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

2. Outline Background RNAP: architecture and messaging Pricing models
User adaptation
Testbed demonstration of Resource Negotiation Framework
Simulation and discussion of Resource Negotiation Framework
Resource Negotiation
Framework
We will first present the background of this work
We then describe the proposed resource negotiation framework, which consists of
a Resource negotiation and pricing protocol, a pricing model, and a user adaptation model.
Finally, we introduce our test-bed setup and
also present some simulation results.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

3. 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
A large number of new applications are appearing in the Internet. This includes real-time audio, video, and mission-critical financial data.
At the same time, revenue from the traditional connectivity services (raw bandwidth) is declining
The ISP has new business opportunities, but also new 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
In addition, it is difficult to predict the various user requirements, especially due to the quick deployments of new applications. Also, recent history tells us that
availability of more bandwidth will create its own demand through increasing utilization of bandwidth intensive applications. Another option: manage the existing bandwidth more efficiently
Option - manage the existing bandwidth better, with a service model which uses bandwidth efficiently.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

4. 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
How to manage multiple service more efficiently? How much to charge a service?
Application adaptation
Source rate adaptation based on network conditions - congestion control and efficient bandwidth utilization
Best effort service
Why would an application adapt?
Two models currently can lead to more efficent bandwidth usage: QoS and Application adaptation
QoS: Provide value-added services with QoS expectations even during high user demand. Provide multiple levels of QoS to meet diverse user requirements.
In general, adding multiple levels of QoS requires more network management, and greater user-network negotiation.
Clearly, providing different services also means that we need a differentiated pricing structure.
Adaptation schemes in literature assume user adapts source rate based on knowledge of network statistics (polling, etc.) - able to control congestion at high loads
Literature work in multimedia adaptation assumes best effort service, users are well-behaved, would adapt even without any incentive
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

5. A More Efficient Service Model (cont’d)
Requirements of QoS/adaptive model:
mechanism to select and negotiate services
adaptive applications
short-term resource configuration for better response to user demand and network conditions, for more efficient resource usage
Allow dynamic resource negotiation during ongoing service
price network services based on QoS (resources consumed), allocate resources based on user willingness-to-pay
provide signal / incentive for user adaptation through pricing
A dynamic service selection and resource negotiation mechanism
Usage-,QoS-,demand-sensitive pricing
To support multiple services with QoS - service providers need to provide a service selection and negotiation mechanism (well-known example - RSVP)
For efficient resource usage - network should be able to commit resources for a short-term, dynamically re-configure resources based on demand and network conditions, particularly if users are adaptive
Network should price services based on QoS, or resources consumed to provide a certain QoS, and allocate resources based on user willingness-to-pay. Pricing should also serve as a signal and/or incentive for users to adapt
All of the above translate to two main requirements: …..
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

6. 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
Enables network to formulate and communicate prices and charges
Service predictability: commit service and price for an interval
Multi-party negotiation: senders, receivers, or both
Reliable and scalable
Lightweight and flexible: embedded in other protocols, e.g., RSVP, or implemented independently
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
Our work:
We designed a A dynamic resource negotiation protocol: RNAP
to enable
to provide Service predictability
to support Multi-party negotiation
to be Reliable and scalable
to be Lightweight and flexible: embedded in other protocols, e.g., RSVP, or implemented independently
We proposed A demand-sensitive pricing model which supports differential charging for multiple levels of services, reflecting the cost and long-term user demand
The model also allows for congestion pricing to motivate user adaptation
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

7. What We Add... (cont’d)
Demonstrate a complete resource negotiation framework (RNAP, pricing model, user adaptation) on test-bed network
Show significant advantages relative to static resource allocation and fixed pricing using simulations:
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
Our work:
We demonstrate a complete resource negotiation framework on test-bed network
We show significant advantages of this framework relative to static resource allocation and fixed pricing using simulations.
The resource negotiation framework provides much lower service blocking rate under resource contention, provides service assurances even under large or bursty offered loads.
It provides higher perceived user benefit and higher network revenue
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

8. Protocol Architectures: Centralized (RNAP-C)
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.
In general, each NRN is in charge of admission control, monitoring network statistics, price quotation and charging for its domain.
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.
Edge Router
Access Domain - B
Internal Router
Intra-domain messages
Transit Domain
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

9. 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
In RNAP-D, Local Resource Negotiators (LRN) are implemented at each router. At network edge, LRNs dynamically configure traffic conditioners, based on on-going user requests.
Internal Router
Transit Domain
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

10. Close: Tears down negotiation session
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
We presents the messaging sequence between the users and network.
This sequence also works between two network domains.
If users do not need to find the service information, it could directly send
the reserve message, instead of sending Query and wait for
Quotation message first.
If a user request negotiation functionality from network, the network will
periodically provides the users with updated network status, allowing
users to modify their requests.
Commit
Close: Tears down negotiation session
Close
Release: Releases the resources
Release
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

