1. Resource Negotiation, Pricing and QoS
for Adaptive Multimedia Applications
Xin Wang
Work with Henning Schulzrinne
Internet Real -Time Laboratory
Columbia University

2. Scope of Metropolitan IP Network
Service Level Agreements (SLA) are negotiated based on Application Specific Needs
bandwidth, loss, delay, jitter, availability, price
Application SLA
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.
Proliferation of new IP services and applications with different bandwidth and quality of service requirements presents significant opportunities and challenges for service providers for IP networks provisioning.
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3. The needs from Next Generation Service Providers
Increase revenue by offering innovative IP services:
Delivering 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.
It is critical for ISPs to offer premium IP services cost effectively to offset the
declining revenue from the traditional connectivity services (raw bandwidth).
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4. 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".
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5. 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.
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6. 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.
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7. Solutions ? Simply over-provisioning? Quality of Service (QoS)?
Adding enough bandwidth for all applications
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 it costs to add capacity?
Quality of Service (QoS)?
Conditioning the network to provide some predictability to an application
How efficient a QoS mechanism manages the bandwidth? How much complexity? How much a user needs to pay for QoS?
Application adaptation?
Adapting the source rate based on network conditions to avoid congestion
Why would an application adapt? To leave room for others?
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.
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8. 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
Purchase a higher-speed connection if the option to manage the existing bandwidth to support the applications is available?
What company can disregard high recurring monthly costs?
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9. Bandwidth Pricing (cont.)
Links connecting ISP’s are very expensive
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.
T megabits per second (24 DS0 lines)
T megabits per second (28 T1s)
OC megabits per second (100 T1s)
OC megabits per second (4 OC3s)
OC gigabits per seconds (4 OC12s)
OC 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. We need a service model and pricing mechanism that would allocate bandwidth more efficiently.
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10. Solutions ? Simply over-provisioning? Quality of Service (QoS)?
Adding enough bandwidth for all applications
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 it costs to add capacity?
Quality of Service (QoS)?
Conditioning the network to provide some predictability to an application
How efficient a QoS mechanism manages the bandwidth? How much complexity it will add? How much a user needs to pay for QoS?
Application adaptation?
Adapting the source rate based on network conditions to avoid congestion
Why would an application adapt? To leave room for others?
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.
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11. What we add to these solutions?
Developed a service negotiation framework
QoS support + user adaptation, allows resource commitment for short intervals.
Proposed a Resource Negotiation And Pricing protocol RNAP:
Enables user and network (or two network domains) to dynamically negotiate services
Can be embedded in other protocols, e.g., RSVP, or implemented independently
Proposed a pricing model
Enable differentiated charging to suppor multiple levels of services
Considers long-term user demand
Allows for congestion pricing to motivate user adaptation
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12. Goals Achieve Network Efficiency Achieve Economic Efficiency
Maintain a target level of service while minimizing the resources needed to provide this service
Improves network availability and QoS for the users
Allow a service class to meet its performance assurances under large or bursty offered loads, without high network management overhead, or over-provision cost
Achieve Economic Efficiency
Price services levels to reflect the QoS cost
Under resource contention, allocate resources to the users that value them most
Maximize the user benefit, maximize network revenue
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13. Protocol Architectures: Centralized
Host Resource
Negotiator
RNAP Messages
Network Resource
Negotiator
NRN
NRN
NRN
HRN
HRN
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, monitoring network statistics price quotation and charging for its domain.
Edge Router
Access Domain - B
Internal Router
Intra-domain messages
Transit Domain
RNAP-C
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14. Protocol Architectures: Distributed
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
In RNAP-D, Local Resource Negotiators (LRN) were implemented on each router, for
admission control, monitoring network statistics, forming price for each service class.
At network edge, NRNs dynamically configure traffic conditioners, based on on-going user requests.
Internal Router
Transit Domain
RNAP-D
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15. 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/Updates service(s),
resources
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
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16. Message Aggregation (RNAP-D)
Turn off router alert
Turn on router alert
Sink-tree-based aggregation
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17. Message Aggregation (RNAP-C)
Sink-tree-based aggregation
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18. 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
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19. Block Negotiation
Aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics
Bandwidth
time
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20. 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., phone
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
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21. 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 reduce sending rate OR 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
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22. Important Time Scales Technical levels of interactions
Monetary levels of interactions
atomic
short-term
medium-term
long-term
Retransmission
Error Handling
Flow Control
Resource
Reservation
Capacity
Planning
Scheduling
Feedback
Policing
Routing
time
Congestion
Pricing
Time-of-day
Flat Rates
Pricing
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23. Pricing Strategies
Holding price and charge: cost by depriving others’ opportunity to be admitted even if no data is sent
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
Tatonnement process (CPA-TAT):
pc j (n) = min [{pcj (n-1) +  j (Dj, Sj) x (Dj-Sj)/Sj,0 }+, pmaxj ]
ccij (n) = pc j v ij (n)
Second price auction(CPA-AUC): charge as the highest biding price of the requests being rejected.
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24. 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)
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25. 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
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26. Utility User adaptation under CPA-TAT User adaptation under CPA-AUC
Optimally distributes network-allocated among applications in a multimedia system, optimize the system performance
U = Σi Ui (xi (Tspec, Rspec)]
max [Σl Ui (xi ) - Ci (xi) ], s. t. Σl Ci (xi)  b , xmini  xi  xmaxi
Determine optimal Tspec and Rspec
User adaptation under CPA-AUC
Literature model: not consider short-term resource reservation; not address user response to price change; assume unique service request; service uncertainty, signaling burst, setup delay
M-bid auction model: express bid for a number of bandwidth; network select one that has the lowest bidding price
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27. 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
Optimization:
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 )
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28. Stability and Oscillation Reduction
Congestion-sensitive pricing (tatonnement process) has been shown to be stable (see web page for proof)
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
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29. 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 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
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30. 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.
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31. Network Resource Negotiator (NRN)
Provide price for each service class
Measurement-based admission control
predict future demand, update congestion price based on predictions
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32. Network States Per-class bandwidth and price variations
blocking reduction due to adaptation
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33. 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
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34. Simulation Design Performance comparison: Four groups of experiments:
Network with dynamic services and rate-adaptive users and network with no adaptive users
Fixed price based 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
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35. Performance Measures Engineering metrics Economic 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
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36. Simulation Modeling 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
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37. Simulation Architecture
Topology 1 (60 users)
Topology 2 (360 users)
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38. Load Balance between Classes
Variation over time of the
price of AF class
Ratio of AF class traffic migrating through class re-selection
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39. Effect of Traffic Burstiness
Average packet delay
Average packet loss
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40. 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
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41. Effect of Traffic Load (cont’d)
Average packet loss
Average packet delay
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42. Price average and standard deviation of AF class
Effect of Traffic Load
Price average and standard deviation of AF class
Average user benefit
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43. Load Balance between Classes (cont’d)
Average packet delay
Average packet loss
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44. Effect of Admission Control
Average packet delay
Average packet loss
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45. Effect of Admission Control (cont’d.)
Average and standard deviation of AF class price
User request blocking rate
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46. 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 upon resource scarce
Application adaptation
Maximize users’ perceptual value, tradeoffs between quality and payment
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47. 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
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48. 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 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
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