size and/or inter-packet interval of a CBR flow
should break synchronisation. Indeed, the examination of the CBR
portion of the on-period of the video flow reveals that only every
50-th packet of the same flow will see the same token bucket state.
This reflected in the fact that "video" experiments had a large
number of partially marked flows, and synchronization could not have
been responsible for relatively bounded over-preemption of about 9%
reported in Table 4.1
In the video case the ~9% over-preemption was traced to the
burstiness of our crude "video" traffic model at the time scales
commensurate with the measurement period. Just as in the VBR case,
changing configured rate thresholds in the same manner as for CBR
experiments did not result in substantial performance changes!!
In our quest to further understand the unexpectedly reasonable
performance at small ingress-egress aggregation we then tested the
hypothesis that randomizing the packet inter-arrival time must surely
break synchronization of the CBR traffic, and to that end we modified
or CBR traffic model to what we call "randomized CBR". Randomized
CBR is obtained from a CBR stream by randomly moving the packet by a
small amount of time around its transmission time in the
corresponding CBR flow. Repeating the experiment with the randomized
CBR with the configured preemption rate showing CBR synchronization,
we finally were able to see more substantial over-admission of about
13%. However, implementing the same randomization of the on periods
of our VBR and "video" models did not yield any substantial
degradation of performance compared to CBR on-periods.
These results are summarized in Table 4.2 below, and are summarized
in the next subsection
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----------------------------------------------------------------------
| Exp# | Description
----------------------------------------------------------------------
| Sch1 | Uniform arrival with preemption threshold=0.5 |
| Sch2 | Uniform arrival with preemption threshold=0.4 |
| Sch3 | Uniform arrival with preemption threshold=0.6 |
| Sch4 | Uniform arrival with preemption threshold=0.50384757292833 |
| Sch5 | Randomize arrival with preemption threshold=0.5
| Sch6 | Randomize arrival with preemption threshold=0.4 |
----------------------------------------------------------------------
---------------------------------------------------------------------
|Traf |Number |Flow | Over-Preemption Percentage |
|Model|Ingress|per | |
| | |Ingress| Sch1 | Sch2 | Sch3 | Sch4 | Sch5 | Sch6 |
---------------------------------------------------------------------|
|CBR | 70 | 9 | 1.33% | 1.12% |1.20% | 2.66% | 3.89% | 3.94% |
---------------------------------------------------------------------|
|CBR | 600 | 1 | 1.85% | 1.85% |1.12% | 7.51% | 13.9% | 13.6% |
---------------------------------------------------------------------|
|VBR | 70 | 26 | 2.84% | 4.34% |2.36% | 3.88% | 2.47% | 2.56% |
------------------------- -------------------------------------------|
|VBR | 600 | 3 | 1.28% | 2.50% |2.71% | 1.32% | R6 | 1.42% |
------------------------- -------------------------------------------|
|Video| 70 | 2 | 8.39%| 6.96% |11.03%| 9.11% | 9.11% | 8.63% |
------------------------- -------------------------------------------
Table 4.2.
4.2.1.4. Discussion of the Ingress Aggregation Results
The series of experiments reported in the previous section imply that
although not impossible, it appears exceedingly difficult to find the
combination of reasonable parameters where the inaccuracy of the
Preemption is unacceptably high in the case of a single bottleneck
case. These results suggest a need of a further investigation to
explore this unexpected algorithmic sturdiness at small ingress-
egress aggregation.
The fact that slight randomisation of CBR traffic does increase over-
preemption substantially in the simple single bottleneck topology
does suggest a strong need of looking at this phenomenon in the
context of a multi-hop network with multiple bottlenecks, as queuing
at the multiple hops will result in the change of the strict CBR
pattern of the CBR voice.
Investigation of the sensitivity of the accuracy of Preemption at
small ingress-egress aggregation levels for voice traffic should
certainly include simulation of other voice codices and their traffic
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mix.
In general, the unexpected sturdiness of the Preemption algorithm at
small levels of aggregation warrants further investigation of this
phenomenon both from the theoretical point of view and further
simulations.
It is important to not overshadow another conclusion of the results
in this section related to experiments with higher ingress-egress
aggregation. It is clear that they provide further evidence that at
sufficient aggregation levels Preemption algorithm investigated here
works reasonably well, at least in the single bottleneck case.
4.2.2. Effect of RTT Difference
Our experiments indicate that absolute value of RTT within the chosen
range ( up to 220 ms) has no effect on the performance of the
Preemption algorithm. This section investigates the impact of the
difference or RTTs of different flows sharing a single bottleneck.
We show that in principle, the difference in RTT may cause over-
preemption.
