Xin Wang

1
Scalable Network Architecture Measurements
for Multicast and Adaptive QoS

• Xin Wang
• (Thesis Defense)
• Adviser Henning Schulzrinne
• Internet Real -Time Laboratory
• Columbia University
• http//www.cs.columbia.edu/xinwang

2
Scope of this Talk

• Main work
• Resource Negotiation, Pricing, and QoS for
  Adaptive Multimedia Applications
• Other work
• Measurements and Analysis of LDAP Performance
• IP Multicast Fault Recovery in PIM over OSPF

3
Resource Negotiation, Pricing and QoS
for Adaptive Multimedia Applications
4
Outline

• Introduction
• A Resource Negotiation And Pricing
  protocol RNAP
• Pricing models
• User adaptation models
• Test-bed demonstration of Resource Negotiation
  Framework
• Simulation and discussion of Resource Negotiation
  Framework
• Conclusion and future work

Resource Negotiation Framework
5
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.
6
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 charging scheme to support
  differentiated services
• 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?

7
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 allow network operator to create
  different trade-offs between blocking admissions
  and raising congestion prices

8
Related Work

• Signaling for Resource Reservation
• RSVP, YESSIR, SIBBS (Simple Inter-domain
  Bandwidth Broker Signaling protocol)
• Problems
• No support for service selection and negotiation
• No support for short-term resource commitment and
  dynamic resource negotiation
• Restricted to either sender-driven or
  receiver-driven
• No support for pricing and billing

9
Related Work (contd)

• Adaptive Internet Multimedia Applications
• Sender-driven, receiver driven, or
  transcoder-based
• Problems
• Fairness is one issue. TCP friendly adaptation
  may lead to unpleasantly abrupt changes in
  quality buffering smoothes the abrupt change at
  the cost of high delay
• No mechanism for rate adaptation in QoS-enhanced
  environment
• No motivation for user adaptation

10
Related Work (contd)

• Pricing and Billing in the Network
• Total user benefit maximization based on welfare
  theory
• problems rely on a centralized optimization
  process for total user utility maximization
  assume knowledge (users truthful revelation )
  of utility functions
• Pricing for congestion control
• problems rely on well-defined source statistical
  model not consider congestion control during
  sessions
• Pricing in multi-service environment
• problems Qdlyzko99 rely purely on pricing and
  user behaviors, without any service assurance
  Kumaran99 is restricted to special admission
  control algorithm

11
Related Work (contd)

• Auction Mechanism
• problems signaling bursts, set-up delay,
  uncertainty of connection availability, user
  response to fluctuations in price
• Signaling Support for Pricing and Charging
• Very limited work in this area
• Karsten98 estimated the cost for user request,
  no price quotation restricted to IntServ

12
Thesis Contributions

• Propose a Resource Negotiation And Pricing
  protocol RNAP
• Enables user and network (or two network domains)
  to dynamically negotiate multiple services
• Supports price and charge collation, auction bids
  and results distribution
• 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

13
Thesis Contributions (contd)

• Demonstrate negotiation framework on test-bed
  and simulation
• Show significant advantages relative to static
  resource allocation and fixed pricing using
  simulations

14
Outline

• Introduction
• A Resource Negotiation And Pricing
  protocol RNAP
• Pricing models
• User adaptation model
• Test-bed demonstration of Resource Negotiation
  Framework
• Simulation and discussion of Resource Negotiation
  Framework
• Conclusion and future work

Resource Negotiation Framework
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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
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Protocol Architectures Distributed (RNAP-D)
Local Resource Negotiator
RNAP Messages
HRN
LRN
LRN
LRN
LRN
LRN
LRN
LRN
LRN
HRN
LRN
LRN
LRN
Access Domain - A
LRN
LRN
Edge Router
Access Domain - B
Internal Router
Transit Domain
17
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
18
Message Aggregation (RNAP-D)
Turn on router alert
Edge Routers
Sink-tree-based aggregation
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Message Aggregation (RNAP-D)
Turn off router alert
Sink-tree-based aggregation
20
Block Negotiation (Network-Network)
Aggregated resources are added/removed in large
blocks to minimize negotiation overhead and
reduce network dynamics
Bandwidth
time
21
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
22
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

23
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 providers
  profit, constrained by resource availability
• max Sl 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 (tatonnement process or auction)

