Management Plane Analytics Aaron Gember-Jacobson, Wenfei Wu, Xiujun Li, Aditya Akella, Ratul Mahajan 1.

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Management Plane Analytics

Aaron Gember-Jacobson, Wenfei Wu, Xiujun Li, Aditya Akella, Ratul Mahajan

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What is the management plane?

Data planeForwards packets

Control planeGenerates forwarding tables

Management plane Defines data plane structure and control plane config

ConfigRoutingTable

Forwarding Table

We can model these

How do we model this?

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Why study the management plane?

• Important to well functioning networks!• But…there’s no systematic understanding

of how management practices impact the health of networks

?Operators have a diversity of opinions on

what matters

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Our goals

1. Characterize management practices in modern networks

2. Infer the practices that matter mosttoward health (e.g., # of failures)

3. Predict health, based on practices→ Perform what-if analysis

4. Suggest control plane configurations, given some objectives

Inspired by empirical software engineering

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Challenges

• Management practices aren’t explicitly logged

• Data may be incomplete or insufficient– Use data from many networks and time periods

Configs Inventory Practices Health

&Tickets

+ +

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Characterizing management practices

• 850+ networks from an online service provider• Two classes of practices– Design: long-term decisions

Define network structure and provisioningE.g., how many switches and which vendors

– Operational: day-to-day activities Changes to address emerging needsE.g., adding subnets

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Design Practices

• Heterogeneous physical composition– Multiple roles (86% of networks),

vendors (81%), and models (96%)• Heterogeneous logical composition– 2+ layer-2 protocols

(e.g., VLAN, MSTP)– 1+ routing

protocol (89%)

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Operational practices

# changes ≈

# devices

Differentdevices changed

each month

Interface changes are the most

frequent

Lots of variability in automation;

not correlated with # changes

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Are current languages good?

• Example: adding a VLAN– Cisco IOS

– Pyretic

– Intent: isolation want a construct that allows operators to specify this intent (e.g., Merlin?)

interface GigabitEthernet1/1switchport access vlan 101switchport mode access

match(switch=s,inport=1)[modify(vlan=101)>>learn]

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Predicting network health

• Build a decision tree classifier– Benefit: intuitive for operators to understand– 5 bins for practices; 2 or 5 bins for health– Build with C4.5; prune to avoid over-fitting

Healthy Unhealthy0

0.2

0.4

0.6

0.8

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F-Sc

ore

Excellent Good Moderate Poor Very poor

91.6% accuracy 81.1% accuracy

Few unhealthy data points

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Improving predictions

• Boosting– Increases (decreases) the weight of examples

that were classified incorrectly (correctly)• Minority oversampling– Clusters data points

in the minority class– Generates examples

in the same cluster

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Summary

• Management plane is important to well functioning networks, but not well understood

• Modern networks have a heterogeneous design and frequent, sometimes automated changes

• Enable organizations to perform what-if analysis• How do we capture intent?