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Software-defined Measurement. Minlan Yu University of Southern California. Joint work with Lavanya Jose, Rui Miao, Masoud Moshref , Ramesh Govindan , Amin Vahdat. Management = Measurement + Control . Accounting Count resource usage for tenants Traffic engineering - PowerPoint PPT Presentation
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Software-defined Measurement
Minlan YuUniversity of Southern California
Joint work with Lavanya Jose, Rui Miao, Masoud Moshref, Ramesh Govindan, Amin Vahdat
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Management = Measurement + Control • Accounting
– Count resource usage for tenants
• Traffic engineering– Identify large traffic aggregates, traffic changes– Understand flow characteristics (flow size, etc.)
• Performance diagnosis– Why my application has high delay, low throughput?
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Yet, measurement is underexplored• Measurement is an afterthought in network device
– Control functions are optimized w/ many resources– Limited, fixed measurement support with NetFlow/sFlow
• Traffic analysis is incomplete and indirect– Incomplete: May not catch all the events from samples– Indirect: Offline analysis based on pre-collected logs
• Network-wide view of traffic is especially difficult– Data are collected at different times/places
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Software-defined Measurement• SDN offers unique opportunities for measurement
– Simple, reusable primitives at switches– Diverse and dynamic analysis at controller– Network-wide view
Controller
Heavy Hitter detection
Configure resources1 Fetch statistics2(Re)Configure resources1
Change detection
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Challenges• Diverse measurement tasks
– Generic measurement primitives for diverse tasks– Measurement library for easy programming
• Limited resources at switches– New data structures to reduce memory usage– Multiplexing across many tasks
Software-defined Measurement
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OpenSketch (NSDI’13)
DREAM(SIGCOMM’14)
Sketch-basedcommodity switch
components
Flow-based OpenFlow TCAM
Data plane Primitives
Optimization w/ Provable resource-accuracy bounds
Dynamic Allocation w/ Accuracy
estimatorResource alloc across tasks
OpenSourceNetFPGA + Sketch library
networks of hardware switches and Open vSwitch
Prototype
Software-defined Measurement with Sketches
(NSDI’13)
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Software Defined Networking
API to the data plane (OpenFlow)Fields action countersSrc=1.2.3.4drop, #packets, #bytes
SwitchesForward/measure packets
ControllerConfigure devices and collect measurements
Rethink the abstractions for measurement
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Tradeoff of Generality and Efficiency
• Generality– Supporting a wide variety of measurement tasks– Who’s sending a lot to 23.43.0.0/16?– Is someone being DDoS-ed?– How many people downloaded files from 10.0.2.1?
• Efficiency– Enabling high link speed (40 Gbps or larger)– Ensuring low cost (Cheap switches with small memory)– Easy to implement with commodity switch components
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NetFlow: General, Not Efficient
• Cisco NetFlow/sFlow– Log sampled packets, or flow-level counters
• General– Ok for many measurement tasks– Not ideal for any single task
• Not efficient– It’s hard to determine the right sampling rate– Measurement accuracy depends on traffic distribution– Turned off or not even available in datacenters
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Streaming Algo: Efficient, Not General• Streaming algorithms
– Summarize packet information with Sketches– E.g. Count-Min Sketch, Who’s sending a lot to host A?
• Not general:Each algorithm solves just one question– Require customized hardware or network processors– Hard to implement every solution in practice
# bytes from 23.43.12.1
3 0 5 1 9
0 1 9 3 0
1 2 0 3 4
Hash2Hash1
Hash3
Data plane
Query: 23.43.12.1
5 3 4
Pick min: 3
Control plane
Where is the Sweet Spot?
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EfficientGeneral
NetFlow/sFlow(too expensive)
Streaming Algo(Not practical)
OpenSketch• General, and efficient data plane based on sketches• Modularized control plane with automatic configuration
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Flexible Measurement Data Plane• Picking the packets to measure
– Hashes to represent a compact set of flows• A set of blacklisting IPs
– Classify flows with different resources/accuracy• Filter out traffic for 23.43.0.0/16
• Storing and exporting the data– A table with flexible indexing– Complex indexing using hashes and classification– Diverse mappings between counters and flows
A three-stage pipeline– Hashing: A few hash functions on packet source– Classification: based on hash value or packets– Counting: Update a few counters with simple calc.