To demonstrate that we consider a simple RTT topology with two
ingresses, with CBR traffic. Table 4.3 shows the experiment setup
and preemption results. The overall traffic on the bottleneck during
the event is 1761 CBR flows, which constitutes 75% of OC3 link.
Ingress 2 has a RTT that around 50ms larger than Ingress 1. The
actual preemption percentage and the over-preemption percentage are
listed for each ingress separately. The results shows that Ingress 1
over-preempts about 10% of its traffic, which results in about 6% of
the overall over-preemption at the bottleneck.
---------------------------------------------
|Ingress|Bottleneck| RTT | Preempt | Over-Pre.|
| |Eventload | | Perc | Perc |
---------------------------------------------
| 1 | 1178 | 1ms | 40.5% | 9.59% |
------------------------- -------------------
| 2 | 583 | 50ms| 30.2% | -0.51% |
---------------------------------------------
Table 4.3. Summary of the RTT difference Results.
Figure 4.3 shows a time vs. load graph that is intended to capture
the effect of the preemption algorithm in this experiment. The
X-axis is the time, where a number important time points are labeled
(actual time is listed in table due to lack of space). The Y-axis is
the load on the bottleneck link. The stacked graph on the right
shows the behavior of each individual ingress. (The shade region is
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the load contributes to Ingress 1 and the clear region corresponds to
Ingress 2). Finally, the dotted line represent the preemption
threshold.
| ____ ____
L1| | | | |
| | | | |
| | | | |
| | |_ | |_
| | | | |
L2|....|......|___.................... |___ ..|___........................
| | |__________________ |****| |_______________________
L | | L |****|
o | | o |****|_____
a | | a |**********|_______________________
d | | d |**********************************
|____| |**********************************
| |**********************************
| |**********************************
| |**********************************
| |**********************************
|____|____|_|___|_______________ |____|_|___|________________________
t1 t2 t3 t4 t1 t2 t3 t4
Time Time
---------------------------------
| t1 | t2 | t3 | t4 |
---------------------------------
| 200.0 | 200.2 | 200.25 | 200.40 |
------------------------- -------
Fig 4.4. Time series of preemption events in the RT Difference
experiment
As the simulated failure event occur at time t1 (200s), the load on
the bottleneck goes over the preemption threshold by 1/3, thereby
activating the preemption algorithm. 200ms afterward at t2, which is
sum of the measurements of sustainable rate at the egress (100 ms)
and the consequent ingress measurement of its current sending rate,
Ingress1 with negligible RTT (1ms) start preempting its traffic. 50ms
later at t3, Ingress 2 preempts its share of traffic. Note, at this
point, both of ingresses had preempted the correct amount, which is
why the load on bottleneck between time t3 and t4 is exactly at the
preemption threshold. However the stacked graph shows that Ingress1
did another around of preemption at t4 (200.4), which corresponds to
its 10% over-preemption. The reason for this effect is that during
the interval between t2 and t3, when Ingress1 finishes its
preemptions, and Ingress2 has not yet started due to its longer RTT,
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the non-preempted traffic from Ingress2 will cause a decrement in
Ingress1's sustainable rate during the measurement interval (t2, t2+
100ms). This will in turn cause Ingress1 to preempt at time t4 to
compensate that 50ms of excess traffic from Ingress2. Our follow-up
results indicate that this RTT effect exists in every experiment that
has Ingress RTT difference, independent of the traffic type.
Although for burstier traffic the over-preemption may be worse than
shown above, in our experiments we did not see over-preemption that
would be drastically larger. However, further investigation is
needed to access whether other scenarios might lead to substantial
over-preemption.
5. Summary of Results
The study presented here demonstrated that overall, both admission
control and Preemption algorithms of
[I-D.briscoe-tsvwg-cl-architecture] work reasonably well and are
relatively insensitive to parameter variations.
We can summarize the conclusions of the study so far as follows.
5.1. Summary of Admission Control Results
o We observed no significant benefit of using "ramp" making instead
of a simpler "step" marking
o There appears to be no appreciable sensitivity of the admission
algorithm to either the absolute value of the round-trip time or
the relative value of the round-trip time between different flows
o As a rule of thumb, the level of bottleneck aggregation necessary
to demonstrate tolerable performance even in the simplest network
topology corresponds to links of about 10 Mbps or higher for voice
traffic (CBR of VBR with silence compression), assuming at least
50% of the link speed is allocated to the PCN traffic. For higher
rate bursty "video" flows, 50% of the OC48 of higher appears to be
a reasonable rule of thumb. The higher the degree of bottleneck
aggregation, the better the performance
o Even though larger per ingress-egress pair aggregation results in
better performance of admission control algorithm, performance
remains reasonable even for really low ingress-egress aggregation
levels (i.e. a single or a small number of bursty "video-like"
flow per ingress).
o Poisson call arrival has a visible effect on performance at lower
levels of aggregation (10 Mbps for voice or lower), but is of less
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Internet-Draft CL Simulation Study October 2006
significance at the higher levels of aggregation/link speeds
o The algorithm is relatively insensitive to variation of key
parameter settings at the internal node or the ingress of the PCN
domain, as long as the variations are kept within a reasonable
range around "sensible" parameter settings.
