1. The War Between Mice and Elephants
Liang Guo, Ibrahim Matta Computer Science Department Boston University Boston, MA USA
Daniel Courcy-
Nathan Salemme-

2. Outline Introduction Analyzing Short Flow Performance
Scheme Architecture and Mechanisms
Simulation
Discussion
Conclusion
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3. References
Most figures, tables and graphs in this presentation were gathered from the Journal paper: The War Between Mice and Elephants.
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4. Mice and Elephants? Most connections are short (mice)
However, long connections (elephants) dominate Internet bandwidth
Problem?
Elephants hinder performance of short mice connections
Long delays ensue
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5. TCP Factors
TCP transport layer has properties that can cause this problem
Sending window slowly ramped up starting at minimum
Regardless of available bandwidth, always starts at minimum value
Packet loss always determined by timeout
Not enough time for duplicate ACKS
Conservative ITO can have devastating affects if initial control packets are lost (SYN, SYN-ACK)
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6. *flow=connection=session*
Proposed Solution
These factors cause short flows to receive less than fair bandwidth
Proposed plan- ‘Preferential treatment’ to short flows
Differentiated Services Architecture (Diffserv)
Core and Edge Routers (similar to CSFQ)
Active Queue Management (AQM)
Core Routers use RIO-PS Queue
Edge Routers use threshold based classification
No packet reordering!
Simulations prove
Better fairness and response time by giving treatment to short flows
Goodput remains the same if not better
*flow=connection=session*
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7. Related Work Worcester Polytechnic Institute
Guo & Matta wrote “Differentiated Predictive Fair Service for TCP Flows” (2000)
A Study of Interaction Between Short and Long Flows
Propose to Isolate Short / Long Flows
This Would Increase Response Time and Fairness for Short Flows
Results Show
Class-based Flow Isolation with Threshold-based Classification at the Edge may Cause Packet Reordering
(Thus) Degradation of TCP Performance
Difference: (Mice & Elephants) Bandwidth (Load) Control is Performed on the Edge
Cardwell et al. Show in “Modeling TCP Latency” that Short TCP flows are Vulnerable
Seddigh et al. Show Negative Impact of Initial Timeout Value (ITO) on Short TCP Flow Latency
Propose to Reduce Value (G & M Test This Later On…)
Many Proposed Solutions Attempt to Revise TCP Protocol
This Study Places Control Inside Network (Fair to All)
Corvella et al. & Bansal et al. Claim Size-aware Job Scheduling Helps to Enhance Response Time of Short Jobs (While Not Really Hurting the Performance of Long Jobs)
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8. Related Work G & M Propose Alternative use of Diffserv Architecture
Diffserv = Framework; Allows for Classification & Differentiated Treatment
G & M Want to Provide a New “better-than-best-effort” TCP Service that Attempts to Enhance the Competence of Short TCP Flows
This Creates a More Fair Environment for Web Traffic
They Classify In / Out to Distinguish Flow Length as Short / Long
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9. Analyzing Short Flow Performance
Packet loss rate needs to be low for short connections
Simulation of different packet size connections
RTT 0.1 seconds
RTO 0.4 seconds
Default ITO 3 seconds
Fixed Packet Size (for each connection)
Vary loss rate
Goal is to show how short flows become very sensitive as packet loss rate increases
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10. Sensitivity of Short Flows
Short flows not sensitive at low loss rates
Short flows very sensitive at high loss rates
Long flows, loss rate simply grows exponentially
Exponential growth as loss rate increases
Linear growth as loss rate increases
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11. Sensitivity of Short Flows
Covariance (C.O.V.)