# bytes from 23.43.12.1
3 0 5 1 9
0 1 9 3 0
1 2 0 3 4
Hash2Hash1
Hash3
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Build on Existing Switch Components• A few simple hash functions
– 4-8 three-wise or five-wise independent hash functions– Leverage traffic diversity to approx. truly random func.
• A few TCAM entries for classification– Match on both packets and hash values– Avoid matching on individual micro-flow entries
• Flexible counters in SRAM– Many logical tables for different sketches– Different numbers and sizes of counters– Access counters by addresses
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Modularized Measurement Libarary
• A measurement library of sketches– Bitmap, Bloom filter, Count-Min Sketch, etc.– Easy to implement with the data plane pipeline– Support diverse measurement tasks
• Implement Heavy Hitters with OpenSketch– Who’s sending a lot to 23.43.0.0/16?– count-min sketch to count volume of flows– reversible sketch to identify flows with heavy counts in
the count-min sketch
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Support Many Measurement TasksMeasurement Programs
Building blocks Line of Code
Heavy hitters Count-min sketch; Reversible sketch
Config:10Query: 20
Superspreaders Count-min sketch; Bitmap; Reversible sketch
Config:10Query:: 14
Traffic change detection
Count-min sketch;Reversible sketch
Config:10Query: 30
Traffic entropy on port field
Multi-resolution classifier; Count-min sketch
Config:10Query: 60
Flow size distribution
multi-resolution classifier; hash table
Config:10Query: 109
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Resource management• Automatic configuration within a task
– Pick the right sketches for measurement tasks– Allocating resources across sketches– Based on provable resource-accuracy curves
• Resource allocation across tasks– Operators simply specify relative importance of tasks– Minimizing weighted error using convex optimization– Decompose to optimization problem of individual tasks
OpenSketch Architecture
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Evaluation• Prototype on NetFPGA
– No effect on data plane throughput– Line speed measurement performance
• Trace Driven Simulators– OpenSketch, NetFlow, and streaming algorithm– One-hour CAIDA packet traces on a backbone link
• Tradeoff between generality and efficiency– How efficient is OpenSketch compared to NetFlow?– How accurate is OpenSketch compared to specific
streaming algorithms?
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Heavy Hitters: false positives/negatives• Identify flows taking > 0.5% bandwidth
OpenSketch requires less memory with higher accuracy
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Tradeoff Efficiency for Generality
In theory, OpenSketch requires 6 times memory than complex streaming algorithm
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OpenSketch Conclusion
• OpenSketch: – Bridging the gap between theory and practice
• Leveraging good properties of sketches– Provable accuracy-memory tradeoff
• Making sketches easy to implement and use– Generic support for different measurement tasks– Easy to implement with commodity switch hardware– Modularized library for easy programming
Dynamic Resource AllocationFor TCAM-based Measurement
SIGCOMM’14
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SDM Challenges
Controller
Configure resources1 Fetch statistics2(Re)Configure resources1
Heavy Hitter detectionHeavy Hitter detectionHeavy Hitter detection HChange detection
Dynamic Resource Allocator
Many Management tasks
Limited resources (TCAM)
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Dynamic Resource Allocator• Diminishing return of resources
– More resources make smaller accuracy gain– More resources find less significant outputs– Operators can accept an accuracy bound <100%
256512 1024 20480
0.2
0.4
0.6
0.8
1
Resources
Rec
all
Reca
ll=
dete
cted
true
HH/
all
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Dynamic Resource Allocator• Temporal and spatial resource multiplexing
– Traffic varies over time and switches– Resource for an accuracy bound depends on Traffic
Reca
ll=
dete
cted
true
HH/
all
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Challenges• No ground truth of resource-accuracy
– Hard to do traditional convex optimization– New ways to estimate accuracy on the fly– Adaptively increase/decrease resources accordingly
• Spatial & temporal changes– Task and traffic dynamics– Coordinate multiple switches to keep a task accurate– Spatial and temporal resource adaptation
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Dynamic Resource Allocator
Controller
Heavy Hitter detectionHeavy Hitter detectionHeavy Hitter detection HChange detection
Dynamic Resource Allocator
Estimated accuracy
Allocated resource
Estimated accuracy
Allocated resource
• Decompose the resource allocator to each switch– Each switch separately increase/decrease resources– When and how to change resources?
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Per-switch Resource Allocator: When?• When a task on a switch needs more resources?