5.2. Summary and Discussion of Pre-emption Results
The simulations results presented in this installment of the
simulation study further demonstrated that at least in a simple one-
bottleneck topology case the preemption mechanism of works reasonably
well for a wide range of parameters for all traffic models we
considered.
The key thrust of this study was the investigation of how much
ingress-egress aggregation is needed for tolerable performance of the
algorithm (assuming sufficient degree of bottleneck aggregation). We
demonstrated that contrary to our expectations, it was not easy to
find cases with sufficiently bad performance. We traced some of this
better-than-expected performance to the effect of synchronization of
the token bucket state for certain combinations of parameter values.
A question of whether this synchronization can be explored to the
benefit of the general operation for voice-only PCN regions remains
open, but seems of substantial interest. Further investigation with
other codices and in a broader set of network conditions is warranted
to address this question.
Our experiments demonstrated that the absolute value of RTT of the
flows sharing the same bottleneck did not have any appreciable effect
as long as the RTT of all flows were the same (or close). However,
we have demonstrated that if RTTs of different flows are
substantially different, longer RTT flows tend to over-preempt,
resulting in overall over-preemption as well. Although a similar
effect (referred to as "beat-down effect" in
[I-D.briscoe-tsvwg-cl-architecture]) has been theoretically expected
in a multi-bottleneck case, the possibility that even in a single
bottleneck case a form of "beat-down" of long-haul flows was not
previously noticed. On the bright side, at least in the experiments
we conducted, the magnitude of the over-preemption was relatively
small.
6. Future work
This draft is but an intermediate step in the investigation of
performance of Admission and Preemption approaches for a PCN region.
Many of the aspects of the real networks have not been addressed due
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to time and resource limitations. These include multiple bottleneck
case, more sophisticated and/or realistic traffic models and traffic
mixes, and many more. Those are subject of on-going investigation.
7. IANA Considerations
This document places no requests on IANA.
8. Security Considerations
There are no new security issues or considerations introduced by this
document.
9. References
9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
9.2. Informative References
[I-D.briscoe-tsvwg-cl-architecture]
Briscoe, B., "An edge-to-edge Deployment Model for Pre-
Congestion Notification: Admission Control over a
DiffServ Region", draft-briscoe-tsvwg-cl-architecture-03
(work in progress), June 2006.
[I-D.briscoe-tsvwg-cl-phb]
Briscoe, B., "Pre-Congestion Notification marking",
draft-briscoe-tsvwg-cl-phb-02 (work in progress),
June 2006.
[I-D.briscoe-tsvwg-re-ecn-border-cheat]
Briscoe, B., "Emulating Border Flow Policing using Re-ECN
on Bulk Data", draft-briscoe-tsvwg-re-ecn-border-cheat-01
(work in progress), June 2006.
[I-D.briscoe-tsvwg-re-ecn-tcp]
Briscoe, B., "Re-ECN: Adding Accountability for Causing
Congestion to TCP/IP", draft-briscoe-tsvwg-re-ecn-tcp-02
(work in progress), June 2006.
[I-D.davie-ecn-mpls]
Davie, B., "Explicit Congestion Marking in MPLS",
Zhang, et al. Expires April 18, 2007 [Page 29]
Internet-Draft CL Simulation Study October 2006
draft-davie-ecn-mpls-00 (work in progress), June 2006.
[I-D.lefaucheur-emergency-rsvp]
Faucheur, F., "RSVP Extensions for Emergency Services",
draft-lefaucheur-emergency-rsvp-02 (work in progress),
June 2006.
Authors' Addresses
Xinyang (Joy) Zhang
Cisco Systems, Inc. and Cornell University
1414 Mass. Ave.
Boxborough, MA 01719
USA
Email: joyzhang@cisco.com
Anna Charny
Cisco Systems, Inc.
1414 Mass. Ave.
Boxborough, MA 01719
USA
Email: acharny@cisco.com
Vassilis Liatsos
Cisco Systems, Inc.
1414 Mass. Ave.
Boxborough, MA 01719
USA
Email: vliatsos@cisco.com
Francois Le Faucheur
Cisco Systems, Inc.
Village d'Entreprise Green Side - Batiment T3 , 400, Avenue de Roumanille
06410 Biot Sophia-Antipolis,
France
Email: flefauch@cisco.com
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Internet-Draft CL Simulation Study October 2006
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