End-to-end Congestion Management for the NGI

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End-to-end Congestion Management for the NGI

• Hari Balakrishnan
• MIT Laboratory for Computer Science
• http//nms.lcs.mit.edu/
• DARPA NGI PI Meeting
• October 2, 2000
• Srinivasan Seshan (CMU), Frans Kaashoek (MIT)
• Dave Andersen, Deepak Bansal, Dorothy Curtis,
  Nick Feamster

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iNAT Project Motivation

• Increasing heterogeneity in the Internet
• Nodes Mobiles, devices, sensors,...
• Links Optical, wireless,...
• Services applications Web, telepresence,
  streaming, remote device control
• Need a general solution for applications to
  discover resources and deal with mobility
• Need a general framework for learning about and
  adapting to changing network conditions

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iNAT Approach

• Intelligent naming
• Resource discovery Intentional Naming System
  (INS) using expressive names and self-configuring
  name resolver overlay network
• Mobility Via dynamic name updates and secure
  connection migration (check out demo!)
• Adaptive transmission
• End-system congestion management and adaptation
  framework for the NGI
• Congestion Manager software and algorithms

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The Problem

• End-to-end congestion management is essential
• Reacting when congestion occurs
• Probing for spare bandwidth when it doesnt
• The future isnt about just TCP!
• Many applications are inherently adaptive, but
  they dont adapt today
• Enable applications to learn about network
  conditions
• Many applications use concurrent flows between
  sender and receiver, which has adverse effects
• Enable efficient multiplexing and path sharing

Congestion Manager (CM) A new end-system
architecture for congestion management
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The Big Picture
HTTP
Video1
Audio
Video2
Per-macroflow statistics (cwnd,rtt,)
TCP1
TCP2
UDP
API
IP
Flows aggregated into macroflows to share
congestion state All congestion management tasks
performed in CM Apps learn and adapt using API
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CM Architecture
Receiver
Sender
Application (TCP, HTTP, RTP, etc.)
Application
API
Congestion Controller
Scheduler
Sharing macroflow bandwidth Deciding who can
send
Stable controls Deciding when to send
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Transmission API

• Traditional kernel buffered-send has problems
• Does not allow app to pull back data

cm_send( ,dst)
Lesson move buffering into the application
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Transmission API (cont.)

• Callback-based send

Schedule requests, not packets
Enables apps to adapt at the last instant
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Transmission API (cont.)

• Request API works for asynchronous sources
• while (some_event)
• get_data() / e.g., from a file, image
  capture, etc. /
• send_data() / call cm_request() and send on
  callback /
• But what about synchronous sources (e.g., audio
  at constant sampling rate)?
• do_every_t_ms / timer loop /
• get_data()
• send() / oops, waiting for send
  callback wrecks timing /
• Solution rate-change callback cmapp_update(newrat
  e)Sender then adapts packet size or timing rate

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Benefits of macroflow sharing

• Shared learning
• Avoids overly aggressive behavior
• Good for Internet stability and fairness
• Adoption incentives
• More consistent performance of concurrent
  downloads
• Avoids independent slow-starts and improves
  response times
• Beats persistent-connection HTTP on interactive
  performance by allowing parallel downloads

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CM Web Performance
TCP Newreno
Sequence number
With CM
Time (s)
CM greatly improves predictability and
consistency of downloads
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CM applications

• TCP over CM
• Congestion-controlled UDP
• HTTP server
• Uses TCP/CM for concurrent connections
• cm_query() to pick content formats
• SCTP Stream Control Transport Protocol
• Real-time streaming applications
• Synchronous API for audio (e.g., vat)
• Callback API for video (scalable MPEG-4 delivery
  system)

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Congestion Control for Streaming Applications

• CM provides a flexible framework for
  per-macroflow congestion control algorithms
• TCP-style additive-increase/multiplicative
  decrease (AIMD) is ill-suited for streaming media

MD causes large, drastic rate changes
Slow start

• Goal Smooth rate reductions

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TCP-friendliness

• Throughput vs. loss-rate equation for AIMD
• l ? K ? size / (sqrt(p) ? RTT)
• Important for safe deployment and competition
  with TCP connections
• Two different approaches
• Increase/Decrease rules
• Increase w(tR) ? I(w) e.g., w1 or 2w
• Decrease w(tdt) ? D(w), e.g., w/2
• Loss-rate monitoring (e.g., TFRC)
• Estimate loss rate, p, and set rate ? f(p)

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Binomial Algorithms

• I(w) and D(w) are nonlinear functions
• I w(tR) ? w a / wK
• D w(t dt) ? w - b wL
• Generalize linear algorithms
• AIMD (K0, L1) MIMD (K-1, L1)
• When L lt 1, reductions are smaller than
  multiplicative-decrease
• Are there interesting TCP-friendly binomial
  algorithms?

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The (K,L) space
I w(tR) ? w a / wK D w(t dt) ? w - b wL
Unstable (L gt 1)
L
1
0
K
Unstable (KL lt -1)
TCP-friendly KL 1
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Window Evolution
w(t)
AIMD
dw/dt a / (wK ?RTT)
Trade-off between increase aggressiveness and
decrease magnitude TCP-friendliness rule KL 1
t
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Binomial Algorithms Benefit Layered MPEG-4
Delivery
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CM Linux Implementation
App stream
cmapp_()
Stream requests, updates
libcm.a
User-level library implements API
Control socket for callbacks
System calls (e.g., ioctl)
UDP-CC
CM macroflows, kernel API
TCP
Congestion controller
Scheduler
Prober
ip_output()
ip_output()
cm_notify()
IP
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Server performance
CPU seconds for 200K pkts
cmapp_send()
Buffered UDP-CC
TCP, no delack
TCP/CM, no delack
TCP/CM, w/ delack
TCP, w/ delack
Packet size (bytes)
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Status

• CM Linux alpha code release this week
• http//nms.lcs.mit.edu/projects/cm/
• Sender-only CM soon to be up for proposed
  standard in IETF ECM working group
• WG document draft-ietf-ecm-cm-02.txt
• Mailing list ecm-request_at_aciri.org
• On-going work
• Evaluation of slowly responsive algorithms
• Macroflow formation for diffserv
• Congestion control vs. feedback frequency
• CM scheduler algorithms
• Using binomial algorithms on high-speed paths

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Summary

• Congestion Manager (CM) framework provides
  end-to-end adaptation platform for NGI
  applications and protocols
• CM enables
• Adaptive applications using application-level
  framing (ALF) ideas
• Efficient multiplexing and stable control of
  concurrent flows by sharing path information
• Per-macroflow congestion control algorithms,
  including binomial algorithms for streaming media
• Download it!