1. Data Center Transport Mechanisms Joint work with: Mohammad Alizadeh, Berk Atikoglu and Abdul Kabbani, Stanford University Ashvin Lakshmikantha, Broadcom Rong Pan, Cisco Systems Mick Seaman, Chair, Security Group; Ex-Chair, Interworking Group, IEEE 802.1 Balaji Prabhakar Departments of Electrical Engineering and Computer Science Stanford University

2. 2 What are Data Centers? Large enterprise networks; convergence of – High speed LANs: 10, 40, 100 Gbps Ethernet – Storage networks: Fibre Channel, Infiniband Related idea: Cloud Computing – Outgrowth of high-performance computing networks with integrated storage and server virtualization support Driven by – Economics: One network, not many Low capex and opex – Economics: Server utilization Resource pooling, virtualization, server migration, high-speed interconnect fabrics – Savings in power consumption Unified management of network of servers allows server and job scheduling – Security Storage and processing of data within a single autonomous domain

3. 3 Large networks of servers, storage arrays, connected by a high-performance network Origins – Clusters of web servers Web hosting – High performance computing: Cloud computing Servers, storage Overview of a Data Center N-Tiers of Servers (Web, App, Database) Data Storage NAS & File Storage FC IP Front End Networks (Security & Load Balancing) VPN Firewall IDS Load Balancing Data Center Disk and Tape IB Key drivers – Convergence of Layer 2 neworks Swtiched Ethernet (LANs) and Storage Area Networks (SANs): FCoE – Server virtualization VMs, VM migration

4. A brief overview of the relevant congestion control background A description of the QCN algorithm and its performance The Averaging Principle: A control-theoretic idea underlying the QCN and BIC-TCP algorithms which stabilizes them when loop delays increase; very useful for operating high-speed links with shallow buffers---the situation in 10+ Gbps Ethernets Rest of the Talk

5. 5 Why do Congestion Control? Congestion: Transient: Due to random fluctuations in packet arrival rate Handled by buffering packets, pausing links (IEEE 802.1bb) Sustained: When link bandwidth suddenly drops or when new flows arrive Switches signal sources to reduce their sending rate: IEEE 802.1Qau Congestion control algorithms aim to – Deliver high throughput, maintain low latencies/backlogs, be fair to all flows, be simple to implement and easy to deploy Congestion control in the Internet: Rich history of algorithm development, control-theoretic analysis, deployment – Jacobson, Floyd et al, Kelly et al, Low et al, Srikant et al, Misra et al, Katabi, Paganini, et al

6. 6 A main issue: Stability Stability of control loop – Refers to the non-oscillatory behavior of congestion control loops If the switch buffers are short, oscillating queues can overflow (and drop packets) or underflow (lose utilization) In either case, links cannot be fully utilized, throughput is lost, flow transfers take longer

7. 7 TCP--RED: A basic control loop TCP TCP: Slow start + Congestion avoidance Congestion avoidance: AIMD No loss: increase window by 1; Pkt loss: cut window by half min th max th q avg p RED: Drop probability, p, increases as the congestion level goes up

8. TCP Dynamics Time Congestion Window ~ Rate Congestion message recd Cwnd Cwnd/2

9. 9 TCP--RED: Analytical model RED Control Time Delay - 1/R p C TCP Control - q

10. 10 TCP--RED: Analytical model W: window size; RTT: round trip time; C: link capacity q: queue length; q a : ave queue length p: drop probability Users: Network: *By V. Misra, W. Dong and D. Towsley at SIGCOMM 2000 *Fluid model concept originated by F. Kelly, A. Maullo and D. Tan at Jour. Oper. Res. Society, 1998

11. 11 TCP--RED: Stability analysis Given the differential equations, in principle, one can figure out whether the TCP--RED control loop is stable However, the differential equations are very complicated – 3rd or 4th order, nonlinear, with delays – There is no general theory, specific case treatments exist “Linearize and analyze” – Linearize equations around the (unique) operating point – Analyze resultant linear, delay-differential equations using Nyquist or Bode theory End result: – Design stable control loops – Determine stability conditions (RTT limits, number of users, etc) – Obtain control loop parameters: gains, drop functions, …

12. 12 Instability of TCP--RED As the bandwidth-delay-product increases, the TCP--RED control loop becomes unstable Parameters: 50 sources, link capacity = 9000 pkts/sec, TCP--RED Source: S. Low et. al. Infocom 2002

13. Feedback Stabilization Many congestion control algorithms developed for “high bandwidth- delay product” environments The two main types of feedback stabilization used are: 1.Determine lags (round trip times), apply the correct “gains” for the loop to be stable (e.g. FAST, XCP, RCP, HS-TCP) 2.Include higher order queue derivatives in the congestion information fed back to the source (e.g. REM/PI, XCP, RCP) We shall see that BIC-TCP and QCN use a different method which we call the Averaging Principle – BIC (or Binary Increase) TCP is due to Rhee et al – It is the default congestion control algorithm in Linux – No control theoretic analysis, until now

