Looking over the fence at networking
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Looking Over the Fence at Networking. Jennifer Rexford. Internet Success Leads to Ossification. Intellectual ossification Pressure for backwards compatibility with Internet Risks stifling innovative intellectual thinking Infrastructure ossification

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Looking Over the Fence at Networking

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Looking over the fence at networking

Looking Over the Fence at Networking

Jennifer Rexford

Internet success leads to ossification

Internet Success Leads to Ossification

  • Intellectual ossification

    • Pressure for backwards compatibility with Internet

    • Risks stifling innovative intellectual thinking

  • Infrastructure ossification

    • Limits on the ability to influence deployment

    • E.g., multicast, IPv6, QoS, and secure routing

  • System ossification

    • Shoe-horn solutions that increase system fragility

    • E.g., NATs and firewalls

A need to invigorate networking research

A Need to Invigorate Networking Research

  • Measurement

    • Understanding the Internet artifact

    • Better built-in measurement for the future

  • Modeling

    • Performance models faithful to Internet realities

    • X-ities like manageability, evolvability, security, …

  • Prototyping

    • Importance of creating disruptive technology

    • Emphasis on enabling new applications

Challenges of measurement

Challenges of Measurement

  • Extreme scale

    • Large number of routers, links, ASes, packets, …

  • Difficulty of identifying flows

    • End-to-end design

    • Statelessness of the IP datagram

    • Routing asymmetry

    • Multipath routing

  • Limitations on collection and sharing of data

    • User privacy

    • Confidentiality of business data

Measurement research line card support

Measurement Research: Line-Card Support

  • Efficient measurement to place in line cards

    • Online data collection at high speed

    • Ideally useful for many kinds of analysis

  • E.g., trajectory sampling

    • Sample based on a hash of packet contents

    • Sampled packets are sampled at each hop

  • E.g., psamp activity at the IETF

    • Parallel banks of filter, sample, and record

  • E.g., deep packet inspection

    • Algorithms for identifying patterns in packets

    • Useful for detecting worms, viruses, etc.

Measurement research tomography

Measurement Research: Tomography

  • Inference based on limited measurements

    • Inverse problems that are often underconstrained

  • E.g., AS relationships (e.g., Gao paper)

    • Given collection of AS paths

    • Infer business relationship between AS pairs

  • E.g., traffic matrix

    • Given link load statistics and routing configuration

    • Infer offered load between ingress-egress pairs

  • E.g., link performance statistics

    • Given path-level measurements (e.g., loss, delay)

    • Infer the performance of the individual links

Measurement research anomaly detection

Measurement Research: Anomaly Detection

  • Mining large, heterogeneous, distributed data

    • To detect and diagnose anomalies, in real time

    • Flash crowd, DDoS attack, worm, failure, …

  • Applying a variety of analysis techniques

    • Statistics (e.g., Fourier, Wavelets, PCA)

    • AI (e.g., Machine Learning)

    • Algorithms (e.g., sketches, streaming algorithms)

  • To a variety of kinds of data

    • Per link: packet or flow traces

    • Per path: delay, loss, or throughput

    • Network-wide: link matrix or traffic matrix

Measurement research privacy confidentiality

Measurement Research: Privacy & Confidentiality

  • Preserving privacy and confidentiality

    • Respect user privacy and business confidentiality

    • While still producing useful analysis results

  • E.g., anonymization of the data

    • Anonymization of multi-dimensional data

    • While still preserving associations across data

  • E.g., privacy-preserving data analysis

    • Distributed computation that hides information

    • Computing a sum without revealing the parts

Measurement research protocol design

Measurement Research: Protocol Design

  • Protocol design

    • Incorporating self-measurement, analysis, and diagnosis in future systems and protocols

  • E.g., Early Congestion Notification

    • Marking TCP packets that encounter congestion

    • To trigger the sender to decrease sending rate

  • E.g., BGP cause tags

    • Tagging BGP update messages with root cause

    • To reduce path exploration during convergence

Performance models

Traditional models

Single queue

Exponential distributions

Open loop

Steady state analysis

Well-behaved parties

Packet models

Protocol analysis

Advanced models

Network of queues

Heavy-tail distributions

Closed loop

Transients & dynamics

Selfish/malicious parties

Multi-timescale models

Protocol design

Performance Models

Modeling the x ities or ilities

Modeling: The X-ities (or Ilities)

  • Beyond higher speed to consider X-ities

    • Reliability

    • Scalability

    • Manageability

    • Configurability

    • Predictability

    • Non-fragility

    • Security

    • Evolvability

  • Challenging to model, or even to quantify

A need for interdisciplinary work

A Need for Interdisciplinary Work

  • Statistical analysis

  • Artificial intelligence

  • Maximum likelihood estimation

  • Streaming algorithms

  • Cryptography

  • Optimization

  • Information theory

  • Game theory and mechanism design



  • Where should the intelligence reside?

    • Traditional Internet says “the edge”

    • What about middleboxes (e.g., NAT)?

    • Need to assemble applications from components located in different parts of the network?

  • Better isolation and diagnosis of faults?

    • Decentralized Internet makes this difficult

    • Need to detection, diagnosis, and accountability

    • Challenges the end-to-end argument



  • Data as a first-class object?

    • Tradition Internet simple moves the bytes

    • Naming, search, location, management in the ‘net

    • Modifyingg the data as it traverse the network

  • Does the Internet have a control plane?

    • Traditional Internet stress data transport

    • What about network management and control?

    • Today we place more emphasis on designing new protocols and mechanisms than controlling them



  • Abstractions on topology and performance

    • Traditional Internet hides details from end hosts

    • Network properties are, at best, inferred

    • Guidelines for placement of middleboxes?

    • Feedback info about topology and performance?

  • Beyond cooperative congestion control

    • Traditional Internet places congestion control in the end hosts, and trusts them to behave

    • Is this trust misguided?

    • New alternatives to congestion control?



  • Incorporating economic factors in design

    • Traditional Internet ignores competitive forces

    • Many constraints are economic, not technical

    • Better to construct/align economic incentives

  • Ways to deploy disruptive technology

    • Traditional core is not open to disruptive tech

    • Overlay network as a deployment strategy

    • Other approaches? Virtualization? Middleboxes? Speaking the legacy protocols with new logic?

    • Experimental facilities? A “do over”?

The innovator s dilemma

The Innovator’s Dilemma

  • Leading companies often miss “next big thing”

    • E.g., disk-drive industry and excavation equipment

  • Problem

    • Listening to customers leads to incremental improvement on the existing technology curve

    • Disruptive technologies are often less effective for the existing customers, so tend to be ignored

    • New companies exploit the new technology for a new market (e.g., desktops, laptops)

    • Eventually, the new technology curve overtakes the old technology, usurping the old technology

  • Will this happen with the Internet?

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