Optimized Network Utilisation through Buffering in PCN enabled Multimedia Access Networks Klaas Roobroeck, Steven Latr´e, Tim Wauters and Filip De Turck Ghent University - IBBT - IBCN - Department of Information Technology Gaston Crommenlaan 8/201, B-9050 Gent, Belgium Telephone: +3293314940, Fax: +3293314899 e-mail:
[email protected] Abstract—With the advent of novel services such as IPTV and videoconferencing broadband DSL networks are facing enormous challenges. These services have strict QoS demands in terms of packet loss, jitter and delay. In an effort to meet these demands, operators introduced centralized admission control mechanisms to avoid congestion when too many session were allowed. These centralized approaches often fail to effectively manage the available resources mainly because of the bursty nature of multimedia traffic. When transmitting variable bit rate videos resources are reserved based on the peak rate of the video. This leads to underutilisation of the network. Measurement based admission control mechanism have been proposed such as the IETF Pre-Congestion Notification (PCN) to allow better network utilisation. Each node in the PCN domain measures the network load and admits or blocks accordingly sessions at the edges of the network. Previous research proposed bandwidth metering and an autonomic rate adaptation algorithm which led to a better utilisation of the network but still introduced unnecessary bandwidth headroom caused by the variable bit rate of videos. In this paper, we propose an additional buffering step before traffic enters the PCN domain and determine configuration guidelines for the parameters. The performance of this buffering step has been evaluated in an NS - 2 based simulator environment. The conducted tests show a 26.5% increase of network utilisation.
Fig. 1. Two different ways to realise an admission control system for VBR traffic. Top: current approach where resources are reserved using the peak rate. Bottom: new approach where the aggregate of sessions is protected, leading to better resource utilisation, enabling more sessions to be handled on the same link.
I. I NTRODUCTION The information revolution has been characterized by the growth in available information, as the Internet and other media continues evolve at an incredible pace. Broadband DSL access networks emerged to a Triple Play Service integrating high-speed Internet access, digital television and less bandwidth demanding narrowband services such as VoIP, etc. Earlier these services were forwarded individually by networks such as PSTN, cable television network and the traditional Internet. These Triple Play Services all have large quality requirements: they must meet their predefined Quality of Service (QoS) requirements, in terms of allowable jitter, packet delay and packet loss. Nevertheless, with the advent of Web 2.0 (Youtube, Netflix, etc.) management of resources for Triple Play Services become very difficult. As broadband DSL access networks provide reliable traffic transportation, the main reason packet loss occurs is due to congestion of the network with decreasing Quality of Service as outcome. ISP typically avoid congestion by overdimensioning. To protect sessions and to manage resources, centralized resource admission control mechanisms are used in access networks. Admission control determines bandwidth
allocation to streams with various requirements limiting the amount of flows if required. Furthermore, they are not optimal to protect variable bitrate traffic such as video services which generally have a bursty nature. Because of a lack of an accurate traffic descriptor, common admission control mechanism reserve resources based on the peak rate of the video service, as shown in Figure 1. This leads to an under-utilisation of the reserved resources due to the fact that peak rate is much higher than the average bitrate of the traffic. Aggregating video sessions induces lower variability and lower peak rate as opposed to individual sessions and allows more admitted video sessions. The Internet Engineering Task Force (IETF) is currently working on Congestion and Pre-Congestion Notification (PCN) with the objective to standardize measurement admission control and flow termination for PCN traffic for single DiffServ domains [1]. The PCN working group proposed mechanism is far from finalized. The overall purpose of PCN consists of avoiding packet loss and maximise link utilisation. The aim is to give an early warning of potential congestion before there is any significant build-up of packets
and react accordingly. Firstly, PCN is applied to protect inelastic streams. Secondly, in potential bottleneck scenarios the assumption is that the amount of sessions is large enough to apply stateless statistical mechanisms. The assumptions are violated when coping with video sessions where the maximum rate is unknown beforehand. This necessitates a new approach where video sessions are aggregated leading to higher quality sessions and better link utilisation. For additional information about PCN we refer to [1]. In the past, we proposed a PCN based admission control system to protect transmission of video services by introducing an autonomic rate adaptation algorithm to avoid underutilisation of the network [4] [6]. This was needed to deal with the burstiness of the aggregate traffic. To avoid unnecessary bandwidth headroom which is introduced to cope with bandwidth peaks we propose an additional buffering step before video services enter the PCN domain. In this paper we will investigate the performance of a novel buffering mechanism through NS-2 based simulations of real video sequences in a PCN based aggregation network. The major contributions of this paper are the following: first, we describe a new buffering mechanism based on fair weighted scheduling. Secondly, we compare our test results with the proposed PCN based admission control system using autonomic rate adaptation described in [4] [6]. The