Changbin Liu, Lei Shi, Bin Liu Department of Computer Science and Technology, Tsinghua University Proceedings of the Fourth European Conference on Universal Multiservice Networks (ECUMN’07) Chen Bin Kuo ( ) Young J. Won ( )

DPNM Lab.  Introduction  NGN traffic classifications and their utility functions  Network utility maximization (NUM)  Numeric results and analysis  Discussion  Conclusion 10/3/20152

DPNM Lab.  Next generation network (NGN) must natively support triple-plays.  How to schedule traffic and allocate bandwidth at both backbone and access links.  Designing a scheduling (bandwidth allocation) algorithm is exactly the issue this paper tries to settle. 10/3/20153

DPNM Lab.  In industry designing NGN [13][14], the strict- priority scheduling is mostly adopted.  Rigidly favors the voice and video traffic without flexibility  Utility-based solutions  Shenker [1] discussed traffic classifications in IP network from the viewpoint of user utility  Kelly et al. [5][6] applying utility-based methods to scheduling and bandwidth allocation in the objective of Network Utility Maximization (NUM) 10/3/20154

DPNM Lab.  No single work has emphasized on the practical issue of scheduling triple-play services under the background of NGN.  Translating this issue into a nonlinear maximization problem with inequality constraints. 10/3/20155

a) VoIP traffic b) IPTV traffic c) TCP elastic traffic d) HTTP traffic e) Other UDP traffic 10/3/20156

DPNM Lab.  Due to remarkable distinction of QoS requirements in NGN  Classifying NGN traffic into five categories  User utility function is introduced  To measure network performance and user satisfaction degree  Determined by the QoS metrics received in the user end  Including packet delay, jitter and loss rate 10/3/20157

DPNM Lab.  Sensitive to packet delay and loss caused by bandwidth insufficiency  Utility function falls into the category of hard real-time kind [1][2][10], with a minimal bandwidth requirement of Bmin1 10/3/20158

DPNM Lab.  Utility function is similar to VoIP’s but with some differences  Tolerate occasional delay-bound violations and packet drops  Minimal encoding rate, denoted as Bmin2 is independent of network congestion  Logistic model is used Logistic model 10/3/20159

DPNM Lab.  Generated by delay-tolerant TCP applications  Such as file transfer and  Utility function have been studied by Kelly et al. [6] and other researchers [11][12] 10/3/201510

DPNM Lab.  TCP traffic which concerns packet delay  Mainly contains the HTTP traffic generated by web services  Utility function is different from TCP elastic traffic, has a minimum tolerable bandwidth Bmin4 10/3/201511

DPNM Lab.  DNS packets, other streaming media traffic, and on- line gaming traffic [17][18]  Delay-sensitive  Every application type has a utility function  The shape of utility function resembles IPTV traffic 10/3/201512

DPNM Lab. 10/3/ VoIPIPTVTCP elasticHTTPUDP B min 64Kbps100 Kbps24Kbps B max 10Mbps 500Kbps ɛ0.001

a) KKT method b) Lagrange multipliers method without KKT conditions 10/3/201514

DPNM Lab.  Based on NGN traffic’s utility functions, we can solve the congestion-phased bandwidth allocation issue while conforming to NUM.  Total utility gained on the link is:  Bandwidth allocation is restricted by: 10/3/ N : the number of NGN users utilizing this link p i : traffics classes C : the bandwidth of a link (set to 10Gbps) N : the number of NGN users utilizing this link p i : traffics classes C : the bandwidth of a link (set to 10Gbps)

DPNM Lab.  Lagrange Multiplier method with KKT (Karush- Kuhn-Tucker) conditionsKKT (Karush- Kuhn-Tucker) conditions  Solving the nonlinear optimization problem  Accurate and comprehensive solution requires substantial complicated computations  Applying simplified form which is enough to ravel NUM problem for triple-plays 10/3/201516

DPNM Lab.  Observing NGN traffic’s utility functions  VoIP/IPTV/other UDP traffic’s utility functions are relatively smoother in some points  It is not cost-effective to allocate bandwidth to VoIP/IPTV/other UDP traffic without booming the utility  Turning point (TP) 10/3/ Bandwidth IPTV HTTP

DPNM Lab.  After finding the TP, we can apply the Lagrange Multipliers method without KKT conditions to solve the NUM problem in (10)Lagrange Multipliers method  Subject to: 10/3/201518

a) Data-dominated network b) IPTV-dominated network 10/3/201519

DPNM Lab.  Two network scenarios  Current Internet, where HTTP and TCP elastic traffic still dominate the volume  Prospective NGN, where the emerging services, especially the IPTV traffic, will dominate the network  For each scenario, calculate in two situations  Maximal Utility Equalization (MUE)  Maximal Utility In-equalization (MUI) 10/3/ V1 (VoIP)V2 (IPTV)V3 (TCP elastic)V4 (HTTP)V5 (other UDP) MUE11111 MUI

DPNM Lab.  Data-dominated network  According to recent trace observation [15]  IPTV-dominated network 10/3/ VoIPIPTVTCP elasticHTTPother UDP Traffic proportions 10% 50%20% VoIPIPTVTCP elasticHTTPother UDP Traffic proportions 10%50%10%20%10%

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DPNM Lab.  Previous bandwidth allocation schemes for triple- play services mostly adopt the strict-priority scheduling  Highest priority to VoIP traffic  Second highest priority to IPTV and lowest priority to others  In this paper  Highest priority to VoIP traffic  Assigning IPTV traffic with second-highest priority is not well supported from the objective of NUM  Suggesting that ISP charges more about IPTV services (future work) 10/3/201524

DPNM Lab.  Studied the problem of scheduling and bandwidth allocation for triple-play services in the objective of NUM.  Presenting theoretical method to compute bandwidth allocation results  Results:  VoIP and other low-throughput UDP traffic can always be guaranteed of sufficient bandwidth  As congestion becomes severer, IPTV’s bandwidth decreasing quickly  TCP elastic and HTTP traffic experience exponential bandwidth degradations when congestion degree increases 10/3/201525

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