Improving TCP Performance in a Differentiated Services Network: Investigations and Solutions

1. Improving TCP Performance in a Differentiated Services Network:Investigations and Solutions Kaleelazhicathu R R Kumar Centre for Internet Research School Of Computing National University of Singapore

2. Outline • Introduction • Motivation • Background • TCP • DiffServ • TCP in DiffServ : Issues • Related work • Research Objective • Thesis Contribution • Memory-Based Marker (MBM) • Memory-Based Three Color Marker (MBTCM) • TSWTCM vs. MBTCM : A Comparison • Congestion-Aware Traffic Conditioner (CATC) • Linux Implementation of MBM • Areas for Deployment • Conclusion

3. Introduction: Motivation • An exponential growth in traffic resulted in deterioration of QoS. • Over provisioning of networks could be a solution. • A better solution: An intelligent network service with better resource allocation and management methods. • DiffServ has emerged as a solution for providing QoS by service differentiation. • Recent measurements have shown TCP flows being in majority (95% approx. of byte share). • Inherent limitations of TCP is a hurdle for providing better QoS.

4. Background: Transmission Control Protocol (TCP) • De facto Transport layer Protocol • Common applications like Telnet,FTP and HTTP uses TCP • Provides a connection oriented,reliable, byte stream service to the application. • Control mechanisms • Slow start, Congestion Avoidance, Fast Retransmit and Recovery • Flow Control

5. Differentiated Services • Provides QoS for aggregate flows using different per hop forwarding behaviours at the core routers • Scalable • The philosophy: simpler at the core (AQM), complex at the edges • Per-Hop behaviours • Expedited forwarding: Deterministic QoS • Assured forwarding: Statistical QoS • RIO-based schemes proposed for AQM.

6. Differentiated Services • Basic building blocks of DiffServ • Classifier • Traffic Conditioner • Token Bucket (TB), Time Sliding Window (TSW) • Meter • Marker • Shaper/Dropper

7. Differentiated Services Meter Packets Forward Classifier Marker Shaper/ Dropper Drop Logical View of a Packet Classifier and Traffic Conditioner

8. TCP in DiffServ: Issues • TCP flows are much more sensitive to transient congestion. • Bias against connections with long Round Trip Time (RTT) • Reason:Long RTT flows takes longer time to ramp up. • Bias against connections with smaller window sizes • Reason: Smaller windows mean smaller throughput • Protection from unruly traffic like UDP traffic. • Reason: UDP traffic has no rate control mechanism and hence kills TCP traffic. • DiffServ issues • Bandwidth assurance affected by size of target rate. • Markers sensitive to its own parameters. • Absence of Edge-to-Edge feedback. • Existing markers fail to track TCP dynamics

9. Related Work • Clark et al came up with the RIO scheme. • Nandy et al identified the factors affecting bandwidth assurance. • Kalyanaraman et al proposes a TCP-Friendly component. • Kalyanaraman et al also proposed an edge-to-edge feedback architecture based on ECN and ICMP messages. • Sahu et al studied the influence of token bucket parameters on providing assured service. • Feng et al proposed an adaptive marker.

10. Research Objective • Markers, one of the building blocks of a traffic conditioner play a major role for resource allocation in a DiffServ network. • Design an Intelligent Marker • Least sensitive to both the marker and TCP parameters • Should be transparent to end hosts. • Maintain optimum marking • Tracks the TCP dynamics • Minimize synchronizations. • Be fair to different target sizes. • Be congestion aware. • Design an Edge-to-Edge feedback architecure • An early indication of congestion in a network helps to prioritize the packets in advance.

11. Thesis Contribution • Two Approaches • Memory-Based • Memory-Based Marker (MBM) • Reduces influence of TCP’s limitations • Memory-Based Three Color Marker (MBTCM) • Suitable for DiffServ with AF PHB • Feedback-based • Congestion-aware Traffic Conditioner • Provides edge-to-edge feedback to the marker

12. Memory-Based Marker (MBM) • Tracks TCP dynamics. • Transparent to end hosts. • Maintain optimum marking. • Fairness • Less sensitive to its own parameters.

13. Memory-Based Marker? • During the period when TCP flows experience congestion, either or both of the following occurs: a) The cwnd reduces reducing the value of W b) The RTT increases causing a decrease in throughput or rate of flow. • The TCP window size W and the round trip time RTT are related to the throughput by the equation: BW = ¾*(MSS*W)/(RTT) where W is expressed in number of segments. • Any variation in W or RTT is reflected as subsequent changes in BW, i.e., in our case, the avg_rate. • The parameter previous average rate (par) is compared with the present average rate to track any change in the rate of flow and thus indirectly extract the variations in RTT or W.

