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

bandwidth, loss, delay, jitter, availability, price Today’s IP Networks Service Level Agreements (SLA) are negotiated based on Application Specific Needs bandwidth, loss, delay, jitter, availability, price Application SLA ISP Networks & Applications IP Network Service User Large number of new applications are appearing in the Internet. This includes the real-time audio, video, and mission-critical financial data. This provides ISP more business opportunity, and also challenge. The value-added services normally require certain service expectations. Since different applications have different requirements in bandwidth and quality, network resource provision is challenging. SCOPE Growth of new IP services and applications with different bandwidth and quality of service requirements Presents opportunities and challenges for service providers 11/29/2018

The needs of Next Generation Service Providers Revenue from the traditional connectivity services (raw bandwidth) is declining Increase revenue by offering innovative IP services: Deliver high-margin, differentiated services VoIP, VPN, Applications Hosting etc Gain competitive advantage by deploying new services more quickly, placing a premium on time to market and time to scale Reduce cost and operation complexity Evolve from static network management to dynamic service provisioning Reduce costs by automating network and service management Service provider needs to provide different premium services, without big cost. The network management and resource provisioning should be automatic and fast. 11/29/2018

Internet Structure End User LAN POP NAP Backbone Provider Regional Provider Private Network Backbone Provider NAP LAN End User Backbone Provider POP Private Peering Before deciding on the services for the internet, let’s look at the current Internet situation. Network is generally divided into different management domains, with direct peering or connect through Network Access Point (NAP). The Network Access Point (NAP) allows Internet Service Providers (ISPs) to interconnect and exchange information among themselves. The exchanging of Internet traffic is generally referred to as "peering". 11/29/2018

NORDUnet Traffic Analysis Before further describes the service policy, we show some traffic statistics of NORDUnet NORDUnet interconnects the Nordic national networks for research and education and connects these networks to the rest of the world. The current physical connections are shown on the connectivity map. NORDUnet provides its services by a combination of leased lines and Internet services provided by other international operators. 11/29/2018

NORDUnet Traffic Analysis Results: All access links (interconnect ISP’s or connect private networks to ISP’s), including trans-Atlantic links, can get congested. Average utilization is low: 20-30% Peak utilization can be high: up to 100% Congestion Ratio (peak/average): as high as 5. Peak duration can be very long: Chicago NAP congested once in 8/00, lasted 7 hours. TeleGlobe links congested every workday in 8/00 and 9/00 Reasons: Frequent re-configuration and upgrading;Load balancing to protect own users. 11/29/2018

Solution - Over-provisioning? Add enough bandwidth for all applications in access network / backbone Will over-provisioning be sufficient to avoid congestion? How much bandwidth is enough to meet diverse user requirements? No intrinsic upper limit on bandwidth use How much does it cost to add capacity? Demand: Availability of more bandwidth will create its own demand through increasing utilization of bandwidth intensive applications” .real-time audio//video, 3D imaging, virtual reality, etc. Supply: Cost of transportation using fiber optics is declining drastically. However, network management cost: switches and routers, state of the art POP, data centers, etc, will cost money. QoS: Protect the valuable applications through QoS. But will QoS add big complexity? Providing different servers needs different pricing, otherwise, everyone will ask the best service and end up not services. Applications do not have the motivation to adapt. 11/29/2018

Bandwidth Pricing Reality: leased bandwidth price has not been dropping consistently and dramatically. Facts: 300 mile T1 price (rent): 1987: $10,000/month 1992: $4,000/month 1998: $6,000/month (thanks to high Internet demand) 100-mile cabling cost in 1998: $65,000 Links connecting ISP’s are very expensive 11/29/2018