– Based on A’s accuracy (25%) is not enough• if bound is 40%, no need to increase A’s resources
– Based on the global accuracy (47%) is not enough• if bound is 80%, increasing B’s resources is not helpful
– Conclusion: when max(local, global) < accuracy bound
A B
ControllerHeavy Hitter detection
Detected HH:5 out of 20Local accuracy=25% Detected HH:9 out of 10
Local accuracy=90%
Detected HH: 14 out of 30Global accuracy=47%
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Per-Switch Resource Allocator: How?• How to adapt resources?
– Take from rich tasks, give to poor tasks • How much resource to take/give?
– Adaptive change step for fast convergence– Small steps close to bound, large steps otherwise
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GoalMMAMAAMA
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Task ImplementationController
Configure resources1 Fetch statistics2(Re)Configure resources1
Heavy Hitter detectionHeavy Hitter detectionHeavy Hitter detection HChange detection
Dynamic Resource Allocator
Estimated accuracy
Allocated resource
Estimated accuracy
Allocated resource
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Flow-based algorithms using TCAM
• Goal: Maximize accuracy given limited resources• A general resource-aware algorithm
– Different tasks: e.g., HH, HHH, Change detection– Multiple switches: e.g., HHs from different switches
• Assume: Each flow is seen at one switch (e.g., at sources)
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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***** New
Current
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Divide & Merge at Multiple Switches• Divide: Monitor children to increase accuracy
– Requires more resources on a set of switches• Example: Needs an additional entry on switch B
• Merge: Monitor parent to free resources– Each node keeps the switch set it frees after merge– Finding the least important prefixes to merge is the
minimum set cover problem
26
12 1400* 01*
0**
{A,B} {B,C}{A,B,C}
New: A:00*, B:00*,01*, C:01*
Current: A:0**, B:0**, C:0**
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Accuracy Estimation: Heavy Hitter Detection
• Any monitored leaf with volume > threshold is a true HH
• Recall:
– Estimate missing HHs using volume and level of counter
76
26 50
12 14
5 7 12 2
15 35
20 150 15000
001
010
011
100
101
110
111
10* 11*00* 01*
0** 1*****
With size 26 missed <=2 HHs
At level 2 missed <=2 HH
Threshold=10
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DREAM Overview
Task
obj
ect
1
Task
obj
ect
n
DREAMSDN Controller
2) Accept/Reject5) Report
1) Instantiate task
3) Configure counters
4) Fetch counters
7) Allocate / Drop
6) Estimate accuracy
Resource Allocator
• Task type (Heavy hitter, Hierarchical heavy hitter, Change detection)
• Task specific parameters (HH threshold)• Packet header field (source IP)• Filter (src IP=10/24, dst IP=10.2/16)• Accuracy bound (80%)
Prototype Implementation with DREAM algorithms on Floodlight and Open vSwitches
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Evaluation• Evaluation Goals
– How accurate are tasks in DREAM? • Satisfaction: Task lifetime fraction above given accuracy
– How many more accurate tasks can DREAM support?• % of rejected/dropped tasks
– How fast is the DREAM control loop?• Compare to
– Equal: divide resources equally at each switch, no reject– Fixed: 1/n resources to each task, reject extra tasks
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512 1024 2048 40960
20
40
60
80
100
Switch capacity%
of t
asks
DREAM-rejectFixed-rejectDREAM-drop
Prototype Results
Mean
5th %
DREAM: High satisfaction for avg & 5th % of tasks with low rejection
Fixed: High rejection as over-provisions for small tasks
Equal: only keeps small tasks satisfied
256 tasks (various task types) on 8 switches
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512 1024 2048 40960
20
40
60
80
100
Switch capacity%
of t
asks
DREAM-rejectFixed-rejectDREAM-drop
Prototype ResultsDREAM: High satisfaction for avg & 5th % of tasks at the expense of more rejection
Equal & Fixed: only keeps small tasks satisfied
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Control Loop DelayAllocation delay is
negligible vs. other delays
Incremental saving lets reduce save delay
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DREAM Conclusion• Challenges with software-defined measurement
– Diverse and dynamic measurement tasks – Limited resources at switches
• Dynamic resource allocation across tasks– Accuracy estimators for TCAM-based algorithms– Spatial and temporal resource multiplexing
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Summary• Software-defined measurement
– Measurement is important, yet underexplored– SDN brings new opportunities to measurement– Time to rebuild the entire measurement stack
• Our work– OpenSketch:Generic, efficient measurement on sketches– DREAM: Dynamic resource allocation for many tasks
43
Thanks!