24
Usage Price for Differentiated Service

• Usage price based on cost of class bandwidth
• lower target load (higher QoS) -gt 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 Sl (Aj / pu j ) pu j - f (C), puj
  pbasic / ? j , s. t. Sl Aj / ( pu j ? j ) ? C
• Hence pbasic Sl Aj / C , puj Sl Aj /(C? j)

25
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)

26
Congestion Price Second Mechanism - M-bid
Second-price Auction

• Auction models in literature
• Assume unique bandwidth/price preference, one bid
  
• Service uncertainty does not know about high
  demand until rejected
• Other issues setup delay, signaling burst, user
  response to auction results
• M-bid auction Model
• Reduce uncertainty provide complete user
  preferences user bids (bandwidth, price) for a
  number of bandwidths, bids obtained by sampling
  utility function.
• Congestion price charges highest rejected bid
  price
• Bandwidth allocations higher valued bandwidth
  get allocated first more users served
• Congestion control auctions for period of time
• Reduce set-up delay inter-auction admission

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Example of M-bid Auction

• Total capacity 70, congestion price is 2

Bid Bandwidth
Bid Selection
Bid Price
Bidder
1
10
5
2
10
4
1
15
4
3
20
3.5
2
25
3
Cutoff
2
30
3
Congestion Price
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Outline

• Introduction
• A Resource Negotiation And Pricing
  protocol RNAP
• Pricing models
• User adaptation model
• Test-bed demonstration of Resource Negotiation
  Framework
• Simulation and discussion of Resource Negotiation
  Framework
• Conclusion and future work

Resource Negotiation Framework
29
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
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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

31
Rate-Adaptation Models

• Model1 User adaptation under CPA-TAT
  (tatonnement-based pricing)
• Gaining optimal transmission rate by optimizing
  perceived surplus of the multimedia system
  subject to budget and application requirements
• U Si Ui (xi (Tspec, Rspec)
• max Sl Ui (xi ) - Ci (xi) , s. t. Sl Ci (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, with b
  i b (? i / Sl ? k)
• Model2 User adaptation under CPA-AUC
  (second-price auction)
• Adapt rate based on allocated bandwidth/QoS

32
Outline

• Introduction
• A Resource Negotiation And Pricing
  protocol RNAP
• Pricing models
• User adaptation model
• Test-bed demonstration of Resource Negotiation
  Framework
• Simulation and discussion of Resource Negotiation
  Framework
• Conclusion and future work

Resource Negotiation Framework
33
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 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

VIC
RAT
Mbus
HRN
RNAP
NRN
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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
35
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

36
Simulation Architecture
Topology 1 (60 users)
Topology 2 (360 users)
37
Effect of Traffic Load
CPA maintains the traffic load at the targeted
level, meets the expected performance bounds
38
Effect of Admission Control
Admission control is important in maintaining the
expected performance of a class.
39
Effect of Admission Control
(contd)
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.
40
Effect of Admission Control
(contd)
CPA allows for higher network revenue and user
benefit.
41
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.
42
Other Results

• Users with different demand elasticity share
  bandwidth proportional to their willingness to
  pay
• Even a small proportion of adaptive users (e.g
  25) results in a significant performance
  improvement for the entire user population (18
  improvement)
• Performance of CPA further improves as the
  network scales and more connections share the
  resources
• Both M-bid auction and tatonnement process can be
  used to calculate the congestion price auction
  gives higher perceived user benefit and network
  utilization at cost of implementation complexity
  and setup delay

43
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

44
Future 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

45
Measurements and Analysis of
LDAP Performance

• Joint work with Dilip Kandlur, and Dinesh Verma
• (IBM Research)

46
Motivation and Experiment

• Lightweight Directory Access Protocol (LDAP)
  widely used, but little study of performance
• Related work Mindcraft98 treated LDAP server as
  black box, did not study the influence of system
  components
• Thesis contributions
• Developed a benchmark tool to analyze the
  performance of LDAP
• Provided guidelines for the configuration of
  LDAP client and server
• Suggested schemes for LDAP performance
  improvement
• Is the first effort that addressed the
  performance issues and configuration issues for
  the widely used LDAP
• Usage context for thesis management for network
  resources, pricing policies