Bandwidth Pricing (cont.) Facts: International Frame Relay with 256-kbps: thousands dollars a month. Transit DS-3 link: $50,000/month between carriers. Transit OC-3 link: $150,000/month between carriers. Chicago NAP: $3,900/month/DS-3, $4,700/month/OC-3. T1 - 1.544 megabits per second (24 DS0 lines) T3 - 43.232 megabits per second (28 T1s) OC3 - 155 megabits per second (100 T1s) OC12 - 622 megabits per second (4 OC3s) OC48 - 2.5 gigabits per seconds (4 OC12s) OC192 - 9.6 gigabits per second (4 OC48s) The price for a Chicago NAP connection is distance sensitive and based on the location where the ISP's network meets Ameritech's. ATM pricing also varies with contract length with price deductions for longer term contracts. NAP connection prices start at $3,900 per month for a DS3 and $4,700 per month for an OC3. Duration of 12, 36 or 60 month terms are available. Bandwidth may be cheap, but not free Higher-speed connection -- higher recurring monthly costs. Option - manage the existing bandwidth better, with a service model which uses bandwidth efficiently. 11/29/2018

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 QoS to meet diverse user requirements How efficiently does a QoS mechanism manage bandwidth? How much does a user need to pay for QoS? Application adaptation Source rate adaptation based on network conditions can avoid congestion and lead to efficient bandwidth utilization Why would an application adapt? Now we consider a more efficient service model than simply overprovisioning. What are the desirable things to have in such a model? QoS …… We need to worry about how to manage bandwidth for each QoS enhanced service, and how to differentiate the different services through pricing Adaptation ….. Why should ….. 11/29/2018

A More Efficient Service Model (cont’d) Dynamic resource negotiation Network commits resources for short intervals - better response to changes in network conditions and user demand Usage-,QoS-,demand-sensitive pricing Allow network to price services based on resources consumed, and allocate resources based on user willingness-to-pay Give user incentive to select appropriate service based on requirements, adapt demand during network resource scarcity in response to increase in price To support services with QoS mechanisms, and user adaptation, a couple of other features are desirable….. 11/29/2018

What We Add to Enable This Model A dynamic resource negotiation protocol: RNAP An abstract Resource Negotiation And Pricing protocol Enables user and network (or two network domains) to dynamically negotiate multiple services with different QoS characteristics Enables network to formulate and communicate prices and charges Lightweight and flexible: embedded in other protocols, e.g., RSVP, or implemented independently Ensures service predictability:commit service and price for an interval Supports multi-party negotiation: senders, receivers, or both Reliable and scalable A demand-sensitive pricing model Enables differential charging for supporting multiple levels of services; services priced to reflect the cost and long-term user demand Allows for congestion pricing to motivate user adaptation 11/29/2018

What we add... (cont’d) Demonstrate a complete resource negotiation framework (RNAP, pricing model, user adaptation) on test-bed network Simulations show significant advantages relative to static resource allocation and fixed pricing: Much lower service blocking rate under resource contention Service assurances under large or bursty offered loads, without highly conservative provisioning Higher perceived user benefit and higher network revenue 11/29/2018

Outline RNAP: Architecture and Messaging Pricing models: Comparison of models Usage and congestion-based pricing model Pricing mechanism User adaptation Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Resource Negotiation Framework 11/29/2018

Protocol Architectures: Centralized RNAP Messages Network Resource Negotiator End User NRN NRN NRN Access Domain - A We consider two alternative architectures for implementing RNAP in the network, a centralized architecture (RNAP-C) and a distributed architecture (RNAP-D) In RNAP-C, user negotiates through a HRN, each network domain has a NRN. When a user wants to to apply for resources from the network, it first sends a request to the NRN of its access domain. This request is then propagated to next-domain NRN, and so on. In general, each NRN is in charge of admission control, price quotation and charging for its domain. Edge Router Access Domain - B Internal Router Intra-domain messages Transit Domain Host Resource Negotiator (HRN) RNAP-C 11/29/2018

Protocol Architectures: Distributed End User RNAP Messages LRNs Access Domain - A We consider two alternative architectures for implementing RNAP in the network, a centralized architecture (RNAP-C) and a distributed architecture (RNAP-D) In RNAP-C, user negotiates through a HRN, each network domain has a NRN. When a user wants to to apply for resources from the network, it first sends a request to the NRN of its access domain. This request is then propagated to next-domain NRN, and so on. In general, each NRN is in charge of admission control, price quotation and charging for its domain. Edge Router Access Domain - B Internal Router End User Transit Domain RNAP-D 11/29/2018