14. MBM Algorithm else if avg_rate > cir then mp= mp + (par – avg_rate)/avg_rate; par=avg_rate; mark the packet using: cp 11 w.p. mp cp 00 w.p. (1-mp) • For each packet arrival If avg_rate  cir then mp=mp+(1-avg_rate/cir)+ (par- avg_rate)/avg_rate; par = avg_rate; mark the packet using: cp 11 w.p. mp cp 00 w.p. (1-mp)

15. MBM Algo. Cont’d.. • where, avg_rate= the rate estimate upon each packet arrival mp = marking probability (1) cir = committed information rate (i.e., the target rate) par = previous average rate cp denotes ‘codepoint’ and w.p. denotes ‘with probability’.

16. MBM Algo. Explained • In the expression for the marking probability mp, • (par – avgrate)/avgrate tracks the variations in RTT and window size (W) and thus increases or decreases the marking probability according to the changes in the flow rate. • (1- avgrate)/cir constantly compares the average rate observed with the target rate to keep the rate closer to the target.

17. Experiments • We used FTP bulk data transfer for the TCP traffic in all our experiments. • NS (2.1b7a) simulator on Red Hat 7.0 • Modified Nortel’s DiffServ module for our architecture implementation. • Core routers use RIO like mechanism • We conducted simulation studies for: • Assured service for aggregates with different target rates. • Effect of different RTTs • Effect of different window sizes • Protection from best effort UDP flows • Effect of UDP flows with target rates.

18. Topology

19. Results Achieved Rates (Ra) for different Target Rates (Rt).

20. Results.. Achieved Rates (Ra) for different RTT values Achieved Rates (Ra) for different window sizes

21. Results.. Achieved Rates in presence of BE UDP and TCP Achieved Rates in presence of AS UDP and BE TCP

22. Inference • MBM • Achieves transparency from the end hosts, simplicity, and least sensitivity to parameters of both TCP as well as its own parameters. • helps in achieving the target rate, with a better fairness in terms of sharing the excess bandwidth among flows. • provides the TCP flows, a greater degree of insulation from differences in RTT and window sizes. • The overall link utilization also seems to be much better.

23. Memory-Based Three Color Marker(MBTCM) • An Extension of MBM suitable for DiffServ with AF PHB. • Solves some issues in MBM. • an improvement over TSWTCM

24. MBTCM Algorithm else if (avg_rate cir) && (avg_rate pir) then mp= mp + (par – avg_rate)/avg_rate – (avg_rate-cir)/pir; par=avg_rate; mark the packet using: cp 11 w.p. mp cp 00 (red) w.p. (1-mp) else cp 00 w.p 1 • For each packet arrival If avg_rate  cir then mp=mp+(1-avg_rate/cir)+ (par- avg_rate)/avg_rate; par = avg_rate; mark the packet using: cp 10 (green) w.p. mp cp 11 (yellow) w.p. (1-mp)

25. MBTCM Algo. Explained • ·(avg_rate-cir)/pir acts as the reduction factor for reducing the probability as the avg_rate increases towards pir. This component is particularly useful when the traffic stream has a constant avg_rate (e.g., UDP traffic) and is above cir. In such a scenario, mp doesn’t remain constant but reduces to zero.

26. Results… Achieved Rates (Ra) for different Target Rates (Rt).

27. Results.. Achieved Rates (Ra) for different window sizes Achieved Rates in presence of BE UDP and TCP

28. TSWTCM vs. MBTCM: A Comparison • TSWTCM • 3 color TSW-TC based marker • Marking based on two parameters- Committed Target Rate (CTR), and Peak Target Rate (PTR).

29. Results.. Achieved Rates (Ra) for different Target Rates (Rt).

30. Results.. Achieved Rates (Ra) for different window sizes. Achieved Rates in presence of BE UDP and TCP

31. The Comparison. • MBTCM • achieves the target rates for priority flows with optimum marking. • helps in achieving consistency of goodput in cases of flows with different window settings. • performs better than TSWTCM in terms of protection from BE UDP flows. • has an overall link utilization much better than TSWTCM.

32. Congestion-aware Traffic Conditioner(CATC) • Congestion-aware • Least sensitive to the marker parameters. • Transparent to end hosts. • Maintain optimum marking.

