---
title: Self-Driving Networks
tags: lecture, cs176c
description: View the slide with "Slide Mode".
---

# CS 176C:
Self-Driving Networks

[Slides](https://hackmd.io/@arpitgupta/HkLlI2L28)

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## 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

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# Self-Driving Networks

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## 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)

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## What's Different Today? (1)

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

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## 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

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## 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?

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## (Labelled) Data Problem

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

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## 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](https://en.wikipedia.org/wiki/Lidar)

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## LIDAR

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

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## 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

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## 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)

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## Today's Self-Driving Cars

LIDAR contribute significantly to total data usage

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## 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

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## 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!

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## 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

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## 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

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## Approach

Scalable and privacy-preserving data collection/analysis pipeline

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## 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

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# Course Summary

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## Last-Mile Networks
- WLAN Networks
- Cable Networks
- Cellular Networks

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## 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

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## Cable Networks
- Basics:
- key components (HFC networks)
- DOCSIS:
- how it evolved over time?
- DOCSIS 3.1 deep dive

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

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

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## Network Support

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

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## Network Measurements Systems
- Last-mile Networks
- BISMark
- CableMon
- CDN Measurement Systems
- Odin

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## Networking Streaming Telemetry
- System:
- Sonata
- Enabler:
- RMT Architecture

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## Future directions
- Digital divide
- Self-driving networks