RNAP Messages Periodic re-negotiation 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 re-negotiation Quotation Commit: Admits the service request at a specific price or denies it. Reserve Commit Close: Tears down negotiation session Close Release: Releases the resources Release 11/29/2018

Message Aggregation (RNAP-D) Turn off router alert Turn on router alert Sink-tree-based aggregation 11/29/2018

Message Aggregation (RNAP-C) Sink-tree-based aggregation 11/29/2018

RNAP Message Aggregation Summary Aggregation when senders share the same destination network Messages merged by source or intermediate domains Messages de-aggregated at destination border routers (RNAP-D), or NRNs (RNAP-C) Original messages sent directly to destination/source domains without interception by intermediate RNAP agents; aggregate message reserves and collects price at intermediate nodes/domains Overhead Reduction Processing overhead, storage of states 11/29/2018

Block Negotiation (network-network) Aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics Bandwidth time 11/29/2018

Outline RNAP: Architecture and Messaging Pricing models: Comparison of models Usage and congestion-based pricing model Pricing mechanism User adaptation Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Resource Negotiation Framework 11/29/2018

Pricing in Current Internet Access-rate-dependent flat charge (AC) Simple, predictable Difficult to compromise between access speed and cost No incentive for users to limit usage congestion Usage-based charge Volume-dependent charge (V) Time-base charge (T) work better for uniform per-time unit resource demands, e.g., telephone Access charge + Usage-based charge Per-hour charge after certain period of use, or per-unit charge after some amount of traffic transmitted. Flat charge for basic service, usage charge for extra bandwidth or premium services Flat fee: Predictable fee for both users and providers. Avoid potentially considerable administrative costs of tracking, allocating and billing for usage 11/29/2018

Two Volume-based Pricing Strategies Fixed-Price (FP): fixed unit volume price FP-FL: per-byte charge are same for all services FP-PR: service class dependent FP-T: time-of-day dependent FP-PR-T: FP-PR + FP-T During congestion: higher blocking rate OR higher dropping rate and delay Congestion-Price-based Adaptation (CPA): FP + congestion-sensitive price component CP-FL, CP-PR, CP-T, CP-PR-T Congestion price calculation: Tatonnement process or auction During congestion: users maintain service by paying more OR reducing sending rate OR switching to lower service class In period of resource scarcity, quality sensitive applications can maintain their resource levels by paying more \Quality insensitive applications will reduce their sending rate or change to a lower service class 11/29/2018

Important Time Scales Technical levels of interaction Monetary levels of interaction atomic short-term medium-term long-term Retransmission Error Handling Flow Control Resource Reservation Capacity Planning Scheduling Feedback Policing Routing time Congestion Time-of-day Pricing Flat Rates Pricing 11/29/2018

Pricing Strategies Holding price and charge: based on cost of blocking other users by holding bandwidth without sending data phj =  j (pu j - pu j-1) , chij (n) = ph j r ij (n) j Usage price and charge: optimize the provider’s profit 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 (two mechanisms) 11/29/2018

Usage Price for Differentiated Service Usage price based on cost of class bandwidth: lower target load (higher QoS) -> higher per-unit bandwidth price Parameters: pbasic basic rate for fully used bandwidth  j : expected load ratio of class j xij : effective bandwidth consumption 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: xe j ( puj ) = Aj / ( puj  j ) Network maximizes profit: max [Σl (Aj / pu j ) pu j - f (C)], puj = pbasic /  j , s. t. Σl Aj / ( pu j  j )  C Hence: pbasic = Σl Aj / C , puj = Σl Aj /(C j) 11/29/2018

Congestion price: first mechanism - Tatonnement Tatonnement process (CPA-TAT): network applies congestion charge proportional to excess demand relative to target utilization pc j (n) = min [{pcj (n-1) +  j (Dj, Sj) x (Dj-Sj)/Sj,0 }+, pmaxj ] ccij (n) = pc j v ij (n) 11/29/2018

