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Simplifying Campus Networking Using Cloud Wi-Fi

Deepak Gupta, Chief ArchitectMojo Networks

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Campus Wi-Fi challenges

• Large area: multiple buildings, outdoor areas, remote campuses.

• Planning for coverage, capacity, while minimizing cost.• Radio resource management (channel selection, power

control, load balancing, client steering, …).• Policy controls (e.g., role based controls, user based or

application based bandwidth limits, firewall, …)• Central management and control, performance monitoring,

airspace visibility, security.• Performance monitoring, troubleshooting, analytics.• Wi-Fi access for guests.

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Enterprise Wi-Fi Evolution

• Proprietary H/W• Thin APs• Physical server• Limited storage• Scale up to ~5000 devices

• Commodity H/W• Smart APs• Virtual servers• Analytics data • Scalable to millions of devices

2002 2010

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Controller based (Legacy) Wi-Fi Architecture

Dual Band APPHY+LMAC

Multiple SSIDson each radio

WLAN controller(all functions UMAC and up)

RADIUSserver

Trunk Port

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Problems with Controller Based Architecture

Single Point of Failure

Scalability bottleneck

High Cost OperationalComplexity

Limited Storage and Processing Capability

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Cloud Managed Wi-Fi is not …

• Cloud managed Wi-Fi is NOT a “Controller in the cloud”.

• Requires fundamental redesign of data, control planes.

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Control Plane - Distributed Zero functionality loss if connectivity with cloud breaks

Management Plane - CentralizedAccess | Security | Engagement

Data Plane – Flexible

Local Breakout of Data

1

Data Tunnel

VirtualizedTraffic Aggregator

3 2

3a3b

Data Integration (Syslog, RESTful APIs)

• Logging• SIEM• CRM

Mojo cloud architecture

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• Inter-AP Comm Protocol

• Client State Information• PMK ID, Captive portal state• IGMP State

Enables• Seamless Roaming

• RF Neighbor Information• # Clients / Transmit Power

Enables• Load Balancing• Auto Transmit Power and Coverage Hole Recovery• Automatic Channel Selection

Mojo Networks Distributed Cloud Architecture – Control Plane Functions

Inter AP Comm Protocol

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Cloud Managed Wi-Fi

No more controllers to manage

Limitlessly scalable and resilient cloud computing infrastructure

Put those idle CPUs in the APs to work!

The result: seamless scalability

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Distributed Control Plane Design

• Goals:• Optimal RF parameter choice (channel, Tx power)• Optimal client related decisions (which AP a client

should associate to).• Fast handoff for seamless roaming.

• APs need to share information and autonomouslymake near optimal decisions.

• Challenges: communication mechanism, security, scalability, resilience to failures, etc. Algorithms must result in near optimal decisions and should be “stable”.

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Proactive Analysis and Troubleshooting with Mojo Aware

18 Mar 9:30am – 9:45am

Baseline: 2.2%

Total Clients: 33

Clients Affected: 6 (18.2%)

Association Failures: 0 (0.0%)Authentication Failures: 5 (15.2%)Network Failures: 1 (3.0%)

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WiFi almost always gets blamed!

But more often than not, it is not the direct cause of end-user issues.

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Cognitive Wi-Fi (Mojo Aware)

Cognitive Computing

Client Emulation

Intelligent RF Optimizations

Client JourneyTM

Auto Packet Capture

Machine Learning

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Client JourneyTM

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Machine Learning

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In-built intelligence to analyze Wi-Fi connections in real time and automate root cause analysis

Cognitive Computing

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Wi-Fi Analytics – Example Use Cases

Did my investment in extra APs in the auditorium pay off in terms of

better user experience?

How has the recent configuration change affected the performance

of my network?

How does the network throughput and device density compare to an

year ago? Do I need to deploy more APs?

Performance at Location A is better than B? What’s the

difference: Config? Fewer clients? Cleaner wifi channels?

Is a location experiencing worse than usual performance for the

time of the day?

Do devices of a certain type (e.g., brand X mobiles) experience problems that others don’t?

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• Band steering

• Smart steering

• Auto channel selection

• Auto transmit power control

• Smart handling of broadcast and multicast traffic

Intelligent RF Optimizations

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Auto Packet Capture

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Client Emulation

• Client connectivity test

• Network profiling

• Auto-validation of client connectivity and performance

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Components of Enterprise Cloud Software Service

Secure & Efficient Multi-Tenant Software

Single Sign-on & authorization

Customer Provisioning & entitlement management

Service Monitoring & Incident Response

Cloud Ops

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