Scalable and Adaptive QoS Mapping Control Framework for Packet

Scalable and Adaptive QoS Mapping Control Framework for Packet Video Delivery Gooyoun Hwang1 , Jitae Shin2 , and JongWon Kim1 1

Networked Media Lab., Department of Information and Communications, Gwangju Institute of Science and Technology (GIST), Gwangju, 500-712, Korea {gyhwang, jongwon}@netmedia.gist.ac.kr 2 School of Information and Communication Engineering, Sungkyunkwan Univ., Suwon, 440-746, Korea {[email protected]}

Abstract. With the exploding volume of traffic and expanding Quality of Service (QoS) requirements from emerging multimedia applications, many research efforts have been carried out to establish multi-class network service model in next-generation Internet. To successfully support multiple classes of service, network resources must be managed effectively to ensure end-to-end QoS while simultaneously sustaining stable network QoS. First, we present a scalable and adaptive QoS mapping control (SAQM) framework over the differentiated services network focusing reactive edge-to-edge QoS control in class-based. Secondly, under SAQM framework, end-to-end QoS control for per-flow service guarantee is proposed through incorporating relative priority index (RPI)-based video streaming and a special access node called media gateway (MG) at network edge. The SAQM framework is composed of the functionalities of proactive and reactive QoS mapping controls to provide reliable and consistent service guarantee. In our framework, edge-to-edge active monitoring is utilized to obtain measures reflecting each class performance and then measurement-based reactive mapping control for relative network QoS provisioning is performed at MG located in the ingress edges. Simulation results demonstrate the feasibility of an edge-based QoS control and show how to enhance the QoS performance of video streaming in proposed SAQM framework.

1

Introduction

The explosively increased capacity of packet switched networks makes it feasible to support new applications, such as IP telephony, video-conferencing, and online TV broadcast. Meanwhile, the volume of traditional Best Effort (BE) data traffic continues to grow with web applications and I/O-intensive scientific applications. To meet these diverse application needs, the Internet is evolving from a network that provides a single BE service to a network that supports multiple classes of services. Tremendous effort has been devoted to defining new service models that deliver better end-to-end Quality of Service (QoS) than current BE service Y.-S. Ho and H.J. Kim (Eds.): PCM 2005, Part II, LNCS 3768, pp. 465–476, 2005. c Springer-Verlag Berlin Heidelberg 2005


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can deliver. The differentiated services (DiffServ, or DS) model [1] has been proposed as a scalable and manageable architecture for network QoS provisioning. There are two basic types of service differentiation approaches: premium service (PS) [2] and assured service (AS) [3]. Especially, the assured service provides a relative service differentiation among DS classes, in the sense that high-priority classes receives a better or at least not worse service than low-priority ones. In this context, applications do not get an absolute service quality assurance. So, it is up to the applications to select appropriate QoS levels that best meet their requirements, cost, and device constraints. Since most of recent multimedia applications have become more and more resilient to occasional packet loss and delay fluctuation, the AS of DS networks seems a more attractive choice due to its simplicity and flexibility. However, unstable network service situations caused by instantaneous class load fluctuations still occur even though the resource provisioning policy of underlying network is strictly managed. The unstable situations obstruct multimedia applications to achieve their desired service requirements persistently. Since the end-to-end QoS perceived by the applications mainly depend on the current network load condition, the applications should response to this fluctuation in a proper way. Accordingly, an adaptive QoS mapping1 mechanism considering the network situation is required to support the end-to-end QoS guarantee dynamically to end systems and to improve the network utilization. From this point of view, we present a scalable and adaptive QoS mapping control (shortly, SAQM) framework, which consists of proactive QoS mapping control and reactive QoS mapping control in network class/flow-based granularity, over the DS domain even for packet video delivery. In the SAQM framework, edge-to-edge active monitoring is utilized to obtain measures reflecting each class behavior and then measurement-based reactive mapping control for relative network QoS provisioning is performed at the ingress edges of the network. Finally, we propose a reactive end-to-end QoS mapping control to enhance service differentiation-aware video streaming. With the help of network feedback, the end-host video application can recognize the status of network classes and can now react in advance. By leveraging the end-to-end feedback (i.e., in tie with the required congestion control), the feedback is relayed to the sender in a similar way as the explicit congestion notification (ECN) [4]. The network feedbackbased QoS control triggers the QoS mapping adjustment at the boundary node of an access domain. The network simulation based on NS-2 is performed to demonstrate the enhanced performance of the proposed QoS mapping control framework (i.e., more efficient and adaptive to network variation). The remainder of this paper is organized as follows. Section 2 presents overview of the scalable and adaptive QoS mapping framework with video delivery scenario. In section 3, we propose a reactive edge-to-edge QoS mapping control for stable class-based service differentiation and a reactive end-to-end 1

