1. Ph.D. Final Examination August 8, 2006
Analysis and Implementation of Multiplexing Techniques in Connection-Oriented Communication Networks
Ph.D. Final Examination
August 8, 2006
Tao Li
Department of Electrical and Computer Engineering
SEAS, University of Virginia

2. References
T. Li, D. Logothetis, M. Veeraraghavan, “Analysis of a polling system for telephony traffic with application to wireless LANs,” IEEE Transactions on Wireless Communications, vol. 5, pp , June 2006.
T. Li, M. Veeraraghavan, “Resource allocation for a polling system with application to wireless LANs,” to be submitted for journal publication.
H. Wang, M. Veeraraghavan, R. Karri, T. Li, “Design of a High-Performance RSVP-TE Signaling Hardware Accelerator,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 23, no. 8, pp , August 2005.
H. Wang, M. Veeraraghavan, R. Karri, T. Li, “Hardware-Accelerated Implementation of the RSVP-TE Signaling Protocol,” in Proc. of IEEE ICC2004, June 20-24, 2004, Paris, France.
Ph.D. Final Examination

3. Outline Background Problem statement and contributions
Study a polling system with vacations
Implementation of a signaling control card
Conclusions
Ph.D. Final Examination

4. Background
Applications have diverse Quality of Service (QoS) requirements (bandwidth, delay, loss, etc.)
deterministic QoS guarantees: mission-critical control
statistical QoS guarantees: most audio/video applications
No specific requirements: best-effort applications
Two types of networking technologies
Connectionless (CL): Internet, best-effort type of service
Connection-Oriented (CO): support of QoS
Circuit-switched networks: SONET, WDM, etc.
Packet-switched networks: ATM, MPLS, etc.
Ph.D. Final Examination

5. Architecture of a CO switch
Background (more)
Chief characteristics of CO networks
Resources are reserved prior to data transfer in a call admission control (CAC) phase
Resources are left idle during connection setup phase
Per-connection state maintenance at control-plane
How to reserve resources? – through signaling protocols
RSVP-TE, PNNI, SS7, etc.
Architecture of a CO switch
Ph.D. Final Examination

6. Background (more) In circuit-switched networks
Reserve a dedicated circuit for a connection
In packet-switched networks
Reserve bandwidth, buffer space, etc., for a connection
Data plane: packet classification, policing, scheduling, buffer management
How much resources should be reserved?
Depends on service model (hard QoS or soft QoS), traffic characteristics (burstiness), buffer size, scheduling algorithms
Ph.D. Final Examination

7. Background (more)
Multiplexing techniques in shared-medium based access
Connection-Oriented
Circuit-switched networks: FDMA, TDMA
Packet-switched networks: Polling, scheduling-based access
Connectionless
Random access
Ph.D. Final Examination

8. Problem statement Our mission:
Study a polling system for QoS provisioning
With application to IEEE
Target real-time application: telephony
A data-plane problem
Demonstrate that signaling protocols, can, in spite of their complexity, be implemented in hardware
Performance gain in terms of call-handling capacity and message process delay
A control-plane problem
Supported by NSF, DOE
Ph.D. Final Examination

9. Contributions Study of a polling scheme
CDF of delay in a single queue scenario
Assume a continuous-time Markov Modulated Fluid model
Can be used to approximate the CDF of delay in certain multiple-queue case
Voice capacity and delay bounds (deterministic service)
For the MMF model or a discrete-time Markov ON/OFF model
Allow heterogeneity
Voice capacity (statistical service)
MMF model: results obtained by simulations
Resource allocation (statistical service)
Assume a discrete-time Markov ON/OFF model
Derive approximations for tradeoff between service degradation measure (overflow probability, or packet loss ratio) and resource allocation
Ph.D. Final Examination

10. Contributions (more) Implementation of a signaling control card
Schematic design at a later stage
Power regulation module
Prior work completed by collaborators (Haobo Wang, Liji Wu)
Collaborated with Appli-CAD Inc. for PCB design
Provided a reference design for 1.25Gbps signal path
Examination of placement and route
Design or VHDL implementation of some functional modules
Configuration module, PCI interface module, FIFO interface unit, switch-fabric interface unit
Software design (device driver; contributed to a message generator)
Debugging (board and VHDL)
Ph.D. Final Examination

11. Overview Background Problem statement and contributions
Study of a polling system with vacations
Motivation and related work
System model
Analysis with a continuous-time MMF model
Analysis with a discrete-time Markov model
Implementation of a signaling control card
Conclusions
Ph.D. Final Examination

12. Motivation In CO mode: scheduling-based channel access
Several communication systems simultaneously support CO and CL modes of operation
IEEE
polling and random access
DOCSIS and IEEE
Extended Real-Time Variable Rate and Best-Effort services
In CO mode: scheduling-based channel access
Scheduler
downstream
upstream
Ph.D. Final Examination

