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Xin Wang Internet Real -Time Laboratory Columbia University

An Integrated Resource. Negotiation, Pricing, and. QoS Adaptation. Framework for. Multimedia Applications. Xin Wang Internet Real -Time Laboratory Columbia University http://www.cs.columbia.edu/~xinwang. Outline. Motivation Objectives

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Xin Wang Internet Real -Time Laboratory Columbia University

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  1. An Integrated Resource Negotiation, Pricing, and QoS Adaptation Framework for Multimedia Applications Xin Wang Internet Real -Time Laboratory Columbia University http://www.cs.columbia.edu/~xinwang

  2. Outline • Motivation • Objectives • Resource negotiation & RNAP: architectures, messages, aggregation, reliability • Pricing • Price and charge formulation • Pricing on current Internet • Proposed pricing schemes • User request adaptation • Simulation • Conclusions IRT, Columbia University

  3. Motivation • Multimedia requires minimum QoS • Current approaches • A: resource reservation, admission control, differentiated services • Pros: QoS expectation • Cons: insufficient knowledge on data traffics, conservative, network dynamics not considered, lacks pricing support for multiple service levels • B: multimedia adaptation to network conditions • Pros: efficient bandwidth usage • Cons: users have no motivation to adapt requests IRT, Columbia University

  4. Objectives • Develop Resource Negotiation and Pricing Framework which: • Combines QoS support and user adaptation • Allows resource commitment for short intervals • Provides differential pricing for differentiated services • Allows usage- and congestion-sensitive pricing to motivate user adaptation • Allows provider to trade-off blocking connections and raising prices IRT, Columbia University

  5. Resource Negotiation through RNAP • Assumption: • Different service types, e.g. diff-serv, int-serv, best-effort. • With a pricing structure (may be usage-sensitive) for each. • RNAP (Resource Negotiation and Pricing):a protocol through which the user and network (or two network domains) negotiate network delivery services. • Network -> User:availability of services, price quotations, accumulated charges, service statistics • User -> Network: request/re-negotiate services • Underlying Mechanism: • Traffic engineering + network pricing IRT, Columbia University

  6. Resource Negotiation through RNAP (cont’d.) • Characteristics • Multiple service selection • Centralized or distributed architecture • Dynamic negotiation, multi-party negotiation • Price collation and communication • Scalable and reliable • Who can use RNAP? • Adaptive applications: adapt sending rate, choice of network services • Non-adaptive applications: take fixed price, or absorb price change IRT, Columbia University

  7. Protocol Architectures: Centralized (RNAP-C) NRN NRN NRN HRN HRN S1 R1 Access Domain - A Access Domain - B Transit Domain Network Resource Negotiator Internal Router NRN Host Resource Negotiator Edge Router HRN Host Data Flow RNAP Messages Intra-domain messages IRT, Columbia University

  8. Protocol Architectures: Distributed (RNAP-D) HRN HRN S1 R1 Access Domain - A Access Domain - B Transit Domain Internal Router HRN Host Resource Negotiator Edge Router Data Flow Host RNAP Messages IRT, Columbia University

  9. RNAP Messages Query:Inquires about available services, prices Query Quotation: Specifies service availability, prices Quotation Reserve Reserve: Requests service(s), resources Commit 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 Release: Releases the resources IRT, Columbia University

  10. RNAP Message Aggregation RNAP-D RNAP-C IRT, Columbia University

  11. RNAP Message Aggregation (cont’d) • Aggregation when senders share the same destination network • Messages merged by source or intermediate domains • Messages de-aggregated by HRNs 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 IRT, Columbia University

  12. Block Negotiation • Block Negotiation • Aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics Bandwidth time IRT, Columbia University

  13. Reliability • Soft state for synchronous messages, liveness tracking through • Retransmission of asynchronous messages • Server backup and information retrieval IRT, Columbia University

  14. Price and Charge Formulation • Routers or NRNs maintain state information • Resource usage, service prices, service statistics • Id, price, accumulated charge for a user • e2e price and charge collation • Message passes through routers or NRNs, price/charge fields in message incremented • Quotation: carry estimated price for each quoted service • Commit : carry accumulated charge for preceding negotiation interval, committed price for next interval IRT, Columbia University

  15. Price Formulation in RNAP-C • Alternative Schemes: • NRN does admission control and price computation • Pricing based on topology, routing, policies, network load • NRN determines data path • Accumulates price • Sends total price to HRN or neighbors • Ingress router does admission control and price computation • may determine internal router loads through egress-to-ingress probe messages • Routers of the path make admission: through intra-domain signaling protocol, such as RSVP/YESSIR. IRT, Columbia University

  16. Pricing on Current Internet • Access rate dependent flat charge • Usage-based charge • Volume-based charge • Time-base charge • Access charge + Usage-based charge IRT, Columbia University

  17. Two Volume-based Pricing Policies • Fixed-Price (FP) • FP-FL: 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 • CP-FL, CP-PR, CP-T, CP-PR-T • During congestion: users maintain service by paying more OR reduce sending rate or lower service class IRT, Columbia University

  18. Proposed Pricing Strategies • Holding price and charge: • phj = j (pu j - puj-1) • chij(n) = ph j r ij(n) j • Usage price and charge: • max [Σl x j (pu1 , pu2 , …, puJ ) puj - f(C)], s.t. r (x(pu2 , pu2 , …, puJ)) R, j  J • cuij (n) = pu j v ij (n) • Congestion price and charge: • pcj (n) = min [{pcj (n-1) + j(Dj, Sj) x (Dj-Sj)/Sj,0 }+, pmaxj ] • ccij (n) = pc j v ij (n) IRT, Columbia University