11. Message Aggregation (RNAP-D)
Turn off router alert
Turn on router alert
If each flow needs a RNAP session, it can’t scale.
We consider a sink-tree based aggregation mechanism:
aggregate messages when senders share the same destination network
Messages merged by source or intermediate domains
At network edge, the router alert function is turned off. As a result, the original per-flow messages will be tunneled directly to the destination network, without interception by intermediate RNAP agents; aggregate message reserves and collects price at intermediate nodes/domains
Messages de-aggregated at destination border routers (RNAP-D), or NRNs (RNAP-C)
Overhead Reduction
Processing overhead, storage of states
Edge Routers
Sink-tree-based aggregation
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

12. Message Aggregation (RNAP-D)
Turn on router alert
Turn off router alert
Sink-tree-based aggregation
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

13. Message Aggregation (RNAP-C)
We use the RNAP-D as an example, the aggregation
process is similar for RNAP-C, but with the
NRN performing the aggregation and de-aggregation
NRN
Sink-tree-based aggregation
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

14. Block Negotiation (Network-Network)
Aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics
Bandwidth
In the above aggregation scheme, all the flows need
to be synchronized, which is not practical. Each flow arrival will modify the aggregation message, resulting high overhead.
In practice, we proposed to use the block negotiation mechanism: aggregated resources are added/removed in large blocks to minimize negotiation overhead and reduce network dynamics
time
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

15. Two Volume-based Pricing Strategies
Fixed-Price (FP): fixed unit volume price
During congestion: higher blocking rate OR higher dropping rate and delay
Congestion-dependent-Price (CP): FP + congestion-sensitive price component
During congestion: users have options to maintain service by paying more OR reducing sending rate OR switching to lower service class
Overall reduced rate of service blocking, packet dropping and delay
During resource contention, fixed price based frame work can only block the future arrivals or drop and delay the packets that have arrived.
If adaptive pricing is allowed, there are some other options for users: 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. As a result, the blocking rate and
average packet loss and delay of the whole network can get improved. We will show this later in our simulation.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

16. Proposed Pricing Strategies
Holding price and charge: based on cost of blocking other users by holding bandwidth even without sending data
phj =  j (pu j - pu j-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 (two mechanisms)
We proposed a pricing model, which comprise holding price, usage price and
congestion price:
The holding price is set to be proportional to the difference of the usage price of two neighboring service classes
The usage price is set to maximize the network revenue, under the constraint of network resources.
The congestion price is set to drive user demand to the target network resource utilization
We will consider the usage price and congestion price setup in more details.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

17. 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)
For an environment with multiple service classes, a class with lower target utilization level will provide higher QoS expectation, but it will also involve higher bandwidth cost
Assume bandwidth are fully used, and the basic price is pbasic; , assume the expected load of s service class is  j the price of a service class can be set to be reverse proportional to the target load of a service class
If we assume the fixed elasticity user demand, that is the user demand will be reverse proportional to the usage
price of the bandwidth, we can obtain the effective
bandwidth usage as: xe j ( puj ) = Aj / ( puj  j )
If we set the price so that the network revenue is optimized, we can obtain the basic bandwidth price
as … the price of a class is inverse proportional to the target load of the class, as we have seen earlier.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

18. Congestion Price: First Mechanism - Tatonnement
Tatonnement process (CPA-TAT):
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)
We consider two models for setting up the congestion price
In the tatonnement model, the congestion price is adjusted that the demand is driven to the supply. Generally, there is an upper limit of the congestion price.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

19. Congestion Price: Second Mechanism - M-bid Second-price Auction
Auction models in literature:
Assume unique bandwidth/price preference, one bid
Service uncertainty: user does not know about high demand until rejected
Other issues: setup delay, signaling burst, user response to auction results
M-bid auction Model
User bids (bandwidth, price) for a number of bandwidths, bids obtained by sampling utility function.
Reduce uncertainty
Network selects highest bids, charges highest rejected bid price
During high demand: lower bandwidth (higher price per unit bandwidth) bids get selected; more users served
Periodic auctions - support congestion control
Inter-auction admission to reduce setup delay
We proposed an M-bid auction model, which is particularly useful for adaptive applications, with multiple preferences for different bandwidth.
An application normally has higher value to the basic bandwidth, and hence would bid higher price for basic bandwidth.
During network contention, the higher price bids get selected, and hence the most valued bandwidth
get supported first, and hence more users could be
allocated with their valuable bandwidth
This model also support short-term resource auction, and
allows inter-auction admission to reduce setup delay:
as long as one bid from a user is higher than the recent
bidding price, the user’s request can get admitted
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