rest of the paper is organised as follows. The next section reviews related work concerning admission control. In section III the PCN architecture, the metering, the autonomic rate adaptation algorithm and buffering mechanism is detailed. The description of the evaluated setup and parameters are presented in section IV. Section V details the conducted experiments results. Finally, results and future work are discussed in section VI. II. R ELATED W ORK Admission control mechanisms in broadband DSL network are mostly centralised such as the Resource and Admission Control Subsystem (RACS) and Diffserv used in TISPAN [9] [10]. These approaches require knowledge of the network topology, resources and the followed route by any flow to make admission decisions. Constraints of this approach are that knowledge needed by the system can be large and difficult keeping up to date due to reconfigurations of the network topology introducing scalability issues. Therefore, these approaches succesfully protect existing sessions but typically provide an under-utilized network. This is discussed in [5] where we compare RACS with PCN. Several decentralised admission control mechanisms have been proposed such as the standarized Intserv [11]. Other decentralised mechanisms such as [12] introduce passive measurments which based on this knowledge allow or block sessions. While all previous work are valuable options they never have been standarized or widely adopted due to the heavy resource utilization demanded by IntServ. The Internet Engineering Task Force (IETF) community is researching new admission control mechanisms for the
Internet based on feedback from the network, called precongestion notification (PCN) [2]. The PCN working group develops admission control mechanisms to protect the Quality of Service (QoS) of established inelastic flows within a DiffServ domain when congestion is imminent or existing. PCN requires DiffServ coding point (DSCP) marking behaviour. Each link in a PCN domain has an associated admissable rate and if traffic on the link exceeds this rate then traffic is marked. PCN inner workings are defined by publication of the PCN architecture in a RFC [1]. Recent work evaluates different PCN marking algorithms through simulation [7] [8]. Main differences between these studies and our work are: firstly, our work focuses on transmission of video services, while [7] focuses on narrowband services. The PCN working group’s orginal aim is to protect inelastic flows with a known maximum peak rate, which is not the case for VBR video services. Secondly, [7] and [8] simulations concentrate on flows in order of kilobits while bandwidth of video flows are in the order of megabits. In the performed simulations no feedback loop, which decides to admitting flows, is incorporated. We introduced such a feedback loop in our simulations where requested sessions are give a request arrival model and are allowed or blocked dynamically. Different techniques such as Dynamic Rate Shaping (DRS) have been proposed in [13] [14] to adapt the video rate. This technique adjusts the rate of the compressed video bitstream to dynamically varying rate constraints. In DRS, shaping is preformed at encoder level. Previous studies differ from our work by improving network utilisation introducing an additional buffering step before traffic enters the PCN domain to decrease the variability of the aggregate [4] [6]. III. P RE -C ONGESTION N OTIFICATION A. Architecture The PCN architecture consists of three different node as illustrated in Figure 2: ingress, egress and interior nodes. Packets enter the PCN domain through the ingress nodes and they leave the domain through the egress nodes. Inside the PCN domain each individual interior node measures the congestion level. If the network load exceeds a reference rate, which is called the admissible rate threshold AR, then
Fig. 2. Overview of the PCN architecture with an additional buffering step. Streaming servers are transmitting video to various home networks. PCN interior nodes measure the network load and start marking packets if the load becomes to high.
room for the variability of the aggregate and provides headroom for future fluctuations. This newly calculate admissible rate AR is defined as: Given the goal rate GR, the targeted bandwidth and delta denotes the expected future variability fluctuation. Var(t) is the variability at a given time t and is calculated as: V ar(t) = 2 × (M axBW (t) − AvgBW (t))
CLEn = X × (1 − w) + w × CLEn−1 , w ∈ [0, 1] By periodically signalling the congestion level in the PCN domain the ingress node decides either to block or allow streams. For this purpose the ingress node interacts with the streaming server using an end-to-end Resource Reservation Protocol (RSVP). The main objective of PCN is to protect the quality of service of inelastic flows within a Diffserv domain in a simple, scalable and robust fashion. B. Bandwidth Metering PCN orginally describes to use the token bucket algorithm to measure network load. Another option is to use bandwidth metering. Bandwidth metering algorithm marks arriving packets at the interior nodes when bandwidth aggregate is higher then the admissible rate threshold. We use a sliding window with a fixed measurement (time) interval to measure the bandwidth. When a packet arrives the bandwidth aggregate is calculated during the last time interval and marked if the bandwidth is higher then the admissible rate. Normally measurement windows overlap when using bandwidth metering. Although using a time based algorithm demands more memory then using a token bucket algorithm it has several advantages. Firstly, the bandwidth metering algorithm accuratly calculates the value of the bandwidth aggregate whereas a token bucket only indicates if the bandwidth aggregate is lower or higher than the token rate. Secondly, by using the sliding window technique the mechanism is less sensitive to the bursty nature when transmitting video traffic [5].