  19. Usage Price for Differentiated Services • Usage price for a service class based on cost of class bandwidth; lower target load -> higher per unit bandwidth price, higher QoS • Parameters: • pbasic basic rate for fully used bandwidth • j: expected load ratio of class j • xij: effective bandwidthconsumption of application i • Aj : constant elasticity demand parameter IRT, Columbia University

  20. Usage Price for Differentiated Services (cont’d) • Price for class j: puj = pbasic /j • Demand of class j: xj ( puj ) = Aj /puj • Effective bandwidth consumption: • xj ( 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) IRT, Columbia University

  21. User adaptation based on utility • Users adapt service selection + data rate based on utility associated with QoS + data rate; utility expressed in terms of perceived value, e.g.,15 cents /min • Multi-application task (eg., video-conference) - maximize total utility of task subject to budget -> dynamic resource allocation among component applications • 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 IRT, Columbia University

  22. User adaptation based on utility: example • User defines utility at discrete bandwidth, QoS levels • 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 bandwidthfind argo.ctr • 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 • Optimization: • max [ΣlU0i + ilog (xi / xmi ) - kdi d - klil - pi xi], s. t. Σlpi xib , x  xm ,d D, l  L • Without budget constraint: x i = i / pi • With budget constraint: b i = b (wi /Σl k ) IRT, Columbia University

  23. 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: a) update price only when traffic change exceeds a threshold; b) negotiate resources in larger blocks IRT, Columbia University

  24. Simulation Model Topology 1 Topology 2 IRT, Columbia University

  25. Simulation Model • Network Simulator (NS-2) • Weighted Round Robin (WRR) scheduler • Three classes: EF, AF, BE • EF: • tail dropping, limited to 50 packets • expected load threshold 40% • AF: • RED-with-In-Out (RIO), limited to 100 packets • expected load threshold 60% • BE: • Random Early Detection (RED), limited to 200 packets • expected load threshold 90% IRT, Columbia University

  26. Simulation Model (cont’d) • Parameter Set-up • topology1: 48 users; topology 2: 360 users • user requests: 60 kb/s -- 160 kb/s • targeted reservation rate: 90% • price adjustment factor: σ = 0.06 • update threshold: θ = 0.05 • negotiation period: 30 seconds • usage price: pbasic = $0.08 / min, pEF = $0.20 / min, pAF = $0.13 / min, pBE = $0.09 / min • holding price: pEF = $0.067 / min, pAF = $0.044 / min • average session length 10 minutes, exponentialdistributed. IRT, Columbia University

  27. Simulation Model (cont’d.) • Performance measures • Engineering metrics • Bottleneck traffic arrival rate • Average packet loss and delay • User request blocking probability • Economicmetrics • Average user benefit • End to end price, and it standard deviation IRT, Columbia University

  28. Design of Experiments • Performance comparison: Fixed price policy (FP: usage price + holding price) and CPA • Four groups of experiments: • Effect of traffic burstiness • Effect of traffic load • Load balance between classes • Effect of admission control • Other experiments (…...): • Effect of system control parameters: target reservation rate, price adjustment step, price adjustment threshold • Effect of user demand elasticity, session multiplexing • Effect when part of users adapt, session adaptation and adaptive reservation IRT, Columbia University

  29. Effect of Traffic Burstiness Price average and stand deviation of AF class Variation over time of the price of AF class IRT, Columbia University

  30. Effect of Traffic Burstiness (cont’d) Average packet delay Average packet loss IRT, Columbia University

  31. Effect of Traffic Burstiness (cont’d) Average traffic arrival rate Average user benefit IRT, Columbia University

  32. Effect of Traffic Load Price average and stand deviation of AF class Variation over time of the price of AF class IRT, Columbia University

  33. Effect of Traffic Load (cont’d) Average packet delay Average packet loss IRT, Columbia University

  34. Effect of Traffic Load (cont’d) Average traffic arrival rate Average user benefit IRT, Columbia University

  35. Load Balance between Classes Variation over time of the price of AF class Ratio of AF class traffic migrating through class re-selection IRT, Columbia University

  36. Load Balance between Classes (cont’d) Average packet delay Average packet loss IRT, Columbia University

  37. Effect of Admission Control Average and standard deviation of AF class price User request blocking rate IRT, Columbia University

  38. Effect of Admission Control (cont’d) Average packet delay Average packet loss IRT, Columbia University

  39. Conclusions • RNAP • Enables dynamic service negotiation • Supports centralized and distributednetwork architectures • Has mechanisms for price and charge formulation, collation, communication • Flexibility of service selection • Multi-party negotiation: senders, receivers, both • Stand alone, or embedded inside other protocols • Reliable and scalable IRT, Columbia University

  40. Conclusions (cont’d) • Pricing • Differential pricing for multiple classes of services • Consider both long-term user demand and short-term traffic fluctuation; use congestion-sensitive component to drive adaptation in congested network • Application adaptation • Bandwidth shared among competing users proportional to user’s willingness to pay IRT, Columbia University

  41. Conclusions (cont’d) • Simulation results: • Differentiated service requires different target loads in each class • Without admission control, CPA coupled with user adaptation allows congestion control, and service assurances • 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 • Allowing service class migration further stabilizes price • Future work • Refine the RNAP protocol, experiments over Internet2 IRT, Columbia University

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