13. Motivation (more)
Problem: queue status info is distributed among stations for the upstream direction
Instantaneous queue status not available to scheduler
Can not directly use scheduling algorithms that need arrival times, queue occupancy, or packet size
Continuous exchange of queue status info can be expensive
Wireless bandwidth is scarce
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14. Motivation (more) Polling emerges as a choice
Serve all queues in a round-robin order
does not require queue status information
Easy to implement: O(1) time complexity
Trade efficiency for timeliness (hard)
Transmission of a poll signal consumes bandwidth
If interpoll time bounded, delay also bounded
suitable for delay-sensitive applications, like telephony
Question: how many calls can be admitted? Or how much resource should be allocated for voice calls?
Ph.D. Final Examination

15. Related work Papers on general polling systems
Poisson arrival process; do not consider voice traffic
Papers on QoS provisioning in wired and wireless networks
Do not specifically address the polling scheme considered in our work
Papers on voice support over MAC protocols
Do not specifically address the polling scheme
Papers on voice support over IEEE polling mode
Largely simulation-based
Ph.D. Final Examination

16. System model Assume a superframe structure
Polling period: supports voice calls
Vacation period: other resource sharing schemes
Partition between polling and vacation: vacation is at least θ×TS
VS: vacation stretch
Frame VS
Polling period
Vacation
Vacation
Foreshortened
polling period
Superframe length: TS
Ph.D. Final Examination

17. System model (more) Polling order Walk time – Twalk
Round-robin with a restriction: each queue can be served at most once in a polling period
Walk time – Twalk
Time needed for the server to move from one queue to another; models physical and MAC layer overheads
Service discipline – gated-service
Pack all voice packets into one MAC frame when responding to a poll
Ph.D. Final Examination

18. Overview Analysis with a continuous-time MMF model Source model
Background
Problem statement and contributions
Study of a polling system with vacations
Motivation and related work
System model
Analysis with a continuous-time MMF model
Source model
Delay analysis in a single queue case
Multiple-queue analysis and simulation
Analysis with a discrete-time Markov model
Implementation of a signaling control card
Conclusions
Ph.D. Final Examination

19. Source model Markov Modulated Fluid model QoS requirements a
Continuous in time
a and b are transition rates
When ON, a bit stream is created at a constant-rate c; when OFF, silence
Average ON time: 352ms
Average OFF time: 650ms
May and Zebo 1968 model
QoS requirements
Stringent in delay
Can tolerate a small loss ratio ON
OFF a b
Ph.D. Final Examination

20. Delay analysis in a single queue case
Delay of interest: DW=DQ+DS
DQ: queueing delay
DS: service time, depends on service rate R and data size
DS=0: empty packet, not of interest
First, compute the PDF of TI given TI=TS+(stretch2 - stretch1)
Assume: stretch1 and stretch2 are i.i.d. R.V.s with known PDF
Second, compute P{DQ≤q|TI=t, nonempty packet}, and then obtain P{DQ≤q| nonempty packet} by unconditioning
Ph.D. Final Examination

21. Delay analysis (more) Third, compute P{Z≤z|DQ=q} and P{DS≤s|DQ=q}
Z: total time spent in the ON state during DQ
Can be solved with a uniformization technique
Z can be linked to DS by DS=Zc/R
c: source rate; R: service rate
Finally, combine all together, given DW=DQ+DS
P{DQ≤q| nonempty packet} obtained in the second step
P{DS≤s|DQ=q} obtained in the third step
Ph.D. Final Examination

22. All numerical results: assume IEEE 802.11b PHY
Delay analysis (more)
All numerical results: assume IEEE b PHY
CDF of DW with TS as a parameter
Twalk and C are set to 0.23ms and 8.5Kbps, respectively
Ph.D. Final Examination

23. Multiple-queue case Deterministic service
Each queue is guaranteed to be polled in a superframe
Number of queues N ≤ Np (voice capacity)
Referred to as small-N regime of operation
Statistical service (when N > Np)
Service degradation: not guaranteed to be polled in each superframe; statistical QoS guarantees
Statistical multiplexing gain since N > Np
Referred to as large-N regime of operation
Ph.D. Final Examination

24. Computation of Np: worst-case analysis
Polls in the kth interval: empty packets
Polls in the (k+1)th interval: maximum-sized packets
Vacation stretch: VSmax
Admission condition
Np can be computed iteratively
Delay bound DWmax,i
Ph.D. Final Examination

25. Delay in small-N regime of operation
Simulation results: CCDF of DW; θ, codec rate, and Twalk are set to 0.5, 64Kbps, and 0.23ms, respectively
Implication: delay analysis in the single queue case is a fair approximation, given the range of parameter values under consideration
Ph.D. Final Examination