MaxBW(t) denotes the maximum bandwidth and AvgBW(t) the average bandwidth. IV. B UFFERING M ECHANISM A. Problem Description The main goal of the autonomic rate adaptation algorithm was converging the admissable rate towards the goal rate introducing a very small amount of variability, Var(t). In figure 3 we detail the bandwidth usage in a traditional PCN configuration using bandwidth metering and autonomic rate adaptation. The higher the aggregate variability is, the lower PCN admissable rate will be configured. Figure 3 introduces a varying bandwidth mainly caused by traffic burstiness introduced by the variability of the individual video sessions. This headroom can be seen as an under-utilisation of the network resulting in less admitted sessions. If we are able to decrease this headroom by introducing an additional buffering step we will increase the admissable rate allowing more admitted sessions. The additional buffering mechanism’s main goal is to decrease the aggregate variability when the network is almost congested and increase the number of admitted video sessions. In order to increase the usable bandwidth and decrease the introduced headroom we apply packet shaping techniques. Different techniques can be used however shaping is normally achieved by delaying packets. By reducing the aggregate variability the autonomic rate adaptation algorithm calculated
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Using a static admissable bandwidth rate threshold leads to an under-utilisation of the network because the variability of the aggregate is not known beforehand the configured admissible rate in a static PCN admission control mechanism should always be lower. The bandwidth admissible rate can not be set statically because the aggregate depends on the type of traffic and the request process. Avoiding congestion and maximise utilisation of the link are main objective from a service provider point of view. The algorithm measures the variability of the aggregated bandwidth during a time interval. AR = GR − V ar(t) × (1 + ∆) Assuming that previous variability measurments are a good estimator for the variability in the near future the algorithm calculates a new admissable rate threshold that still will leave
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PCN-packets are marked with the appropriate encoding. Per pair of egress/ingress nodes the marked packets are collected and then interpreted into a Congestion Level Estimator, CLE which is a value between 0 and 1. The CLE is calculated using an Exponential Weighted Moving Average (EWMA) and signalled back to the PCN ingress node.
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Fig. 3. Aggregate bandwidth for the bandwidth metering approach using autonomic rate adaptation. Sessions are allowed until the bandwidth aggregate is above the bandwidth threshold.
PCN’s admissible rate will increase as discussed in Section III-C. B. Buffering Architecture The buffering mechanism is described in figure 4, which places an additional buffering step between the streaming servers and the PCN ingress nodes. This additional step consists of four different components: a buffer, a classifier, queuing components and a weighted fair scheduler.
Fig. 4.
Buffering mechanism concept.