Ratio between standard deviation and the mean of a certain random variable
Variance for short flows increase as loss rate increases
Variance for long flows decrease as loss rate increases
Long connections decrease in variance
Short connections increase in variance
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12. Sensitivity of Short Flows
Possible reasons for high covariance in short flows
High congestion puts TCP into exponential backoff
Resending packets in slow start vs. congestion avoidance will yield high variance
Slow start=aggressive
Congestion avoidance=conservative
Law of Large Numbers; more variance for short connections
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13. Preferential Treatment of Short TCP Flows
Simple Simulations Used to Show:
Preferential Treatment of Short TCP Flows can Significantly Enhance Short Flow Transmission Time (w/ No Major Effects on Long Flows)
G & M use ‘ns’ to Setup:
10 Long (10000-packet) TCP Flows
10 Short (100-packet) TCP Flows
These Compete For Bandwidth Over 1.25 Mbps Link
G & M Use Queue Bottleneck
They Measure Instantaneous Bandwidth
Shows Effects of Preferential Treatment
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14. Impact of Preferential Treatment
Consider Drop Tail vs. RED vs. RIO-PS
(RIO with Preferential Treatment)
Drop Tail Fails to Give Fair Treatment
Favors Aggressive Flows w/ Larger Windows
RED Gives Almost Fair Treatment
RIO-PS Queue Gives Short Flows > Than Fair Share
Graph Shows Short Flows Under RIO-PS can Temporarily Steal Bandwidth
In The Long Run a Short Flow’s Early Completion Returns Resources
i.e. Long Flows can Better Adapt to Network State
Giving Short Flows Preferential Treatment Does Not Impact LT Goodput
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15. Additional Notes on Preferential Treatment
Preferential Treatment Might Enhance Long Flows (As Seen on Previous Slide)
Helps Get Initial ‘Long Flow’ (Control) Packets Through via Threshold
Short Flows w/ Less Drops Enhances Fairness Between Themselves
Table 1 (Above):
When Load is Low: RIO-PS & RED Slightly Lower Goodput
When Load is Higher: RIO-PS Slightly Higher Goodput
Therefore G & M Propose Diffserv-like Architecture
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16. Proposed Architecture
Diffserv Architecture
Edge Routers
Core Routers
Classify flows into short and long
Marks packets accordingly
Actively manage flows based on their class
AQM policy implemented at core
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17. Edge Router
Edge router sits on edge of network and is responsible for determining short and long flows
Simple threshold counter used, Lt
Connection < Lt, short flow
Connection > Lt, long flow
All connections start of short
Lt is dynamic
Connection is considered active until Tu expires
SLR parameter used to balance short connections vs. long connections
This ratio is updated every Tc time period through additive increase/decrease
Chosen values Tu=1 second and Tc=10 seconds
All flows at first are considered short
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18. Core Router: Preferential Treatment to Short Flows
G& M Require Core Routers to Give Preferential Treatment to Short Flows
Many Queue Policies Available
Picked RIO (RED w/ In and Out)
Conforms to Diffserv
Other Advantages (Lets Bursty Traffic Through)
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19. RIO
Operation of Queue
In (Short) Packets not Affected by Out (Long) Packets
Dropping / Marking Probability for Short Based Only on Average Backlog of Short Packets (Qshort)
Dropping / Marking Probability for Long Based on Total Average Queue Size (Qtotal)
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20. Design Features of RIO G & M Discuss RIO Features
Only a Single FIFO Queue is Used for all Packets
Reordering Will not Happen
Reordering Can Lead to Over-Estimation of Congestion
RIO Inherits Features of RED
Protection of Bursty Flows
Fairness within Traffic Classes
Detection of Incipient Congestion
RIO-PS (RIO w/ Preferential Treatment for Short Flows)
BW for Short Flows Determined by Parameters
G & M Choose 75% Total Link Bandwidth In Times of Congestion
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21. Simulation G & M use ‘ns’ Simulator to Study:
Performance vs. Drop Tail & RED
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22. Simulation Setup Assume Network Traffic Dominated by Web Traffic
Model as Such:
Randomly Selected Clients Start Sessions That Randomly Surf to Random Web Pages (of Different Sizes)
Each Page may Have Several Objects
Thus Each Requires a TCP Connection [HTTP 1.0 is Assumed]
Client Request and Servers Respond w/ Acknowledgement and Remainder of Data (the Web Page)
Load is Carefully Tuned to be Close to Bottleneck Link Capacity
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23. Simulation Topology Worcester Polytechnic Institute
Traffic Largely Flows Right to Left
Node 0 is Thus Entry Edge Router
Nodes 1, 2, 3 are Core Routers
Bottlenecks to Clients (1-3 and 2-3)
Bottleneck Buffer Size and Queue Management Set to Maximize Power
Power is Ratio Between Throughput and Latency