The issue of QoS mapping occurs when we map prioritized and classified groups of some applications(or users or flows) based on their importance into different DS levels or network classes.

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QoS control to enhance relative differentiation-aware video streaming. Various sets of performance evaluation through computer simulations are presented in section 4. Finally, section 5 concludes this paper.

2 2.1

The SAQM Framework Overview of the SAQM Framework

As stated in section 1, the network QoS of a DS domain may be time-varying due to instantaneous class load fluctuation. It is clear that this QoS variation results in network service violation and also exerts bad influence on the endto-end QoS guarantee. To ensure the overall QoS, we present a scalable and adaptive QoS mapping control framework in the DiffServ network. The SAQM framework can be divided into three QoS controls : 1) proactive QoS control for initially managing traffic aggregates at the entrance of the network, 2) reactive edge-to-edge QoS control based class-based active monitoring for stable network service provisioning, and 3) reactive end-to-end QoS control for flow-based service guarantee.

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Fig. 1. The scalable and adaptive QoS mapping framework

As shown in Fig. 1, we assume that there is several ingress points and only one egress point in the DiffServ network. For class-based QoS monitoring between ingress-egress pair, we insert probe packets into the data stream of each class periodically at an ingress edge router (ER). The egress ER is responsible for classifying the probe packets and measuring the QoS metrics of each class, such as edge-to-edge delay and packet loss rate. Then the egress ER notifies the measured information to the corresponding ingress ER. Based on the feedback analysis, the ingress ER decides whether to perform a reactive QoS mapping adjustment or not. This reactive edge-to-edge QoS control (shortly, EG2EG control) process can build a control loop between ingress-egress pair. The detailed description of

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the EG2EG control will be presented in section 3.1. Finally, a proper networkaware feedback control (i.e., reactive end-to-end QoS control), which can give the applications a guidance of network status, enables the fine-tuned refinement on top of coarse edge-to-edge QoS control. For this, a network-adaptive QoS control for packet video streaming will be presented in section 3.2. 2.2

Video Delivery Scenario Under the SAQM Framework

For the end-to-end video streaming, sources within the access network send videos to the corresponding clients. The access network subscribes to DS services, and traffic is delivered to the clients through the DS domain. The access network has SLAs [1] with the DS domain. In this framework, the video applications at the source assign a relative priority-based index (RPI) to each packet in terms of loss probability and delay as studied in [5] so that each packet can reflect its influence to the end-to-end video quality. The source then furnishes each packet with this prioritization information for a special boundary node called media gateway (MG) as shown in Fig. 2. Thus, the video streams from the sources are merged at the MG. In order to prevent the sources from violating their SLAs and protect resources from a selfish source, the MG exercises the traffic shaping on a per-flow basis through the token buckets (TBs) assigned for individual flows, as seen in Fig. 2. The packets violating this agreement are assigned with the lowest DS class (i.e., best-effort class). The main function of the MG is to make a cost-efficient coordination between the prioritized packets (or flows) and the DS service classes, which we call QoS mapping. For the optimal QoS mapping, we just refer [5] and mention briefly due to page limitation. That is, for packets conforming to the TB, the MG assigns to each packet a DS codepoint (DSCP) on the basis of the packet’s RPI. Then, the MG forwards the packet streams to the ER at the ingress of the DS network. The ER is composed of an aggregate traffic conditioner (ATC) and a packet forwarding mechanism [6]. The ATC is employed to observe the traffic conditioning agreement (TCA) with the DS domain and the packet forward mechanism provides proportionally relative QoS spacing between network service