The buffering algorithm works as follows. We calculate a threshold rate (TR) which is the middle between the admissible rate threshold (AR) and the goal rate (GR). AR + GR 2 The calculated threshold rate is used to construct a buffer which can support bursty traffic with a maximum rate of the threshold rate during a given timeframe, denoted by the buffer size which is a configuration parameter. Packets are served to the buffer queuing component using a DropTail management algorithm where the traffic is not differentiated. Each packet served to the buffer is treated identically. When the buffer is populated to its maximum capacity, incoming packets will be dropped until the buffer has enough room to accept new incoming packets. Due to QoS requirements, dropped packets are not acceptable in video traffic. We serve all packets including the dropped packets to the scheduling component. Incoming packets are classified based on their drop behaviour and transmitted towards their corresponding queue. Finally dropped packets are scheduled with a lower weight than packets which were not dropped and forwarded to the proper ingress nodes. TR wdropped ∈ [0, 1] wdropped = 1 − GR TR =
V. E VALUATION A. Experimental Setup We examinate the preformance of PCN buffering mechanism in broadband aggregation network. These networks have a tree topology. Where the root of the tree consists of one or more video streaming servers and the leaves indicate the home networks of the end-users. The presented results in this paper are all obtained by using a modified version of the NS-2 simulator [3] adding PCN and weighted fair scheduling support. The network topology is presented in Figure 5. This topology consists of a video streaming server offering video streaming service to the end-users. This streaming server is connected by a 1 Gbps link with the buffering mechanism (including the buffer and weighted fair scheduling component). The buffering mechanism forwards all packets towards the Service Router using a 1 Gbps link. A 500 Mbps link between the Service Router and the Service Aggregator results in a congestion point. Due to the fact that there are no other congestion points down in the tree topology the bitrate of the transmitted videos can be maximum 500 Mbps without resulting in a congested network. B. Video Traffic Details We apply PCN to our simulated network by using the Service Router as an ingress node. The Service Router decides whether to admit or block the new requests depending on the congestion state of the network. We assume that once a request has been admitted videos are not stopped. Videos are transmitted to 400 home networks and have a wide range of different content (sports, news, documentary, action movie) using a random uniform process as request arrival process. Persuing a very high compression the videos are encoded using the H.264 videocodec resulting in variable bit rate traffic. They have a frame rate of 25 fps, a 1920 x 1080 video resolution and a bitrate of 11 Mbps. VI. R ESULT D ESCRIPTION A. Evolution over Time Different configurations have been simulated. The progress of the admitted sessions and the bandwidth aggregate over time are illustrated in Figure 3 for a PCN system without an additional buffering step and Figure 6 and 7 for a buffering mechanism approach using bandwidth metering and the autonomic rate adaptation algorithm with a different configured buffer size.
TR wbuf f er = GR wbuf f er ∈ [0, 1] The resulting effect of this algorithm is straightforward. The bandwidth aggregate will be shaped due to the buffer queuing and weighted fair scheduling component to a bandwidth equals to the calculated threshold bandwidth. An occasional burst can occur depending on the size of the buffer. This is caused by draining the dropped packets queuing component with a reduced priority. If the length of this burstiness is not too high then the PCN admissable rate threshold and the calculated threshold rate will converge towards the same value.
Fig. 5. Simulated network topology for all experiments: the topology consists of 1 congestion point at the Service Router.
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Fig. 6. Aggregate bandwidth and the number of admitted sessions for the buffering mechanism approach using bandwidth metering and the autonomic rate adaptation algorithm using an overal buffer size of 400000. This small buffer size results in occasional traffic bursts. Total admitted sessions of 84 due to over-admittance (flash crowd).
The bandwidth metering admission control mechanism used a measurement window of 100 milliseconds. The goal rate was set to 500 Mbps, CLE weight to 0.9 and the request rate at 10 request per second. The bandwidht metering admission control mechanism is able to to detect the pre-congestion state leading to blocked sessions when bandwidth metering measurements are above the predefined goal rate. The admissible rate varies over time due to the autonomic rate adaptation algorithm. The buffering mechanism in Figure 6 and Figure 7 uses an overal buffer size, in number of packets, of 400000 and 800000. Initially the threshold rate is defined as 475 Mbps, using the formulas discussed in Section IV-B, which varies over time due to the admissible threshold rate variation. Dropped packets are being served to the scheduler with a weight of 0.05 