Congestion price: second mechanism - Second, M-bid Auction Auction models in literature: assume unique bandwidth/price preference; user does not know about high demand until rejected bid -> service uncertainty user may tend not to bid true valuation M-bid auction model: User bids for a number of bandwidths, bid prices obtained by sampling utility function. Network selects highest bids (one per user); charges highest rejected bid price During high demand: lower bandwidth (higher price per unit bandwidth) bids get selected; more users served Inter-auction admission to reduce setup delay 11/29/2018

Outline RNAP: Architecture and Messaging Pricing models: Comparison of model Usage and congestion-based pricing model Pricing mechanism User adaptation Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Resource Negotiation Framework 11/29/2018

Rate Adaptation of Multimedia System Enable multimedia applications to gain optimal perceptual value based on the network conditions and user profile. A Host Resource Negotiator (HRN) negotiates services with network on behalf of a multimedia system. Utility function: users’ preference or willingness to pay Cost U1 U2 Utility/cost/budget U3 Budget Bandwidth 11/29/2018

Example Utility Function User defines utility at discrete bandwidth, QoS levels 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 11/29/2018

Two Rate Adaptation Models User adaptation under CPA-TAT (tatonnement-based pricing) Optimize perceived surplus (utility - cost) subject to budget and minimum application requirements: max [Σl U0i + i log (xi / xmi ) - kdi d - kl i l - pi xi ], s.t. Σl pi xi  b , x  xm , d  D, l  L Without budget constraint: x i = i / pi With budget constraint: b i = b ( i / Σl  k ) User adaptation under CPA-AUC (second-price auction) Submit M-bid derived by sampling utility function. Adapt rate based on allocated bandwidth/QoS Adaptation of applications in multimedia system Distribute bid/allocated bandwidth among applications for optimal overall surplus U = Σi Ui (xi (Tspec, Rspec)] max [Σl Ui (xi ) - Ci (xi) ], s. t. Σl Ci (xi)  b , xmini  xi  xmaxi 11/29/2018

Stability and Oscillation Reduction Congestion-sensitive pricing (tatonnement process) has been shown to be stable Oscillation reduction Users: re-negotiate only if price change exceeds a given threshold Network: update price only when traffic change exceeds a threshold; negotiate resources in larger blocks between domains 11/29/2018

Outline RNAP: Architecture and Messaging Pricing models: Comparison of model Usage and congestion-based pricing model Pricing mechanism User adaptation Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Resource Negotiation Framework 11/29/2018

Test-bed Architecture Demonstrate functionality and performance improvement: blocking rate, average loss and delay, price stability, perceived media quality Host HRN negotiates resources for a system Host processes (HRN, VIC, RAT) communicate through Mbus Network FreeBSD 3.4 + ALTQ 2.2, CBQ extended for DiffServ NRNs: Process RNAP messages Admission control, monitor service statistics, compute price At edge, dynamically configure the conditioners and form charge Inter-entity signaling: RNAP RAT VIC Mbus HRN RNAP NRN 11/29/2018

Functions of Routers Interior routers: per-class policing, e.g, TBMETER (in/out) for a class Edge routers: flow conditioning/policing based on SLA The NRN supports flexible definition of service classes and their specification. The spec is typically based on a set of QoS parameters. It also includes a pricing function for calculating price components. Identifier is typically a standard mechanism to identify the service to clients. For example, for a DiffServ service like Expedited Forwarding, the identifier is the DSCP. The NRN at edge routers performs per-flow policing and conditioning. The policing function can for example use a token-bucket to measure the flow’s usage and take appropriate measures. The NRN's running on interior routers do not maintain per-flow measurements. But they do keep track of per-flow allocation information. They do per-class policing using the DiffServ BA technique. 11/29/2018

Network Resource Negotiator (NRN) Provide price for each service class Measurement-based admission control predict future demand, update congestion price based on predictions 11/29/2018

Network States Per-class bandwidth and price variations Reduction in blocking due to adaptation 11/29/2018

Adaptive Wireless Terminal WAP development over Nokia Toolkit 2.0 Currently cell phone services: Flat pricing and best effort: all users get worse quality (coarse voice, busy signal, cut off) when congestion occurs. Using our solution: Provide real-time pricing information, e.g., every 10 minutes or every call Customers choices: pay a premium to have best quality pay less by tolerating worse quality back off to call another time. Reduce the blocking rate of overall network 11/29/2018