High Power is Low Delay and High Throughput
Again for RIO-PS Queue Short Flows are Set to Get about 75% of Total Bandwidth
ECN is Turned ON (This Aids RED w/ Short Flows)
ECN was Tried OFF  Witnessed Larger Performance Gains
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24. Experiment 1: Single Client
Only one client set used in previous topology
Parameters:
Experiment run for 4000 seconds (first 2000 discarded)
SLR set to 3
Drop-tail, RED and RIO-PS are subjects
Response time recorded after each successful object download
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25. Experiment 1: Single Client Response Time (ITO=3)
ITO set to 3 seconds
Average response time determined to be 25-30% less than competition
It is argued that 3 second ITO is too safe, so 1 second timer is tried
25-30% gap between RIO-PS and competition. Pretty good…
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26. Experiment 1: Single Client Response Time (ITO=1)
ITO set to 1 seconds
Potentially an unsafe setting if used in environment with long slow links and long propagation times
Results show less gap between RIO-PS and competition
Still good performance, might not be worth risk
15-25% gap between RIO-PS and competition. Not as good and more risky…
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27. Experiment 1: Single Client Queue Size (ITO=3)
Instantaneous queue size for last 20 seconds using 3 second ITO
High variation due to file size distribution
RIO-PS relatively low compared to Drop-tail and RED
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28. Experiment 1: Single Client Drop/Mark Rate (ITO=3)
Overall drop/mark rate of entire network reduced
Short connections rarely drop packets
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29. Study of Foreground Traffic
To Better Show Queue Management Policy’s Effects on Fairness of TCP Connections G & M Injected 10 Short and 10 Long Foreground TCP Connections and Recorded the Response Times
The Fairness Index of Response Times Computed
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30. Fairness – Short Flows RIO-PS Most Fair For Short Flows
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31. Fairness – Long Flows Long Flows Do Well Across The Board
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32. Transmission Time – Short Connections
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33. Transmission Time – Long Connections
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34. Table IV Summary of Overall Goodput Proposed Scheme Does Not Hurt
Slightly Improves Overall Goodput Over Drop Tail
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35. Experiment 2: Unbalanced Requests
Simulation Where File Requests on Different Routes are Unbalanced
Small Requests on One Route / Large Requests on Another
Proposed Scheme Reduced to Tradition Unclassified Traffic + RED
i.e. No Different than RED
Still Getting the First Few Packets Across w/ Preferential Treatment Does Help Reduce the Chance of Retransmission and Allows Short Connections to Finish Quickly
Remainder of Results Omitted
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36. Discussion Simulation Model: Queue Management
-“Dumbbell and Dancehall”
Only one-way
Different propagation delays not considered
Queue Management
RIO does not necessarily guarantee class-based flows
PI controlled RED may be better solution
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37. Discussion Deployment Issues:
Scheme Requires that Edge Devices be Able to Perform Per-flow State Maintenance and Per-packet Processing
G & M Quote Previous Work [31] Stating this Does not Really Impact End-to-end Functionality
Incrementally Deployable?
Only Edge Routers Need to Be Configured
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38. Flow Classification Flow Classification:
Threshold-based Classification
Because Edge Node Cannot Predict
This Allows the Beginning of a Long Flow to Look like a Short Flow
This “mistake” Actually Helps Performance
This Allows the First Few Packets of a Long TCP Flow to be Treated as the First Few of a Short Flow
Makes System Fair to all TCP Connections
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39. Discussion Controller Design: Malicious Users
Edge load control may not be so dependant upon the value of SLR
More importantly are the values of Tc and Tu (small values may be more accurate but increase overhead)
Malicious Users
Users who would try and break long TCP connections into smaller segments
The dynamic nature of edge routers should prevent such things
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40. Conclusions TCP = Majority of Bytes Flowing Over Internet
Diffserv-like Architecture Where Edge Routers Classify Flow Size
Core Routers Implement Simple RIO to give Preference to Short Flows
Mice Get Better Response Time and Fairness
Elephants are Enhanced (Slightly) or at Least Only Minimally Affected
Goodput Enhanced (or at Least not Degraded)
Flexible Architecture (Needs only Edge Tuning)
Size-aware Traffic Management has Considerable Promise
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