and the accepted packets with a corresponding weight of 0.95. The first 10 seconds still show some bursty traffic due to the start-up phase of the network. In Figure 3 the variability of the aggregate is very high. This results in a lot of bandwidth headroom which can not be used due to the high aggregate variability. Figure 6 illustrates the evolution of the bandwidth aggregate and admitted session with a small buffer size. After this start-up phase the aggregate bandwidth averages around 475 Mpbs threshold rate and is quite stable. The small buffer size introduces bursts at 34 seconds. These bursts are caused by an overflow of the buffer allowing more dropped packets. These packets are then transmitted to the dropped packets queing component with a lower weight. The aggregate drops and allows more sessions which causes a buffer overflow. This cycle of over-admittance (flash crowd) causes an enormous number of dropped packets and delay. Increasing the size of the buffer, detailed in Figure 7, to 800000 disposes occasional bursts and increases the admitted sessions. Taking the start-up phase not into account, it is
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Fig. 7. Aggregate bandwidth and the number of admitted sessions for the buffering mechanism approach using bandwidth metering and the autonomic rate adaptation algorithm using an overal buffer size of 800000. Total admitted sessions of 62. TABLE I T HE NUMBER OF ADMITTED SESSIONS FOR A TRADITIONAL PCN AND A PCN SYSTEM USING B UFFERING
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clearly visible that there are no bursts above the threshold rate. Furthermore, the admittance decision is also stable, no sessions are allowed anymore when the total bandwidth reaches the calculated threshold rate. B. Gain by the Algorithm When comparing a traditional PCN system with autonomic rate adaptation with a PCN system using our proposed buffering mechanism we observe that the buffering mechanism is able to achieve a maximization of the resources utilisation. As illustrated in Table I, combining the buffering mechanism with PCN allows to admit 62 high definition video sessions setting the buffer size to 800000 packets. This is a lot higher than the 49 sessions, admitted by the traditional PCN system. Both PCN systems where configured with a goal rate of 500 Mbps. This indicates that the under-utilisation of the available resource in a traditional PCN system can be converted. PCN’s additional buffering step increases the amount of admitted sessions by 26.5% for this simulated scenario. C. Impact of the Buffer Size With the intention of validating this novel buffering mechanism we investigate the decreasing variability of the network aggregate bandwidth. In Figure 8, the impact of the size of the buffer on the aggregate variability is shown for the buffering mechnism. Obviously, this variability will have an impact on the overall performance of the PCN configuration. These experiments were conducted using the same parameters for the
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Fig. 9. Impact of increasing buffering threshold rate with a static admissible rate of 450 Mbps on the admitted sessions and delay at the playout buffer. The overal buffer size was set to 800000.
D. Impact of the Threshold Rate bandwidth metering and buffering mechnism as previous test but with a varying buffer size expressed in number of packets without taking the start-up phase into account. The variability values in Figure 8 are the minimum and maximum aggregate bandwidth over a timeframe of 5 seconds. Figure 8 shows how an increasing buffer size causes decreasing aggregate variability. This is obvious behaviour since a higher buffer size can accept more packets and increase the aggregate variability then a low buffer size which has a increasing effect on the number of admitted sessions. This aggregated variability caused by a traditional PCN system is shown in Figure 8 with a zero buffer size. Due to the increasing buffer size we decrease the amount of dropped packets which are transmitted to the scheduler component. This denotes the decrease of the aggregate variability. When we take a closer look at Figure 8 we notice that there is a huge difference in aggregate variability when comparing a buffer size of 0, where variability is very high to a buffer size of 750000 with a variability close to zero. The drop of variability when the buffer size reaches 750000 is can be attributed to the decline of bursts. This figure thus indicates that a complete shaping of the bursty traffic entering the PCN domain is possible, but that configuration of the buffer size plays an important role in the experienced shaping and enables increasing PCN’s configured admissible rate thanks to the autonomic rate adaptation algorithm. Care is needed when configuring the size of the buffer, if the buffer size is too high, the buffer while introduce a large delay at the playout buffer. Considering the Quality of Experience (QoE) we need to find the right balance between buffer size and delay at the playout buffer. The packet size of the video traffic is 1024 bits, lowering the packet size of the traffic would also decrease the introduced delay. Table II shows the theoretical and simulated delay at the playout buffer. This is the startup delay the client will experience.