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classes by using a multiple random early detection (MRED) queue and adaptive WFQ. In order to support the relative service differentiation, the DS domain provides three assured forwarding (AF) classes and each class queue has three drop precedences [3]. That is, the order of DS level, or DSCP, from high to low is {AF11 , AF12 , AF13 ; AF21 , AF22 , AF23 ; AF31 , AF32 , AF33 ; BE}. Hence, the proactive QoS control is realized as discussed.

3

Reactive QoS Controls in the SAQM Framework

This section presents two types of reactive QoS mapping controls in the SAQM framework. One is an edge-based QoS control (i.e., the EG2EG control) proceeded inside the DS network domain and another is a flow-based QoS control performed at the boundary node, i.e., the MG, of an access domain. The latter is specialized for source-marked video streaming and is activated by user’s request. 3.1

The EG2EG Control

The EG2EG control relies on class-based QoS monitoring to obtain adequate information of each class behavior and performance so that a reactive QoS mapping adjustment can be performed. In our framework, the monitoring scheme measures delay and packet loss on per-class basis and then compares these measurements to the predefined values in the SLA. The combined monitoring of delay and packet loss information can be used for reliable estimation of network condition. Note that delay is defined as the edge-to-edge latency and packet loss rate is defined as the ratio of total number of packets dropped from the aggregated flows mapped into a class in the domain to the total number of packets belonged to the same class entering into the domain [7]. Function blocks of the EG2EG control between ingress-egress pair are illustrated in Fig. 3. The probe packet generation and insertion module encodes the following states into probe packet inserted in the network: [Ingress ID; Class ID; Sequence number; Timestamp]

Ingress ER

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Fig. 3. Building blocks of the EG2EG control

Pacekt loss estimation

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The ingress and class identifier will allow the EG2EG control on a per-path, per-class (i.e., ingress-egress pair) basis. The sequence number is used to identify loss on the path at the egress ER. To measure delay, the probe packets should also contain their current timestamps. Assume that all router clocks in a domain are synchronized fairly accurately. The probing frequency is important because it determines the overhead of the monitoring process. In our simulations, we found that the overhead of about 10% of the total bandwidth is adequate to monitor the network resources. The class-based QoS monitoring module at the egress ER classifies the probe packets as belonging to a class i of an ingress ER j, and then ˜ i at time t using an exponentially weighted ˜ i and packet loss L measures delay D j j moving average (EWMA) according to the following equations respectively: ˜ i (t) = (1 − α) ∗ D ˜ i (t − 1) + α ∗ Di (t), D j j j

(1)

˜ i (t) = (1 − β) ∗ L ˜ i (t − 1) + β ∗ Li (t), L j j j

(2)

where 0 < α < 1 and 0 < β < 1 are smoothing factors. Then the monitoring module feeds the measurements back to the edge-to-edge QoS control module at the corresponding ingress ER.