Outline RNAP: Architecture and Messaging Pricing models: Comparison of model Usage and congestion-based pricing model Pricing mechanism User adaptation Test-bed demonstration of Resource Negotiation Framework Simulation and discussion of Resource Negotiation Framework Resource Negotiation Framework 11/29/2018

Simulation Design Performance comparison: Four groups of experiments: Network with dynamic services and rate-adaptive users versus network with non-adaptive users Fixed price policy (FP) (usage price + holding price) versus congestion price based adaptive service (CPA) (usage price + holding price + congestion price) Four groups of experiments: Effect of traffic burstiness Effect of traffic load Load balance between classes Effect of admission control Engineering metrics: Bottleneck traffic arrival rate, average packet loss and delay, user request blocking probability Economic metrics Average and total user benefit, end-to-end price and its standard deviation, network revenue 11/29/2018

Simulation Models (delete?) Network Simulator (NS-2) Weighted Round Robin (WRR) scheduler Three classes: EF, AF, BE EF: tail dropping, limited to 50 packets; load threshold 40%, delay bound 2 ms, loss bound 10-6 AF: RED-with-In-Out (RIO), limited to 100 packets; load threshold 60%, delay bound 5 ms, loss bound 10-4 BE: Random Early Detection (RED), limited to 200 packets; load threshold 90%, delay bound 100 ms, loss bound 10-2 Sources: on-off or Pareto on-off (shape parameter: 1.5) Negotiation period: 30 s, session length 10 min 11/29/2018

Simulation Architecture Topology 1 (60 users) Topology 2 (360 users) 11/29/2018

Load Balance between Classes Variation over time of the price of AF class Ratio of AF class traffic migrating through class re-selection 11/29/2018

Effect of Traffic Burstiness Average packet delay Average packet loss 11/29/2018

Price average and standard deviation of AF class Effect of Traffic Burstiness (cont’d) Price average and standard deviation of AF class Average user benefit 11/29/2018

Effect of Traffic Load (cont’d) Average packet loss Average packet delay 11/29/2018

Price average and standard deviation of AF class Effect of Traffic Load Price average and standard deviation of AF class Average user benefit 11/29/2018

Load Balance between Classes (cont’d) Average packet delay Average packet loss 11/29/2018

Effect of Admission Control Average packet delay Average packet loss 11/29/2018

Effect of Admission Control (cont’d.) Average and standard deviation of AF class price User request blocking rate 11/29/2018

Conclusions RNAP Pricing model Application adaptation Supports dynamic service negotiation, mechanisms for price and charge collation, auction bids and results distribution Allows for both centralized and distributed architectures Supports multi-party negotiation: senders, receivers, or both Can be stand-alone, or embedded inside other protocols Reliable and scalable Pricing model Consider both long-term user demand and short-term traffic fluctuation; use congestion-sensitive component to motivate user demand adaptation during resource scarcity Application adaptation Maximize users’ perceptual value, tradeoffs between quality and expenditure 11/29/2018

Conclusions (cont’d) Auction Model Simulation results Serves more users than comparable schemes, and has less signaling overhead and greater certainty of service availability Simulation results Differentiated service requires different target loads in each class CPA policy coupled with user adaptation effectively limit congestion, provide lower blocking rate, higher user satisfaction and network revenue than with the FP policy Both auction and tatonnement process can be used to calculate the congestion price; auction scheme gains higher perceived user benefit and network utilization at cost of implementation complexity and setup delay Without admission control, service is assured by restricting the load to the targeted level; with admission control, performance bounds can be assured even with FP policy, but CPA reduces the request blocking rate greatly and helps to stabilize price 11/29/2018

Conclusions (cont’d) Future work Allowing service class migration further stabilizes price Users with different demand elasticity share bandwidth proportional to their willingness to pay Even a small proportion of user adaptation results in a significant performance improvement for the entire user population Performance of CPA further improves as the network scales and more connections share the resources Future work Propose light-weight resource management protocol Resource management in wireless environment 11/29/2018