Figure 9 shows the impact of an increasing buffering threshold rate (TR) applying a static admissible rate of 450 Mbps on the admitted sessions and delay at the playout buffer using the bandwidth metering admission control mechanism with a static admissible rate. We performed 20 iterations of each experiment. The bandwidth metering uses a measurement window of 100 milliseconds. The bandwidth metering admissible rate threshold was set to 450 Mbps, the goal rate to 500 Mbps, CLE weight to 0.9 and the request rate at 10 request per second. The buffer size for this experiment was set to 800000. Instead of dynamically adapting the threshold rate based on the admissible rate and the goal rate as detailed in Section IVB, we increased the threshold rate starting from 450 Mbps. A threshold rate lower than the admissible rate obviously would never allow the rate at which the video sessions are transmitted to go above the admissible rate leading to a uncongested network. This infers that streams would never be blocked. The number of admitted flows are increasing, detailed in Figure 9, until a threshold rate of 460 Mbps is reached. This effect was already extensively discussed in Section VI-B. Increasing the threshold rate even more allows a decrease of the delay at the playout buffer. Due to the higher threshold rate, less packets are dropped and transmitted towards the clients with a lower weight which results in a lower delay at the playout buffer. TABLE II T HEORETICAL AND SIMULATED DELAY AS A FUNCTION OF THE BUFFER SIZE WITH A PACKET SIZE OF 1024 BITS Buffer Size
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VII. C ONCLUSIONS In this paper we evaluated the performance of a PCN based admission control system for protecting the transmission of video services, which are bursty in nature. We designed based on previous research a novel buffering mechanism to reduce the aggregate variability which was still present. Furthermore, we compared this buffering mechanism with a traditional PCN system using bandwidth metering and an autonomic rate adaptation algorithm. We obtained results through simulations which provide information about network utilisation, the introduced delay at the playout buffer and admitted sessions. The buffering mechanism’s main goal is to decrease the aggregate variability when the network is almost congested and increasing the number of admitted video sessions. In order to increase the usable bandwidth, we applied a packet shaping technique using fair weighted scheduling. The buffering mechanism adjusts automatically the buffer threshold rate based on measured admissible rate and goal rate in the network. Extensive evaluation showed that this buffer mechanism provides a better utilisation of the resource and assures no congestion in the network. The impact of the buffer size and calculated threshold rate on the delay at the playout buffer and admitted sessions are evaluated. Solutions to increase this delay are proposed. In future work, we are investigating different buffering mechanisms and their impact on the delay at the playout buffer. For example, by introducing different scheduling methods. Moreover, we are investigating the impact on the admitted sessions combining the proposed buffering mechanism and the scalable video coding concept in PCN. ACKNOWLEDGMENT Steven Latr´e and Tim Wauters are funded by Ph.D grant of the Fund for Scientific Research, Flanders (FWO-V). The research leading to these results has received funding from the European Unions Seventh Framework Programme ([FP7/2007-
2013]) under grant agreement n 248775 as part of the FP7 STREP project OCEAN. R EFERENCES [1] P. Eardly, ”Pre-Congestion Notification (PCN) Architecture (draft-ietfpcn-architecture-10”, [online] http://tools.ietf.org/html/draft-ietf-pcnarchitecture-10, 2010. [2] ”Congestion and Pre-Congestion Notification (PCN)”, [online] http://datatracker.ietf.org/wg/pcn/, 2010. [3] ”NS-2, The Network Simulator”, [online] http://www.isi.edu/nsnam/ns/, 2010. [4] S. Latr´e, B. De Vleeschauwer, W. Van de Meerssche, F. De Turck, P. Demeester ”PCN Based Admission Control for Autonomic Video Quality Differentiation: Design and Evaluation”, Journal of Network and Systems Management, 2010. [5] S. Latr´e, B. De Vleeschauwer, W. Van de Meerssche, F. De Turck, P. Demeester ”Design and Configuration of PCN Based Admission Control in Multimedia Aggregation Network”, in Proceedings of IEEE Globalcom, 2009. [6] S. Latr´e, B. De Vleeschauwer, W. Van de Meerssche, K. De Schepper, C. Hublet, W. Van Leekwijck, F. De Turck, ”An autonomic PCN based admission control mechanism for video services in access networks”, in Proceedings of IEEE International Symposium on Integrated Network Management, 2009. [7] M. Menth, F. Lehrieder, ”Performance Evaluation of PCN-Based Admission Control”, in Proceedings of Quality of Service, 2008. IWQoS 2008. 16th International Workshop on , 2008, pp 110-120. [8] M. Menth, M. Hartman, ”Threshold configuration and routing optimization for PCN-based resilient admission control”, Journal of Computer Networks, 2009. [9] ETSI TS 182 019, ”Resource and Admission Control Sub-stystem (RACS); Function Architecture”. [10] K. Nichols, V. Jacobson, L. Zhang, A Two-bit Differentiated Services Architecture for the Internet”, RFC2638 (Informational), 1999. [11] R. Braden, D. Clark, S. Shenker, Integrated Services in the Internet Architecture: an Overviev, RFC 1633 (Informational), 1994. [12] P. Yuan, J. Schlembach, A. Skoe, E. Knightly, Design and implementation of scalable edge-based admission control, Computer Networks, 2001. [13] A. Eleftheriadis, P. Batra, Constrained and General Dynamic Rate Shaping of compressed Digital Video, in Proceedings of 2nd IEEE International Conference on Image Processing, 1995, pp 396-399. [14] A. Eleftheriadis, P. Batra, Meeting arbitrary QoS constraints using Dynamic Rate Shaping of coded digital video, in Proceedings of 5th International Workshop on Network and Operating System Support for Digital Audio and Video, 1995, pp 95-106.