Parameter Definition: ˆ i : the delay guarantee of class i; D ˆ Li : the packet loss guarantee of class i; γ, δ, κ, ϕ: utility factors; DP i : downgrading probability in the ATC; Reactive Edge-to-edge QoS Control algorithm: ˆ i ) then ˜i > γ ∗ D if (D i ˆ i ) then ˜ if (L > δ ∗ L ˆi i DP ← DP i + κ ∗ (1 − L ˜i ) L else ˆi DP i ← DP i + ϕ ∗ (1 − D ˜i ) D else BREAK;

Fig. 4. The EG2EG control algorithm

With the feedback information, the EG2EG control module firstly analyzes delay measurements. It is because the sudden increase of delay can be considered as a precursor of service violation (or network congestion) before occurring packet losses. If the measured delay value exceeds the predefined delay guarantee applying a utility factor, packet loss measurements are analyzed in the same way to evaluate the conditions of network classes and set a reactive QoS mapping adjustment in a per-class basis. For the QoS mapping adjustment, the incoming

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packets entering into the corresponding class queue are randomly re-directed by the ATC to a lower class queue with an updated downgrading probability. When a packet is re-directed to a lower AF class, the drop precedence is set to the highest priority in that lower class. By doing so, we believe that the EG2EG control can help to maintain the quality spacing between network classes independent of the class load variations. The EG2EG control algorithm is described in Fig. 4. The operation of the algorithm is affected by the above utility factors. The determination of the factors is difficult due to their trade-off properties and mainly depends on network environment. The specific factor values for a specific network environment can be obtained through a number of simulations. For this sensitivity study, we have performed a number of simulations but we do not present in this paper due to page limitation. Usually, the factors must be relatively insensitive to minor changes in network characteristics. 3.2

Network-Adaptive QoS Control for Video Streaming

A proper combination of congestion signalling from network and source reaction will be an effective solution to the instantaneous network fluctuation in the Internet. The major idea behind our end-to-end feedback control is employing ECN mechanism in conjunction with the proactive QoS control at the MG. It is possible that not only the congestion status of network class is notified to the end-host video applications but also a reactive flow-based QoS mapping control is triggered in a faster manner. Fig. 5 shows the outline of the proposed network-adaptive QoS control under the SAQM framework. When a DS class queue of ECN-MRED router exceeds an predefined threshold, the router sets the CE bit of the packets to indicate the onset of congestion to the end nodes [4]. An ECN-aware receiver monitors the CE bit on each packet arrival i of a flow and calculates the received average f cost of the flow C recv using the following equation: f C recv


N f =

i=1

Cq(i)

Nf

,

(3)

where N f and Cq are total received packet numbers of the flow f and unit cost of DS level q specified in the SLA, respectively. This observed value is considered as a barometer to interpret the degree of service degradation which the receiver experiences. Upon the receipt of an ECN-marked packet, the receiver f immediately sends a 2-tuple {˜ q, C recv } feedback report to the MG through the corresponding sender. Based on this feedback information, the MG regards q˜ f as a congested class and compares C recv with the requested average payment f C send 2 . The MG adjusts the initial mapping of source category k to DS level q based on the comparison. That is, k involved in the reported congestion class q˜ is 2

f

C send is computed by the Eq. (3) in case that video packets of a flow are initially sent.

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Fig. 5. The network-adaptive QoS control f

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re-mapped to higher non-congested classes, for C recv ≤ C send . Otherwise, k is remapped to lower non-congested ones. The reactive QoS mapping is summarized as follows: • DS level: Let q be a DS level, where 1 ≤ q ≤ Q with the increasing order of network service quality and Q is the total number of DS levels. • RPI partitioning: Let Rqk (i) be a partition i among the RPI k categories and be assigned into DS level q. Each k category has equal number of packets initially and is sorted in an increasing order, that is, 1 ≤ k ≤ K, where K is the category with highest RPI values. Generally, the packets within the same k could be assigned into different q levels. • Proactive mapping: Each packet, whose RPI is k (k ∈ Rq ), is mapped to f

q in an optimal way while satifying the requested C send . • Reactive mapping: When a congestion feedback, i.e., ECN, from a class q˜ is received, Rqk (i) is distributed into Sq (t) subset, where Sq (t) is the number of non-congested DS levels at time t and 1 ≤ j ≤ Sq (t), which are higher f

f

levels than the level q for C recv ≤ C send . Then, the packets belonged to Rqk (i) are re-mapped to DS level j. This reactive control is operated in time of [tACK , tACK + ∆], where tACK and ∆ denote the time of receiving feedback packet and a certain time interval, respectively. The arrivals of the subsequent feedbacks with the same q˜ are ignored during ∆. After expiration of tACK + ∆, the MG returns back to the normal state.