47
Experimental Setup General Results

• Experimental setup
• Hardware server- dual Ultra-2 processors, 200
  MHz CPUs, 256 Mb memory Clients- Ultra1, 170 MHz
  CPU, 128 MB memory 10 Mb/s Ethernet
• LDAP server OpenLDAP 1.2, Berkeley DB 2.4.14
• Search filter interface address, and
  corresponding policy object
• Default parameters 10,000 entries, entry size
  488 bytes
• General results
• response latency 8 ms up to 105 requests/second
• Maximum throughput 140 requests/second
• 5 ms processing latency - 36 from backend, 64
  from front end
• Connect time dominates at high load, and limits
  the throughput

48
Scalability of the Performance

• Scaling with Directory Size - determined by
  back-end processing
• In memory operation, 10,000 -gt 50,000 processing
  time increases 60, throughput reduces 21.
• Out-of-memory, 50,000 -gt100,000 processing time
  increases another 87, and throughput reduces
  23.
• Scaling with Entry Size (488 -gt4880 bytes)
• In-memory, mainly increase in front-end
  processing, i.e., time for ASN.1 encoding .
  Processing time increases 8 ms, 88 due to ASN.1
  encoding, and throughput reduces 30.
  
• Out-of-memory, throughput reduces 70, mainly due
  to increased data transfer time.

49
Performance Improvement

• Disabling Nagle algorithm reduces latency about
  50 ms
• Entry Caching
• for 10,000 entry directory, caching all entries
  gives 40 improvement in processing time, 25
  improvement in throughput
• CPU
• For in-memory operation, dual processors improve
  performance by 40
• Connection Re-use
• 60 performance gain when connection left open

50
IP Multicast Fault Recovery
in PIM over OSPF

• Joint work with Chien-ming Yu (Microsoft)
• Paul Stirpe and Wei Wu (Reuters)

51
Motivations

• Many IP multicast applications require high
  availability, especially mission-critical
  real-time data
• A lot of work on reliable multicast, but little
  work on multicast fault tolerance
• Study failure recovery in a complete
  architecture IGMP OSPF (unicast) PIM
  (multicast)
• Focus the interplay of underlying protocols the
  interactions of failure recovery, between
  routers, links, WAN and LAN
• Method quantitative analysis simulation over
  OPNET study failure recovery and implementation
  issues on test-bed using Cisco routers

52
Results

• General observations
• Channel recovery time dominated by unicast table
  re-construction time.
• Protocol control loads
• PIM-DM control load increases proportionally with
  the redundancy factor and decreases inversely
  with the percentage of receivers
• Below certain time interval threshold, OSPF load
  is dominated by Hello messages and increases
  proportionally as OSPF Hello interval decreases
• Neither PIM nor OSPF has high control traffic
  during failure recovery.

53
Results (contd)

• PIM Enhancement for Fault Recovery
• Fast recovery from Dedicated Router (DR) failure
  reduce Hello-Holdtime to detect neighbor failure
  faster Backup DR IGMP group information caching
  in all LAN routers (reloading group membership
  information leads to minutes of delay)
• Fast recovery from last-hop router failure DR
  records the last-hop router address, actively
  recover, instead of waiting for an IGMP report
  to reactivate its oif to the LAN (up to minutes
  of delay)
• Use interrupts instead of polling to reduce delay

54
Some References

• X. Wang, H. Schulzrinne, Auction or Tatonnement
  - Finding Congestion Prices for Adaptive
  Applications, submitted.
• X. Wang, H. Schulzrinne, Pricing Network
  Resources for Adaptive Applications in a
  Differentiated Services Network, In Proceeding
  of INFOCOM'2001, April 22-26, Anchorage,
  Alaska.
• X. Wang, H. Schulzrinne, An Integrated Resource
  Negotiation, Pricing, and QoS Adaptation
  Framework for Multimedia Applications, IEEE
  JSAC, vol. 18, 2000. Special Issue on Internet
  QoS.
• X. Wang, H. Schulzrinne, Comparison of Adaptive
  Internet Multimedia Applications, IEICE
  Transactions on Communications, Vol. E82-B, No.
  6, pp. 806--818, June 1999.
• X. Wang, H. Schulzrinne, D. Kandlur, D. Verma,
  Measurement and Analysis of LDAP Performance,
  International Conference on Measurement and
  Modeling of Computer Systems (ACM
  SIGMETRICS'2000).
• X. Wang, H. Schulzrinne, C. Yu, P. Stirpe, W. Wu,
  IP Multicast Fault Recovery in PIM over OSPF,
  In 8th International Conference on Network
  Protocols (ICNP'00), 2000. Also appears at ACM
  SIGMETRICS2000 as short paper.

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Questions and Answers
Thanks !