26. Cost of large-N regime of operation
Simulation: CCDF of delay with N' as a parameter. TS, θ, and codec rate are equal to 30ms, 0.5, and 8.5Kbps, respectively.
Implication of delay spikes: use DWmax as delay threshold, and P{DW>DWmax} as performance measure (Ploss)
Ph.D. Final Examination

27. Statistical multiplexing
Codec rate, Twalk, and stretch are respectively set to 8.5Kbps, 0.23ms, and VSmax
Capacities increases with TS: payload size vs. Twalk
Multiplexing gain is small: large Twalk, small codec rate
Simulation results
Ph.D. Final Examination

28. Statistical multiplexing
Codec rate, Twalk, and stretch are respectively set to 64Kbps, 0.13ms, and VSmax
Multiplexing gain is significant: small Twalk, large codec rate
Small Twalk is attainable
Simulation results
Ph.D. Final Examination

29. Overview Background Problem statement and contributions
Study of a polling system with vacations
Motivation, related work
System architecture
Analysis with a continuous-time MMF model
Analysis with a discrete-time Markov model
Implementation of a signaling control card
Conclusions
Ph.D. Final Examination

30. Assume a discrete-time Markov model
Motivation
Voice traffic needs to be packetized for transmission in a packet-switched network
A discrete-time Markov model is more realistic
Tractability in analysis
Extend worst-case analysis for small-N regime of operation to discrete-time Markov model
We derive voice capacity Nl and delay bound Dbound
Details are omitted
Delay performance is studied through simulations
Ph.D. Final Examination

31. Resource allocation for large-N
Tsrv: the total time spent on N queues in a superframe
Performance criteria: overflow probability
The smallest x satisfying the above criteria is the amount of time that should be allocated for polling period, denoted as Tp(ε)
Difficulty in exact analysis of P{Tsrv}: correlation
Key approximation: correlation between DS,i, i=1,2,…,N, is small. Approximate DS,i, i=1,2,…,N, as i.i.d. R.V.s
Ph.D. Final Examination

32. Reference service discipline: serve 1, 2, 3, but not 4
Analytical approach
Consider a reference service discipline
Does not incur correlation between DS,i
Perform an exact analysis for this reference service discipline
View the results as approximations for the gated-service discipline
Reference service discipline: serve 1, 2, 3, but not 4
Ph.D. Final Examination

33. Analytical approach Other assumptions: TS=KL; synchronization
First, compute PK(m) for one queue
the probability of m arrivals in K time slots
Using a recursive approach
Then overflow probability
Computational complexity: O(NlogN) with FFT
Ph.D. Final Examination

34. Computation of loss ratio
If the waiting time is too long, packet will be dropped
Define loss ratio as Ploss=E{Nloss}/E{Ntotal}
Nloss : number of lost packets in a superframe
Ntotal : number of created packets in a superframe
Ploss can be linked to overflow probability
Ploss ≤ P{Tsrv>x}/PON, where PON is the probability of a voice source being in the ON state
This approximation of Ploss is not very accurate
Ph.D. Final Examination

35. Computation of loss ratio (more)
For the reference service discipline, an exact computation of Ploss is possible
Computational complexity
O(N2) with direct convolution
For Ω:
Ph.D. Final Examination

36. Numerical results The approximation of P{Tsrv>x} is satisfactory
Tp: polling period length
TS : 30ms
Dbound is set to TS+L+2ms
L: packetization interval, 10ms
Simulation: assume the gated-service discipline; drop the synchronization assumption; allow clock skew and phase error
The approximation of P{Tsrv>x} is satisfactory
Ploss can better approximate the “actual” loss ratio
Cost: computational complexity
Implication: Use P{Tsrv>x} as the QoS measure if computational complexity is a major concern
Ph.D. Final Examination

37. Overview Background Problem statement and contributions
Study of a polling system with vacations
Implementation of a signaling control card
Motivation, Related work, and Solution approach
System architecture, block diagram, and picture
Modules of the signaling control card
Performance
Conclusions
Ph.D. Final Examination

38. Motivation Signaling protocols Characteristics
Complex (parameters, timers, data-table lookups, keep state information)
Requirement for flexibility
Traditionally implemented in software
Call-handling capacities: 1K calls/second ~ 10K calls/second
Call-setup delay: in the order of hundreds of milliseconds
Sycamore SN16000 switch: per message processing delay 90ms
Ph.D. Final Examination

39. Motivation (more) Problems with software implementation
Call-setup delay impacts utilization
Hard to meet the requirement for high call-handling capacities in future CO networks
Objective: demonstrate that signaling protocols can be implemented in hardware in spite of their complexity
Reduce call-setup delay by at least two-to-three orders of magnitude
Increase call-handling capacity significantly
Target signaling protocol and switch
RSVP-TE with extensions for GMPLS
SONET switch
Ph.D. Final Examination