33. Edge-to-Edge Feedback Architecture • Two edge routers • Control sender (CS) and control receiver (CR) • Upstream: • At CS: • CS sends control packets (CP) at regular interval of time, control packet interval (cpi). • CPs are given highest priority. • At Core: • Core routers maintain the status of drops of the best effort packets. • Information maintained as a status flag to a max. of cpi time. • CP’s congestion notification (CN) bit set or reset based on status flag. • At CR: • Responds to the incoming CP with a CN bit set by setting the congestion echo (CE) bit of the outgoing acknowledgement. • Downstream • At CS: • Maintains a parameter, congestion factor (cf). • Cf is set to 1 or 0 based on status of the CE bit in acknowledgement received

34. CATC Algorithm For each packet arrival If avg_rate  cir then mp=mp+(1- avg_rate/cir)*(1+ cf*(cir/cir_max)); mark the packet using : cp 11 w.p. mp cp 00 w.p. (1-mp) else if avg_rate > cir then mp=mp+ (1- avg_rate/cir)*(1- cf*(cir/cir_max)); mark the packet using : cp 11 w.p. mp cp 00 w.p. (1-mp)

35. CATC Algo. Explained • The effect on mp: • i)Flow component (1- avg_rate/cir) constantly compares the average rate observed with the target rate to keep the rate closer to the target. • ii)Network component cf*(cir/cir_max) provides a dynamic indication of congestion level status in the network. The marking probability increment is done in proportion to the target rate by multiplying cf with a weight factor cir/cir_max to mitigate the impact of the target rates.

36. Results Achieved Rates (Ra) for different Target Rates (Rt) -- under- and well-subscribed cases.

37. Results.. Achieved Rates (Ra) for different Target Rates (Rt) -- over-subscribed cases

38. Results: Goodput vs Time Graph (2/6 Mbps target rate.)

39. Results.. Achieved Rates in presence of BE UDP and TCP

40. Results.. Achieved Rates in presence of AS UDP and BE TCP

41. Inference • Achieves goodput close to the target rates. • Succeeds in taking the share of BE TCP and UDP flows in the worst case scenario. • The average link utilization pretty good. • The AS UDP flow gets its assured rate.

42. Linux Implementation of MBM • Incorporated with the existing traffic control functions of Linux. • Linux kernel version used was 2.2.14 , Redhat 6.2.

43. Traffic Control setup for Linux implementation of MBM

44. Areas for Deployment • Marker anywhere (lack of sensitivity to marker parameters). • MPLS over DiffServ.

45. Conclusion • transparency from the end hosts, simplicity, and least sensitivity to parameters of both TCP as well as marker parameters. • helps in achieving the target rate, with a better fairness in terms of sharing the excess bandwidth among flows. • provides the TCP flows, a greater degree of insulation from differences in RTT and window sizes. • overall link utilization also seems to be much better.

46. Conclusion… • Provides an architecture which is transparent to TCP sources and hence doesn’t require any modifications at the end hosts. • The edge-to-edge feedback control loop helps the marker to take proactive measures in maintaining the assured service effectively, especially during periods of congestion. • A single feedback control is used for an aggregated flow. Hence this architecture is scalable to any number of flows between the two edge gateways. • The architecture is adaptive to changes in load and network conditions. • The marking algorithm takes care of any bursts in the flows.

47. Acknowledgement • Dr. Lillykutty Jacob • Prof. A.L.Ananda • NS Community • Rajesh,Boon Peng • Michael, Srijith, Yong Xiang • Saswat, Prashant, Sriram, RK • All my dear friends • My Family • God Almighty

48. Papers published Conferences: 1. K.R.R.Kumar, A.L.Ananda, Lillykutty Jacob,“A Memory-Based Approach for a TCP-Friendly Traffic Conditioner in DiffServ Networks”, in Proc. of the 9th IEEE International Conference on Network Protocols (ICNP 2001), Riverside, California. 2. K.R.R.Kumar, A.L.Ananda, Lillykutty Jacob, “Using Edge-To-Edge Feedback Control to make Assured Service More Assured in DiffServ Networks”, in Proc. of the 26th Annual IEEE Conference on Local Computer Networks (LCN 2001), Tampa, Florida. Journal: 1. K.R.Renjish Kumar, A.L.Ananda, Lillykutty Jacob,“TCP-Friendly Traffic Conditioning in DiffServ Networks : A Memory-Based Approach ”, accepted (invited paper) in Computer Networks, Elsevier Publications.

49. Q & A

50. Thank You