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Volker%20Hilt

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Eric Noel, Carolyn Johnson (AT&T Labs) Volker Hilt, Fangzhe Chang (Bell Labs/Alcatel-Lucent) ... Underload: 114 cps. Overload: 500 cps, 1000 cps. Results ... – PowerPoint PPT presentation

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Title: Volker%20Hilt


1
SIP Overload ControlIETF Design Team Status
  • Volker Hilt
  • volkerh_at_bell-labs.com
  • Bell Labs/Alcatel-Lucent

2
SIP Overload Control Design TeamCurrent Status
  • Team Members
  • Eric Noel, Carolyn Johnson (ATT Labs)
  • Volker Hilt, Fangzhe Chang (Bell
    Labs/Alcatel-Lucent)
  • Charles Shen, Henning Schulzrinne (Columbia
    University)
  • Ahmed Abdelal, Tom Phelan (Sonus Networks)
  • Mary Barnes (Nortel)
  • Jonathan Rosenberg (Cisco)
  • Nick Stewart (British Telecom)
  • Four independent simulation tools
  • ATT Labs, Bell Labs/Alcatel-Lucent, Columbia
    University, Sonus Networks
  • Bi-weekly conference calls.

3
draft-ietf-sipping-overload-design-00Status and
Changes
  • Status
  • Submitted as SIPPING WG item.
  • Changes to draft-hilt-sipping-overload-design-00
  • Numerous clarifications throughout the text
  • Added separate section for problem description
  • Added an overload control mechanism
  • Overload signal-based overload control
  • Discussion of implicit overload control in
    separate section.

4
draft-ietf-sipping-overload-design-00Next Steps
  • Document investigates design choices and models.
  • Document is stable.
  • Simulation results are not included.
  • Ready for WGLC?

5
Simulation Results Overload Control Feedback
Types
  • Rate-based Overload Control
  • Limit the request rate a server receives from an
    upstream element.
  • Feedback X requests per second.
  • Loss-based Overload Control
  • Reduce the request rate a server receives by a
    percentage X.
  • Feedback reduce load by X.
  • Window-based Overload Control
  • Limit the number of request a server can receive
    without confirming a request.
  • Feedback send X more requests.
  • Overload Signal-based Overload Control
  • De-/increase of request rate until a target
    overload notification rate is reached.
  • Feedback server overloaded (503 response without
    RetryAfter header)

6
Simulation ResultsNo Overload Control
  • Simulation Setup
  • Evaluate SIP overload performance under varying
    network conditions.
  • Network delay 0 2 sec
  • Network loss 0 50
  • Topology consists of 5 edge proxies and 2 core
    proxies.
  • Overall capacity 142 cps
  • Three levels of offered load
  • Underload 114 cps
  • Overload 500 cps, 1000 cps
  • Results
  • Delay and loss substantially decrease goodput
    even when there is no overload.
  • Confirmed by all simulators (ATT Labs, Bell
    Labs/Alcatel-Lucent, Columbia University, Sonus
    Networks)

7
Simulation ResultsUpper Bound Estimation
  • Delay and loss triggers retransmissions.
  • Increases the number of SIP messages needed to
    set up a call.
  • Causes the goodput of a server drops.
  • Upper bound estimation
  • Determine increase in message count caused by
    delay/loss.
  • Example topology UAC proxy - UAS
  • Calculate resulting goodput.
  • Example result 125 ms delay
  • Round trip delay exceeds 500ms.
  • One 200 OK/ACK retransmission and one BYE/200 OK
    retransmission triggered.
  • Messages per call increases from 7 to 11.
  • Contributor ATT Labs

8
Simulation ResultsSelected Results on Overload
Control (1)
Rate-based Control
Loss-based Control
ATT Labs
Bell-Labs/Alcatel-Lucent
9
Simulation Results Selected Results on Overload
Control (2)
Overload Signal-based Control
Window-based Control
Sonus Networks
Columbia University
10
Conclusions and Next Steps
  • Conclusions
  • Large delay and loss rates can cause the goodput
    to drop substantially even in non-overloaded
    conditions.
  • Performance of all overload control mechanisms
    under evaluation is similar in steady state.
  • Varying network conditions do not reveal
    significant differences.
  • Next Steps
  • Evaluate additional scenarios.
  • Impact of different load distributions
  • Impact of varying upstream neighbor counts
  • Transient simulations.
  • Evaluate dynamic behavior of overload control
    mechanisms
  • Transition in/out of overload, impact of load
    peaks, etc.
  • Evaluate fairness of overload control mechanisms.

11
SIP Overload Control Design TeamPublications
  • E. Noel, C. Johnson, Initial Simulation Results
    That Analyze SIP Based VoIP Networks Under
    Overload, International Teletraffic Congress
    (ITC07), Ottawa, Canada, June 2007.
  • C. Shen, H. Schulzrinne, E. Nahum, Session
    Initiation Protocol (SIP) Server Overload
    Control Design and Evaluation, Principles,
    Systems and Applications of IP Telecommunications
    (IPTComm08), Heidelberg, Germany, July 2008.
  • V. Hilt, I. Widjaja, Controlling Overload in
    Networks of SIP Servers, IEEE International
    Conference on Network Protocols (ICNP08),
    Orlando, Florida, October 2008.
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