40. Related work TCP offloading engine Software implementations of RSVP-TE
Observation: Overhead of TCP/IP processing overwhelms server’s CPU
Solution: Moving TCP/IP processing to a dedicated h/w
Software implementations of RSVP-TE
E.g.: Sycamore SN16000 switch with a per-message processing-delay of about 90ms
Ph.D. Final Examination

41. Solution approach Manage the complexity of signaling protocols
By only supporting basic and most frequently used messages/parameters in hardware and relegating the rest to software
Define a subset of the signaling protocol for hardware implementation (RSVP-TE with extensions for GMPLS)
Four messages related to connection setup and release: Path, Resv, PathTear, and ResvTear
Support all mandatory objects/parameters and optional parameters needed for SONET switch
Ph.D. Final Examination

42. Solution approach (more)
Meet the flexibility requirement
using reconfigurable Field Programmable Gate Array
FPGA can be reloaded with updated versions
Achieve fast data-table lookups and state maintenance by using Ternary Content Addressable Memory (TCAM)
TCAM: a special memory device designed for data-table lookups
Complexity of a lookup operation: one clock cycle
Ph.D. Final Examination

43. System architecture Focus on signaling control card
Backplane: often proprietary. We assume PCI bus.
Switch fabric card: assume Vitesse 64x64 STS-12
Cross-connection rate: STS-1 (51.8Mbps), total bandwidth: 40Gbps
Ph.D. Final Examination

44. Block diagram of implementation
Optical fiber
PCI bus
Gbit Ethernet
module
Hardware signaling
accelerator
PCI interface
module
5v, 3.3v
5v, 3.3v
Power regulation
module
Configuration
module
1.5v, 1.8v, 2.5v
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45. Top view of the card
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46. Gigabit Ethernet module
Optical-fiber transceiver: convert between optical signals and differential PECL signals
SerDes: convert between serial PECL signals to parallel TTL signals
Ethernet controller: 8B/10B encoding/decoding, MAC layer operations
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47. Hardware signaling accelerator module
Hardware signaling accelerator core: all major functions such as message parsing, creating commands for route lookup, state maintenance, and switch-fabric programming, etc.
MAC/Switch fabric/FIFO/TCAM_SRAM interface units: data path, control/timing signals
FIFO: temporary storage of unsupported signaling messages
TCAM/SRAM: Route lookup operation, state maintenance operations
Ph.D. Final Examination

48. PCI interface module
CPU card interface unit: move messages from FIFO to host memory space through Direct Memory Access (DMA)
Switch-fabric control unit: transmit programming command using DMA
Access arbiter: give switch-fabric control unit higher priority
Configuration interface unit: facilitate management of the card
PCI core: provide commonly used functions for PCI accessing
Ph.D. Final Examination

49. Configuration Module
Enable configuration of MAC address, IP addresses, routing table and other data tables
Initialize the GbE controller, SRAM, and TCAM
Create clock and control signals needed for each device
Ph.D. Final Examination

50. Performance Call-handling capacity Processing delay
400K calls/second (Hardware signaling accelerator module)
Software-based implementation: 1K~10K calls/second
250K calls/second, limited by the 1Gbps link rate
Load on the TCAM: about 6%
Processing delay
Per-message processing delay ≤ 2.4 microsecond
Sycamore SN16000 switch: ≈ 90 ms
Ph.D. Final Examination

51. Performance (more) Concurrent connections
64 ports, each consisting of 12 STS-1 circuits
768 connections (total data rate: 768 × 51.8Mbps ≈ 40Gbps)
Maintaining state for 768 connections consumes 1/32 of TCAM’s memory space
Better performance can be obtained in future implementation
Call-handling capacity, processing delay, number of concurrent connections
Ph.D. Final Examination

52. Performance (more)
Define per-call utilization ratio as : U=Tfile/(Tfile+Tsetup), where Tsetup = Tprocessing+Tpropagation&emission
Condition: U ≥ x %
Assume: Tpropagation&emission is fixed
Assume: software-based Tprocessing >> hardware-based Tprocessing
Operational region with
zero processing delay (ideal)
Circuit
rate
Operational region with
hardware implementation
Operational region with
software implementation
Average file size:
Determined by call-handling capacity
Avg. file
size for h/w
Avg. file
size for s/w
File size
Ph.D. Final Examination

53. Summary Developed analytical models for polling-based access scheme
For delay performance, deterministic service, and statistical service
Can be used for CAC
Limitations: simple traffic model; no consideration for channel variations; polling can be inefficient if traffic is extremely bursty (e.g., connection request)
Implemented a subset of RSVP-TE with extensions for GMPLS in hardware
2-3 orders of performance gain in magnitude
Enable circuit-switched networks to efficiently support a wider range of applications
Ph.D. Final Examination

54. Questions?
Thank you!
Ph.D. Final Examination