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
– 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Advanced models
– Single queue
– Exponential distributions
– Open loop
– Steady state analysis
– Well-behaved parties
– Packet models
– Protocol analysis
–…
– Network of queues
– Heavy-tail distributions
– Closed loop
– Transients & dynamics
– Selfish/malicious parties
– Multi-timescale models
– Protocol design
–…
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
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Statistical analysis
Artificial intelligence
Maximum likelihood estimation
Streaming algorithms
Cryptography
Optimization
Information theory
Game theory and mechanism design
…
Discussion
• 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
Discussion
• 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
Discussion
• 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?
Discussion
• 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
• 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?