resource/accuracy tradeoffs in software-defined measurement masoud moshref, minlan yu, ramesh...
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Resource/Accuracy Tradeoffs in
Software-Defined Measurement
Masoud Moshref, Minlan Yu, Ramesh Govindan
HotSDN’13
2Motivation Tradeoff Case study DiscussionMotivation
Management policies need measurement
Traffic Engineering: • Find elephant flows to route [Hedera]• Estimate rack-to-rack traffic matrix [MicroTE]
Accounting:Tiered pricing based on network usage
Troubleshooting:• Find network bottlenecks of an application• Incast problem
3Motivation Tradeoff Case study DiscussionMotivation
Software Defined Measurement
3
ControllerTraffic
Engineering
Configure resources
1 Fetch statistics
2(Re)Configure resources
1
4Motivation Tradeoff Case study DiscussionMotivation
Challenges
Different measurements• Resource usage depends on measurement
Limited resources• Limited CPU/memory in switches• Limited control network bandwidth
Complexity of different primitives in switches• Counting by prefix matching (TCAM)• Counting based on hashes• Programming
Complexity of different primitives in switches• Counting by prefix matching (TCAM)• Counting based on hashes• Programming
5Motivation ArchitectureTradeoff Case study Discussion
Using Different Primitives for Measurement
Example: Finding heavy hitter IP when traffic from 10.0.1.0/24 goes beyond a threshold
Comparing primitives in performing a measurement task
6Motivation ArchitectureTradeoff Case study Discussion
Counting TCAM matches
0 1 1 0 * * * * 18
Filters Counters
1. Monitor 10.0.1.0/242. When goes beyond threshold, iteratively
search (e.g. binary search)
Controller10.0.1.0/24-
10.0.1.0/251
10.0.1.128/25
2
10.0.1.128/26
3
...
FetchInstall
• Large time-scale measurement• Measure-install loop latency• Controller link bandwidth
• Slowly varying traffic
7Motivation ArchitectureTradeoff Case study Discussion
2 1 5 1 0 0 1 0 5 3 1 0 2H
Src:10.0.1.5, Size:2
3
Controller
1. Fetch counters2. Approximate traffic for each
IP
• Uses cheaper resources (SRAM)• Not dependent on traffic history
• Use for varying traffic• Still history may help in large time scale to
optimize parameters
Sketches using hash based counters
8Motivation ArchitectureTradeoff Case study Discussion
Programming switches
10.0.1.5 90
10.0.1.1 25 10.0.1.3 50
10.0.1.12 17 10.0.1.10 3
Use more complex data structures/commands• Heap for finding large flows
Uses CPU resources• Can it keep up with line rate?
9Motivation ArchitectureTradeoff Case study Discussion
Summary of tradeoffs of primitives
Criteria Prefix matching
Hash-based counting
Programming
Memory usage TCAM SRAM SRAM
Bandwidth usage Medium Large Small
Time-scale Large Small Small
Traffic history sensitivity
Yes No No
Concerns Accuracy vs Resource
Accuracy vs Resource
Accuracy vs Resource
10
Case Study: Hierarchical Heavy Hitter Detection
11Motivation Case studyTradeoff Discussion
Hierarchical Heavy Hitters
36
26 10
12 14
5 7 12 2
5 5
2 30 5000
001
010
011
100
101
110
111
10* 11*
00* 01*
0** 1**
***
12Motivation Case studyTradeoff Discussion
Hierarchical Heavy Hitters
36
26 10
12 14
5 7 12 2
5 5
2 30 5000
001
010
011
100
101
110
111
10* 11*
00* 01*
0** 1**
***
Threshold=10
• Longest IP prefixes • Contribute a large amount of traffic• After excluding any HHH descendants in the
prefix tree
Motivation Case studyTradeoff Discussion
Algorithms for single switch HHH detection
13
26
- -
5
2 3
26
13 13
5
2 3
26
8 18
30
9 20
30
9 20
Approximate
Traverse tree
Prefix-based counting• Proposed an iterative algorithm at controller• Given TCAM capacity, monitor prefixes that maximize
accuracy
Hash-based counting• Use Hierarchical Count-Min sketch• Approximate traffic in all levels using sketches• Efficiently traverse IP tree at controller to find HHHs
14Motivation Case studyTradeoff Discussion
0
0.2
0.4
0.6
0.8
1
Re
call
20k256
80k1k
320k4k
0.05,Hash-based0.05,Prefix matching0.01,Hash-based0.01,Prefix matching
Evaluation
Sketches have better accuracy for small threshold (longer prefixes, higher variability)
Max-Cover saves bandwidth for large number of counters and large thresholds
SRAM countersTCAM counters
Normalize switch cost
80 SRAM 1 TCAM
15Motivation Tradeoff Case study DiscussionMotivation
Future work
ControllerTraffic
EngineeringAccountin
g
Software Defined Measurement
Nort
h-b
oun
dSouth
-bound
• Use joint information
• Guarantee accuracy
• Select primitive• Assign switch resources• Run global
measurement
Anomaly Detection
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