Xin Wang and Henning Schulzrinne Internet Real -Time Laboratory Columbia University

Resource Negotiation and Pricing in DiffServ for Adaptive Multimedia Applications Xin Wang and Henning Schulzrinne Internet Real -Time Laboratory Columbia University http://www.cs.columbia.edu/~xinwang

Outline • Introduction • A Resource Negotiation And Pricing protocol: RNAP • Pricing models • User adaptation • Test-bed demonstration of Resource Negotiation Framework • Simulation and discussion of Resource Negotiation Framework • Conclusion and future work Resource Negotiation Framework Xin Wang, Henning Schulzrinne, Columbia University

Is Simple Over-Provisioning Enough? • Current Internet: • Growth of new IP services and applications with different bandwidth and quality of service requirements • Revenue from the traditional connectivity services is declining • New services present opportunities and challenges • Even though average bandwidth utilization is low, congestion can happen; access links get congested frequently • Wireless bandwidth is even more scarce • Bandwidth prices are not dropping rapidly • No intrinsic upper limit on bandwidth use Option - manage the existing bandwidth better, with a service model which uses bandwidth efficiently. Xin Wang, Henning Schulzrinne, Columbia University

A More Efficient Service Model • Quality of Service (QoS) • Condition the network to provide predictability to an application even during high user demand • Provide multiple levels of services • Problems: signaling to facilitate service negotiation; differential charging • Application adaptation • Source rate adaptation based on network conditions - congestion control and efficient bandwidth utilization • Problems: • How adaptive applications work with QoS-assured services? How to motivate an application to adapt? Xin Wang, Henning Schulzrinne, Columbia University

Design Goals • Develop an efficient service model which combines QoS assurance with user rate adaptation • Increase service value to the users through greater choices over price and quality, improved connectivity, and expected QoS • Reduce network provision complexity, improve network efficiency and increase revenue to the providers; allows network operator to create different trade-offs between blocking admissions and raising congestion prices Xin Wang, Henning Schulzrinne, Columbia University

Our Work • Proposea Resource Negotiation And Pricing protocol: RNAP • Allows for service predictability, multi-party negotiation • Designed to be scalable and reliable • Can be embedded in other protocols, or implemented independently • Enables differential charging for supporting differentiated services, reflecting the service cost and long-term user demand • Support short-term resource commitment for better response to user demand and network conditions, and more efficient resource usage; congestion pricing to motivate user adaptation • Develop reference user adaptation model • Demonstrate negotiation framework on test-bed network • Show significant advantages relative to static resource allocation and fixed pricing using simulations Xin Wang, Henning Schulzrinne, Columbia University

Protocol Architectures: Centralized(RNAP-C) Host Resource Negotiator RNAP Messages Network Resource Negotiator NRN NRN NRN HRN HRN Access Domain - A Edge Router Access Domain - B Internal Router Intra-domain messages Transit Domain Xin Wang, Henning Schulzrinne, Columbia University

Protocol Architectures: Distributed(RNAP-D) Local Resource Negotiator RNAP Messages HRN LRN LRN LRN LRN LRN LRN LRN LRN LRN HRN LRN LRN Access Domain - A LRN LRN Edge Router Access Domain - B Internal Router Transit Domain Xin Wang, Henning Schulzrinne, Columbia University

RNAP Messages Query: Inquires about available services, prices Query Quotation Quotation: Specifies service availability, accumulates service statistics, prices Reserve Commit Reserve: Requests services and resources, Modifies earlier requests Periodic negotiation Quotation Commit: Confirms the service request at a specific price or denies it. Reserve Commit Close: Tears down negotiation session Close Release: Releases the resources Release Xin Wang, Henning Schulzrinne, Columbia University

Turn off router alert Message Aggregation (RNAP-D) Turn on router alert Edge Routers Sink-tree-based aggregation Xin Wang, Henning Schulzrinne, Columbia University

Turn on router alert Message Aggregation (RNAP-D) Turn off router alert Sink-tree-based aggregation Xin Wang, Henning Schulzrinne, Columbia University

Block Negotiation (Network-Network) Aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics Bandwidth time Xin Wang, Henning Schulzrinne, Columbia University

Two Volume-based Pricing Strategies • Fixed-Price (FP): fixed unit volume price • During congestion: higher blocking rate ORhigher dropping rate and delay • Congestion-dependent-Price (CP): FP + congestion-sensitive price component • During congestion: users have options to maintain service by paying moreORreducing sending rateORswitching tolower service class • Overall reduced rate of service blocking, packet dropping and delay Xin Wang, Henning Schulzrinne, Columbia University

Proposed Pricing Strategies • Holding price and charge: based oncost of blocking other users by holding bandwidth even without sending data • phj = j (pu j - puj-1) , chij(n) = ph j r ij(n) j • Usage price and charge: maximize the provider’s profit, constrained by resource availability • max [Σl x j (pu1 , pu2 , …, puJ ) puj - f(C)], s.t. r (x (pu2 , pu2 , …, puJ)) R • cuij (n) = pu j v ij (n) • Congestion price and charge: drive demand to supply level • pcj (n) = min [{pcj (n-1) + j(Dj, Sj) x (Dj-Sj)/Sj,0 }+, pmaxj ] • ccij (n) = pc j v ij (n) Xin Wang, Henning Schulzrinne, Columbia University