20. Example of M-bid Auction
Total capacity 70, congestion price is 2
Bid Bandwidth
Bid Price
Bidder
Bid Selection 5 10 1 10 2 4 15 1 4 20 3
3.5 25 2 3
Cutoff 2 30 3
Congestion Price
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

21. 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
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

22. 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
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

23. Two Rate-Adaptation Models
Model1: User adaptation under CPA-TAT (tatonnement-based pricing)
Optimize perceived surplus of the multimedia system subject to budget and application requirements
With the example utility functions, resource request of application i:
Without budget constraint: x i = i / pi
With budget constraint: x i = bi / pi, with b i = b ( i / Σl  k )
Model2: User adaptation under CPA-AUC (second-price auction)
Submit M-bid derived by sampling utility function; adapt rate based on allocated bandwidth/QoS
When the total resource requirements of all applications are within user’s budget, the resource allocated is set to be the user’s willingness to pay bandwidth
When the total resource requirements of all applications are beyond user’s budget, the budget for a system is first
distributed to each application based on users’ willingness
to pay for that application, and the allocated budget is used
to find the optimal resource allocation for an application
In the auction model, the network will allocate the resources based on the users’ bidding. The
application will adapt based the allocated price
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

24. Testbed Architecture
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 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
RAT
VIC
Mbus
HRN
RNAP
NRN
The main objective behind our testbed work was to demonstrate the functionality of our resource negotiation framework, including RNAP, pricing, and user adaptation. We were also able to demonstrate performance improvements relative to a non-adaptive, fixed-price environment in terms of blocking rate, average loss and delay, price stability, and perceived media quality .
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

25. Simulation Design Performance comparison:
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 load, admission control, traffic burstiness, 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
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

26. Simulation Architecture
Topology 1 (60 users)
Topology 2 (360 users)
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

27. Effect of Traffic Load Average packet loss Average packet delay
Without admission control,
we can see the delay and loss increase almost linearly when the load is beyond the target level, and the targeted service are seriously violated. If adaptive framework is used, all the targeted service are assured.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

28. Effect of Admission Control
Average packet delay
Average packet loss
With admission control the loss and delay can be maintained at target level even under high load.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

29. Average price and standard deviation
Effect of Admission Control (cont’d)
Average price and standard deviation
Blocking rate
This is at the cost of higher user blocking rate. Combining
admission control and user adaptation, the blocking rate can be greatly reduced (up to 40 times)
Compared to without admission control, the price
dynamics is reduced for the dynamic pricing scheme.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

30. Effect of Admission Control (cont’d)
Network revenue
Average user benefit
The network revenue is kept almost constant
when admission control is used, since the load above targeted level is blocked. The CPA framework gain much higher revenue since it serves the high valued customers.
Since we have using the example user utility function
that is sensitive to the loss and delay, the
average user benefit of the static service
drop sharply due to the higher loss and delay at high load.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

31. Effect of Traffic Burstiness
Average packet delay
Average packet loss
(Don’t need to explain this slide and next slide in detail to save time)
The trend are similar for burstiness case and
when allowing the user to shift to difference classes.
Even only 10% of users are encouraged to shift to different classes, the overall QoS level for a service class can be
maintained.
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

32. Load Balance Between Classes
Average packet delay
Average packet loss
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

33. Simulation Results
Congestion-price-based policy (CPA) + user adaptation vs Fixed price policy (FP) + no adaptation:
limit congestion
lower request blocking rate,
higher user satisfaction
higher network revenue
Differentiated service requires different target loads in each class
Even without admission control, CPA policy restricts load to targeted level, can meet service assurance
With admission control, blocking rate and price dynamics further reduced
Allowing service class migration allows for service assurance at predicted level and further stabilizes price
The simulation results indicate that the proposed negotiation framework can effectively limit congestion, provide lower request blocking rate, higher user satisfaction and higher network revenue
For providing some qualitative expectation of the service level, a class needs to restrict the load to the targeted
level, with admission control, or to encourage user
to back off.
……..
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

34. Conclusions
Proposed a dynamic resource negotiation framework: A Resource Negotiation And Pricing protocol (RNAP) , a rate and QoS adaptation model, and a pricing model
RNAP: Supports dynamic service negotiation between network and users, and between peer networks
Pricing models
Based on resources consumed by service class and long-term user demand, including congestion-sensitive component to motivate user demand adaptation during resource contention
M-bid Auction Model serves more users than comparable auction schemes, and reduces uncertainty of service availability
User adaptation: maximize perceived user satisfaction
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University

35. 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
11/28/2018
Xin Wang, Henning Schulzrinne, Columbia University
Xin Wang, Columbia University