14. Quantized Congestion Notification (QCN): Congestion control for Ethernet

15. Ethernet vs. the Internet Some significant differences … 1.No per-packet acks in Ethernet, unlike in the Internet Not possible to know round trip time or lags! So congestion must be signaled to the source by switches Algorithm not automatically self-clocked (like TCP) 2.Links can be paused; i.e. packets may not be dropped 3.No sequence numbering of L2 packets 4.Sources do not start transmission gently (like TCP slow-start); they can potentially come on at the full line rate of 10Gbps 5.Ethernet switch buffers are much smaller than router buffers (100s of KBs vs 100s of MBs) 6.Most importantly, algorithm should be simple enough to be implemented completely in hardware – Note: QCN has Internet relatives---BIC-TCP at the source and the REM/PI controllers

16. Data Center Ethernet Bridging: IEEE 802.1Qau Standard A summary of standards effort 1.Everybody should do it at least once Like proving limit theorems in Probability But, in this case, no more than once!? 2.Intense, fun activity Broadcom, Brocade, Cisco, Fujitsu, HP, Huawei, IBM, Intel, NEC, Nortel, … Conference calls every Thursday morning Meeting every 6 weeks (Interim and Plenary) 3.Real-time engineering: Tear and re-build Our algorithm was the 4 th to be proposed It underwent 5—6 revisions because of being “subjected to constraints” Draft of standard: 9 revs

17. QCN Source Dynamics Time Rate Congestion message recd TCP – AIMD Time Rate Current Rate Congestion message recd Rd Rd/2 Rd/4 Rd/8 Target Rate CR TR BIC-TCP and QCN

18. Stability: AIMD vs QCN AIMD QCN RTT = 50 μs RTT = 300 μs

19. Experiment & Simulation Parameters Baseline scenario – Output-queued switch – OG hotspot; hotspot severity: 0.2Gbps, hotspot duration ~3.5sec – Vary RTT: 100us to 1000us 0.95 G 0.2 G NIC 1 NIC 2

20. 1 source, RTT = 100μs HardwareOMNET++

21. 1 source, RTT = 1ms HardwareOMNET++

22. 8 sources, RTT = 1ms HardwareOMNET++

23. 23 Fluid Model for QCN Assume N flows pass through a single queue at a switch. State variables are TR i (t), CR i (t), q(t), p(t). FbFb P = Φ(F b ) 10% 63

24. Accuracy: Equations vs ns2 sims

25. QCN Notes The algorithm has been extensively tested in deployment scenarios of interest – Esp. interoperability with link-level PAUSE and TCP – All presentations and p-code are available at the IEEE 802.1 website: http://www.ieee802.org/1/pages/dcbridges.html http://www.ieee802.org/1/files/public/docs2008/au-rong-qcn-serial-haipseudo-code%20rev2.0.pdf The theoretical development is interesting, but most notably because QCN and BIC-TCP display strong stability in the face of increasing lags, or, equivalently in high bandwidth-delay product networks While attempting to understand the unusually good performance of these schemes, we uncovered a method for improving the stability of any congestion control scheme

26. The Averaging Principle

27. The Averaging Principle (AP) A source in a congestion control loop is instructed by the network to decrease or increase its sending rate (randomly) periodically AP: a source obeys the network whenever instructed to change rate, and then voluntarily performs averaging as below TR = Target Rate CR = Current Rate

28. A Generic Control Example As an example, we consider the plant transfer function: P(s) = (s+1)/(s 3 +1.6s 2 +0.8s+0.6)

29. Step Response Basic AP, No Delay

30. Step Response Basic AP, Delay = 8 seconds

31. Step Response Two-step AP, Delay = 14 seconds

32. Step Response Two-step AP, Delay = 25 seconds Two-step AP is even more stable than Basic AP

33. Applying AP to RCP (Rate Control Protocol) RCP due to Dukkipatti and McKeown Basic idea: Network computes max-min flow rates for each flow. – Rate computed every 10 msecs Flows send at their advertised rate Apply the AP to RCP

34. AP-RCP Stability RTT = 60 msec RTT = 65 msec

35. AP-RCP Stability cont’d RTT = 120 msec RTT = 130 msec

36. AP-RCP Stability cont’d RTT = 230 msec RTT = 240 msec

37. Understanding the AP As mentioned earlier, the two major flavors of feedback compensation are: 1.Determine lags, chose appropriate gains 2.Feedback higher derivatives of state We prove that the AP is sense equivalent to both of the above! – This is great because we don’t need to change network routers and switches – And the AP is really very easy to apply; no lag-dependent optimizations of gain parameters needed

38. AP Equivalence Systems 1 and 2 are discrete-time models for an AP enabled source, and a regular source respectively. Theorem: Systems 1 and 2 are algebraically equivalent. That is, given identical input sequences, they produce identical output sequences. Source does AP Fb Regular source 0.5 Fb + 0.25 T dFb/dt

39. AP vs Equivalent PD Controller No Delay

40. AP vs PD Delay = 8 seconds

41. Conclusions We have seen the background, development and analysis of a congestion control scheme for the IEEE 802.1 Ethernet standard The QCN algorithm is – More stable with respect to control loop delays – Requires much smaller buffers than TCP – Easy to build in hardware The Averaging Principle is interesting; we’re exploring its use in nonlinear control systems