4

Simulation Results

In this section, we investigate the feasibility of the proposed EG2EG control by comparison with the proactive approach (PQ-only) having no reactive QoS mapping mechanism. Next, we evaluate the effectiveness of our network feedbackbased QoS control (PQ-NBF) in the end-to-end video streaming.

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Simulation Specification

The simulation topology model is shown in Fig. 6, which is used to generate underlying network dynamics. Each source connected with ER1 and ER2 generates a mix of CBR and On/Off UDP flows. The sending rates of the flows are varied according to the desired network load level. One test video source is also connected to the DS network through the MG and communicates with one destination nodes. The link capacities and delays between the routers are shown in the links of Fig. 6. As described in section 2.2, the ER implements three major components for managing traffic aggregates: the interconnected trTCM3 , the MRED, and adaptive WFQ. For our simulations, the ER uses the MRED with the values of [50, 70, 0.01] for AFx1 , [30, 50, 0.02] for AFx2 , and [10, 30, 0.1] for AFx3 [8]. The weighting factors of the WFQ for class queues are set as AF 1 : AF 2 : AF 3 : BE = 4 : 3 : 2 : 1. We also set the trTCM’s peak rate to be equal to the bandwidth assigned by the WFQ. .

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4.2

Feasibility of the EG2EG Control

To show the impact of the EG2EG control in the framework, an unbalanced input load is considered. Three input load levels are allocated to the AF classes: AF 1 : AF 2 : AF 3 = 120% : 110% : 103%. For all the flows assigned with the AF class, the drop precedence is set to AFx1 . To accurately evaluate the QoS performance of the classes between ingress-egress pair, we observe all data packets traversing on the path. The performance results in Table 1 show that the EG2EG control can detect network condition in per-class basis and keep interclass service order. That is, the EG2EG control is valuable for providing stable class-based differentiation compared to PQ-only. As discussed in section 3.1, the efficiency of the EG2EG control depends on the utility factor values. For instance, if κ and η are set to relatively high value, the class remapping process is occurred frequently. It results in overall system instability. Accordingly, careful choice of the factor values should be required. The sensitivity analysis of the factors is under our further investigation. In the simulation, we select the following values for the EG2EG control: {γ, δ, κ, η} = {0.70, 0.85, 0.45, 0.30}. 3

Please refer [6] for the detailed description of the inter-connected trTCM.

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G. Hwang, J. Shin, and J. Kim Table 1. Performance evaluation of the EG2EG QoS control PQ-only EG2EG Throughput Packet loss Delay Throughput Packet loss Delay (kbps) (%) (msec) (kbps) (%) (msec) AF1 AF2 AF3 BE

