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 Status of this Memo By submitting this Internet-Draft, each author represents that any applicable patent or other IPR claims of which he or she is aware have been or will be disclosed, and any of which he or she becomes aware will be disclosed, in accordance with Section 6 of BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet- Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." The list of current Internet-Drafts can be accessed at http://www.ietf.org/ietf/1id-abstracts.txt. The list of Internet-Draft Shadow Directories can be accessed at http://www.ietf.org/shadow.html. This Internet-Draft will expire on April 18, 2007. Copyright Notice Copyright (C) The Internet Society (2006). 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 Zhang, et al. Expires April 18, 2007 [Page 1] Internet-Draft CL Simulation Study October 2006 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]. Zhang, et al. Expires April 18, 2007 [Page 2] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 3] Internet-Draft CL Simulation Study October 2006 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. Zhang, et al. Expires April 18, 2007 [Page 4] Internet-Draft CL Simulation Study October 2006 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) Zhang, et al. Expires April 18, 2007 [Page 5] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 6] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 7] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 8] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 9] Internet-Draft CL Simulation Study October 2006 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. Zhang, et al. Expires April 18, 2007 [Page 10] Internet-Draft CL Simulation Study October 2006 ---------------------------------------------------------------------- | | | | 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). Zhang, et al. Expires April 18, 2007 [Page 11] Internet-Draft CL Simulation Study October 2006 ------------------------------------------------------------- | 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 Zhang, et al. Expires April 18, 2007 [Page 12] Internet-Draft CL Simulation Study October 2006 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. Zhang, et al. Expires April 18, 2007 [Page 13] Internet-Draft CL Simulation Study October 2006 ------------------------------------------------------------- | | | | 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. Zhang, et al. Expires April 18, 2007 [Page 14] Internet-Draft CL Simulation Study October 2006 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. Zhang, et al. Expires April 18, 2007 [Page 15] Internet-Draft CL Simulation Study October 2006 ------------------------------------------------------------------- | 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. Zhang, et al. Expires April 18, 2007 [Page 16] Internet-Draft CL Simulation Study October 2006 -- -------------------------------------------------------------------- | | 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 Zhang, et al. Expires April 18, 2007 [Page 17] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 18] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 19] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 20] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 21] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 22] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 23] Internet-Draft CL Simulation Study October 2006 ---------------------------------------------------------------------- | 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 Zhang, et al. Expires April 18, 2007 [Page 24] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 25] Internet-Draft CL Simulation Study October 2006 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, Zhang, et al. Expires April 18, 2007 [Page 26] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 27] 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 Zhang, et al. Expires April 18, 2007 [Page 28] Internet-Draft CL Simulation Study October 2006 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 Zhang, et al. Expires April 18, 2007 [Page 30] Internet-Draft CL Simulation Study October 2006 Full Copyright Statement Copyright (C) The Internet Society (2006). 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The IETF invites any interested party to bring to its attention any copyrights, patents or patent applications, or other proprietary rights that may cover technology that may be required to implement this standard. Please address the information to the IETF at ietf-ipr@ietf.org. Acknowledgment Funding for the RFC Editor function is provided by the IETF Administrative Support Activity (IASA). Zhang, et al. Expires April 18, 2007 [Page 31]