Usage Price for Differentiated Service • Usage price based on cost of class bandwidth: • lower target load (higher QoS) -> higher per-unit bandwidth price • Parameters: • pbasicbasic rate for fully used bandwidth •  j : expected load ratio of class j • xij: effective bandwidthconsumption of application i • Aj : constant elasticity demand parameter • Price for class j: puj = pbasic / j • Demand of class j: xj ( puj ) = Aj /puj • Effective bandwidth consumption: xej ( puj ) = Aj / (puj j ) • Network maximizes profit: • max [Σl(Aj /pu j ) pu j - f (C)], puj = pbasic / j , s. t.ΣlAj / (pu j j ) C • Hence: pbasic =ΣlAj / C , puj = ΣlAj /(C j) Xin Wang, Henning Schulzrinne, Columbia University

Rate Adaptation of Multimedia System • Gain optimal perceptual value of the system based on the network conditions and user profile • Utility function: users’ preference or willingness to pay Cost U1 U2 Utility/cost/budget U3 Budget Bandwidth Xin Wang, Henning Schulzrinne, Columbia University

Example Utility Function • Utility is a function of bandwidth at fixed QoS • An example utility function: U (x) = U0 + log (x / xm) • U0 :perceived (opportunity) value at minimum bandwidth •  : sensitivity of the utility to bandwidth • Function of both bandwidth and QoS • U (x) = U0 + log (x / xm) - kd d - kl l , for x  xm • kd : sensitivity to delay • kl : sensitivity to loss Xin Wang, Henning Schulzrinne, Columbia University

Rate-Adaptation Models • Optimize perceived surplus of the multimedia system subject to budget and application requirements • User utility optimization: • U = ΣiUi (xi(Tspec, Rspec)] • max [ΣlUi (xi ) - Ci (xi) ], s. t. ΣlCi (xi) b , xmini xi xmaxi • Determine optimal Tspec and Rspec • With the example utility functions, resource request of application i: • Without budget constraint: x i = i / pi • With budget constraint: x i = bi / pi, withb i = b (i /Σl k) Xin Wang, Henning Schulzrinne, Columbia University

Demonstrate functionality and performance improvement: blocking rate, loss, delay, price stability, perceived media quality Host HRN negotiates for a system Host processes (HRN, VIC, RAT) communicate through Mbus Network Router: FreeBSD 3.4 + ALTQ 2.2, CBQ extended for DiffServ NRN: (1) Process RNAP messages; (2) Admission control, monitor statistics, compute price; (3) At edge, dynamically configure the conditioners and form charge Inter-entity signaling: RNAP Testbed Architecture RAT VIC Mbus HRN RNAP NRN Xin Wang, Henning Schulzrinne, Columbia University

Simulation Design • Performance comparison: fixed price policy (FP) vs. congestion price based adaptive service (CPA) • loss, delay, blocking rate, user benefit, network revenue, stability • Three groups of experiments: effect of traffic load, admission control, and load balance between classes • Weighted Round Robin (WRR) scheduler • Three classes: EF, AF, BE • EF: load threshold 40%, delay bound 2 ms, loss bound 10-6 • AF: load threshold 60%, delay bound 5 ms, loss bound 10-4 • BE: load threshold 90%,delay bound 100 ms,loss bound 10-2 • Sources: mix of on-off traffic and Pareto on-off traffic Xin Wang, Henning Schulzrinne, Columbia University

Simulation Architecture Topology 1 (60 users) Topology 2 (360 users) Xin Wang, Henning Schulzrinne, Columbia University

Effect of Traffic Load CPA maintains the traffic load at the targeted level, meets the expected performance bounds Xin Wang, Henning Schulzrinne, Columbia University

Effect of Admission Control Admission control is important in maintaining the expected performance of a class. Xin Wang, Henning Schulzrinne, Columbia University

Effect of Admission Control (cont’d) With admission control, the dynamics of the network price can be better controlled.Coupled with user adaptation, the blocking rate of CPA is up to 30 times smaller than that of FP. Xin Wang, Henning Schulzrinne, Columbia University

Effect of Admission Control (cont’d) CPA allows for higher network revenue and user benefit. Xin Wang, Henning Schulzrinne, Columbia University

Load Balance Between Classes Even when a small portion of users (15%) select other service classes, the performance of the over-loaded class is greatly improved. Xin Wang, Henning Schulzrinne, Columbia University

Conclusions • Proposed a dynamic resource negotiation framework consisting of: A Resource Negotiation And Pricing protocol (RNAP) , a rate and QoS adaptation model, and a pricing model • RNAP:supports dynamic service negotiation • Pricing models: based on resources consumed by service class and long-term user demand; including congestion-sensitive component to motivate user demand adaptation • Performance: • Effectively restricts load to targeted level and meet service assurance • Provide lower blocking rate, higher user satisfaction and network revenue • Admission control and inter-service class adaptation give further improvements in blocking rate and price stability Xin Wang, Henning Schulzrinne, Columbia University

Further Work • Interaction of short-term resource negotiation with longer-term network provision • A light-weight resource management protocol • Cost distribution in QoS-enhanced multicast network • Pricing and service negotiation in the presence of alternative data paths or competing networks • User valuation models for different QoS • Resource provisioning in wireless environment Xin Wang, Henning Schulzrinne, Columbia University

Xin Wang and Henning Schulzrinne Internet Real -Time Laboratory Columbia University