4.3

936.1 817.4 586 131

22.68 17.71 3.60 6.41

85.4 74.2 91.7 110.9

1049.3 955.2 441.5 75.1

3.67 6.53 27.38 64.78

57.1 64.3 87.6 118.0

Performance Evaluation of the NBF Control for Video Delviery

In this section, the main objective of the experiments is to investigate the effectiveness of PQ-NBF. To make a congestion period, the sending rates of AF2x sources and AF3x sources are adjusted in runtime from 10 to 17 (sec) and from 17 to 20 (sec) alternately so that it corresponds to total provision level of 120%. For the sake of simplicity, the range of unit cost per packet in PQ-NBF is 9 to 0 and is associated with the same order of DS level. The overall simulation setup is illustrated in Fig. 7. The standard-based H.263+ encoding/decoding is used to evaluate the end-to-end video performance. While H.263+ encoder encodes video, mean-square-error (MSE) value of each group of block (GOB) is calculated and stored as a data file. Since each GOB is packetized into a separate packet in the simulation, priority will be assigned to each packet according to the relative loss importance of payload. Error patterns generated from results by the NS-2 simulations are used to decide whether the packet is lost or not. Then, at the receiver side, encoded bitstream is decoded with the error pattern file. Consequently, peak signal to noise ratio (PSNR) between original and reconstructed video is calculated to quantify how much video quality is degraded by the packet loss during transmission.

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Fig. 7. Simulation diagram for H.263+ streaming video over a simulated DS network

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Table 2. Performance evaluation of PQ-NBF End-to-end QoS Parameters Throughput Packet loss Delay (kbps) (%) (msec) PQ-only PQ-NBF

391.446 406.518

7.672 1.387

73.608 61.727

PSNR (dB) 29.797 33.378

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Fig. 8. PSNR performance comparison and achieved average PSNR at different traffic loads

Table 2 presents the end-to-end performance results of PQ-only and PQNBF. It represents that the proposed feedback control effectively responses to the network congestion and has competitive advantage for video streaming. To examine the perceptual quality of the H.263+ video, we play out the decoded video sequence at the receiver and measure the PSNR as an objective quality metric. Note that the average original PSNR for the video trace is about 34.76 dB (i.e., ideal case). In the simulations, we do not differently use the reference QoS mapping between categorized packets and DS levels in order to get a fair comparison. Fig. 8(a) and Fig. 8(b) present PSNR performance comparison of different QoS controls and achieved average PNSR at different traffic loads . The objective PSNR quality measure of PQ-NBF is better than that of PQ-only over various under-provisioned situations.

5

Conclusion

In this paper, we introduced the scalable and adaptive QoS mapping control (SAQM) framework. On this framework, we proposed two types of reactive QoS controls: an edge-based QoS control for stable class-based service differentiation and a reactive end-to-end QoS control to enhance relative differentiation-aware video streaming. Simulation results verified the validity of the QoS controls. Further work would include the refinement of our framework and take care of the end-to-end video streaming over multiple DS domains.

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Acknowledgement This work was supported in part by the BK21 Program and in part by the Korea Institute of Industrial Technology Evaluation and Planning (ITEP) through the Incheon IT Promotion Agency.

References 1. S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, and W. Weiss, “An architecture for differentiated services,”, IETF RFC 2475, Dec. 1998. 2. K. Nichols, V. Jacobson, and L. Zhang, “A two-bit differentiated servicesarchitecture for the Internet,”, IETF RFC 2638, July 1999. 3. J. Heinanen, F. Baker, W. Weiss, and J. Wroclawski, “Assured forwarding PHB group,”, IETF RFC 2597, June 1999. 4. K. K. Ramakrishnan and S. Floyd, “A proposal to add explicit congestion notification (ECN) to IP,”, IETF RFC 2481, Jan. 1999. 5. J. Shin, J. Kim, and C.-C. J. Kuo, “Quality-of-Service mapping mechanism for packet video in differentiated services network,” IEEE Transaction on Multimedia, vol. 3, no. 2, pp. 219-231, June 2001. 6. J. Shin, “An analysis of aggregated traffic marking for multi-service networks,” IEICE Transactions on communications, vol. E86-B, no.2, pp. 682-689, Feb. 2003. 7. A. Habib, M. Khan, and B. Bhargava, “Edge-to-edge measurement-based distributed network monitoring,” Computer Communicatons vol. 44, pp. 211-233, Aug. 2004. 8. S. Floyd and V. Jacobson, “Random early detection gateway for congestion avoidance,” IEEE/ACM Transactions on Networking, vol. 1, no. 4, pp. 397-413, Aug. 1999.