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End-to-end Congestion Management for the NGI

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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End-to-end Congestion Management for the NGI

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  1. 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

  2. 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

  3. 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

  4. The Problem • End-to-end congestion management is essential • Reacting when congestion occurs • Probing for spare bandwidth when it doesn’t • The future isn’t about just TCP! • Many applications are inherently adaptive, but they don’t 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

  5. Congestion Manager The Big Picture HTTP Audio Video1 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

  6. feedback cm_update(feedback) API Hints Dispatch Responder Congestion Detector Prober CM protocol CM Architecture Sender Receiver Application (TCP, HTTP, RTP, etc.) Application API Congestion Controller Scheduler Sharing macroflow bandwidth Deciding who can send Stable controls Deciding when to send

  7. App Rate change CM Can’t pull out and re-encode IP Packets queued to dst 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

  8. App send( ) cm_request() cmapp_send() based on allowed rate CM IP Transmission API (cont.) • Callback-based send Schedule requests, not packets Enables apps to adapt “at the last instant”

  9. 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

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

  11. 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)

  12. Window Time 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

  13. 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(t+R)  I(w); e.g., w+1 or 2w • Decrease: w(t+dt)  D(w), e.g., w/2 • Loss-rate monitoring (e.g., TFRC) • Estimate loss rate, p, and set rate  f(p)

  14. Binomial Algorithms • I(w) and D(w) are nonlinear functions • I: w(t+R)  w + a /wK • D: w(t+ dt)  w - b wL • Generalize linear algorithms • AIMD (K=0, L=1); MIMD (K=-1, L=1) • When L < 1, reductions are smaller than multiplicative-decrease • Are there interesting TCP-friendly binomial algorithms?

  15. MIMD AIMD Less aggressive than AIMD (K+L > 1) SQRT (K=L=0.5) MIAD AIAD IIAD (K=1, L=0) More aggressive than AIMD (-1 < K+L < 1) The (K,L) space • I: w(t+R)  w + a /wK • D: w(t+ dt)  w - b wL Unstable (L > 1) L 1 0 K Unstable (K+L < -1) TCP-friendly K+L = 1

  16. Window Evolution w(t) AIMD dw/dt = a / (wK RTT) Trade-off between increase aggressiveness and decrease magnitude TCP-friendliness rule: K+L = 1 t

  17. Binomial Algorithms Benefit Layered MPEG-4 Delivery

  18. CM Linux Implementation App stream Stream requests, updates cmapp_*() libcm.a User-level library; implements API System calls (e.g., ioctl) Control socket for callbacks UDP-CC TCP CM macroflows, kernel API Congestion controller Scheduler Prober ip_output() ip_output() cm_notify() IP

  19. 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)

  20. 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@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

  21. 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!

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