Learning Objectives

• Self-Driving Networks
  • What's the history, what's new, and what are the key challenges?
  • How can we realistically realize the goal of developing self-driving networks?
• Course Summary
  • What did we learn this quarter?
  • Logistics and next steps

Self-Driving Networks

Long Road to Self-Driving Networks

• Definition:
  • Networks that run with minimal to no human intervention—able to configure, monitor, and maintain themselves independently
• Past efforts:
  • Active networking (late 90s)
  • Self-* networking (early 2000s)
  • Autonomous networking (~2010)

What's Different Today? (1)

Market Pull

• Zero-touch networks (Google, ~2016)
• Self-driving networks (Juniper, ~2017)
• Adaptive networks (Cienna, ~2018)

What's Different Today? (2)

Technology Push

• Fully programmable, protocol-indepdentent data planes
  • with language to program (e.g., P4)
  • what methods for formal verification
• AI and ML techniques
  • AI/ML for networking
  • Networking for AI/ML

AI/ML and Self-Driving Networks

• AI/ML techniques have been potent for areas where
  • (labeled) data is abundant
  • trust/privacy are not key requirements
  • large-scale testing in productions settings was feasible

Is Networking such an area?

(Labelled) Data Problem

• What is the right type of data (scale)?
• How to obtain the data (privacy)?
• How to address data quality issues (scale)?

Self-driving Car Analogy

• DARPA Grand Challenge

  • 2004: None of the finalists finishes the 150-mile route
  • 2005: 5 of the 23 finalists completed the 132-mile course
  • 2007: 6 of the 11 finalists completed the 55-mile urban course

• What changed after 2004?

  • LIDAR

LIDAR

LIDAR measurements have been viewed as game changer for self-driving cars

Why LIDARs made a difference?

• Features
  • Millions of beams of laser light per second
  • 3600 visibility, for distances up to ~200m
  • +/- 2cm depth perception accuracy

Why LIDARs made a difference?

• Features
  • Millions of beams of laser light per second
  • 3600 visibility, for distances up to ~200m
  • +/- 2cm depth perception accuracy
• Criticism
  • Cost (was $80K in 2010, now ~$8K)
  • Overkill (too much data)

Today's Self-Driving Cars

LIDAR contribute significantly to total data usage

LIDAR for Self-Driving Networks? (1)

• Network data monitoring/collection today …
  • Piecemeal, problem-specific, ad-hoc, an after-thought, …
  • The curse of complexity, scale and dimensionality

LIDAR for Self-Driving Networks? (2)

• Network data monitoring/collection tomorrow …
  • Holistic, network-wide, all the time, no sampling,
  • Augmented with auxiliary data (e.g., config files, routing info)
  • Early criticism:
    • Too expensive!
    • Overkill!

AI/ML Trust Problem

• A clash of two cultures
• Black-box nature of conventional AI/ML
  • Opaque, unintuitive, unintelligible
• Network operators desire for white-box solutions
  • Want intuition
  • Look for understanding
  • Seek to gain insights

Such clash impedes deployment of self-driving networks in conventional settings

Summary of Challenges

• Data problem
  • Collect network data at scale in a privacy-preserving problem
• Trust problem
  • Make inferences and actions more explainable/interpretable/verifiable
• Ownership
  • Democratize ownership of data and learning algorithms/models

Approach

Scalable and privacy-preserving data collection/analysis pipeline

Privacy-preserving Model/Data Sharing

Use two non-colluding cloud providers, homomorphic encryption, and Yao's garbled circuits to train learning models in a privacy-preserving manner

Course Summary

Last-Mile Networks

• WLAN Networks
• Cable Networks
• Cellular Networks

WLAN Networks

• Basics
  • key components, different standards
• Wireless medium access
  • hidden and exposed terminal problems
• Mobility
  • client's association/disassociation
• 802.11 n
  • what enables high data rates

Cable Networks

• Basics:
  • key components (HFC networks)
• DOCSIS:
  • how it evolved over time?
  • DOCSIS 3.1 deep dive

Cellular Networks

• Basics:
  • Key components, different generations
• Mobility:
  • How cellular networks handle mobility?
• 5G Networks:
  • key components, architecture, research directions

Multimedia Networking

• Video streaming
  • requirements, client-side buffering, transport protocols
• VoIP
  • requirements, playout delays, operational challenges
• Real-time protocols
  • RTP headers, RTCP protocol

Network Support

• Scheduling/Policing Mechanisms
  • packet marking, policing, traffic shaping
• Bufferbloats
• Active Queue Management

Network Measurements Systems

• Last-mile Networks
  • BISMark
  • CableMon
• CDN Measurement Systems
  • Odin

Networking Streaming Telemetry

• System:
  • Sonata
• Enabler:
  • RMT Architecture

Future directions

• Digital divide
• Self-driving networks