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The Data-Centric Revolution in Networking?

Scott ShenkerInternational Computer Science Institute

U. C. Berkeley

Liberally stealing the insight and work of others, particularly Hari Balakrishnan, Deborah Estrin,

Ramesh Govindan, Joe Hellerstein, and Ion Stoica

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Two Communities Apart

Networking (Internet) researchers:- don’t know and don’t care about databases

Vast gap between communities- much more overlap with other systems communities

But data-centrism has narrowed the gap- metaphors and algorithms

This talk will tell that story: in reverse order- Internet, then sensornets

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Our Central Mission

Get data from here to there from here to there

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Host-centric Protocols

Protocols defined in terms of IP addresses:- Unicast: IP address = host

- Multicast: IP address = set of hosts

Destination address is given to protocol

Protocol delivers data from one host to another- unicast: conceptually trivial

- multicast: address is logical, not physical

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Host-centric Applications

Classic applications: destination is “intrinsic”- telnet: target machine

- FTP: location of files

- electronic mail: email address turns into mail server

- multimedia conferencing: machines of participants

Destination is specified by user (not network)- Usually specified by hostname not address

DNS translates names into addresses

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Domain Name System (DNS)

DNS is built around recursive delegation- Top level domains (TLDs): .com, .net, .edu, etc.

- TLDs delegate authority to subdomains

• berkeley.edu

- Subdomains can further delegate

• cs.berkeley.edu

Hierarchy fits host administrative structure- Local decentralized control

- Crucial to efficient hostname resolution

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Network Research in Early 90’s

Consumed by a few obsessions:- Quality of service for streaming media

- Multicast

- Congestion control

But nobody questioned host-centricity:- assumed to be the only way to build Internet

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Surprise #1: The web catches on!

But we don’t....

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The Web

Web URLs have host-name/path format- Essentially the same information as FTP

Early web:- browsers basically a GUI for FTP

- URLs were easily transmitted pointers

Early web was host-centric- and largely ignored (but used) by net researchers

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Modern Web

URLs often function as names of data- users think of www.cnn.com as data, not a host

- Fact that www.cnn.com is a hostname is irrelevant

Users want data, not access to particular host

The web is now data-centric

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Data-centric App in Host-centric World

Data still associated with host names (URLs)- administrative structure of data same as hosts

- weak point in current web

Key enabler: search engines- Searchable databases map keywords to URLs

- Allowed users to find desired data

Networkers focused on technical problems:- HTTP, persistence (URNs), replication (CDNs), ...

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We Missed the Point!

We thought: web was an aberration search engines were a sufficient hack

No networker (except Jacobson) articulated that: web had gone from host-centric to data-centric it was a harbinger of future applications

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Surprise #2: Stolen Music is Popular!

And we finally get the message...

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The P2P Filesharing Phenomena

Napster: “Fastest growing Internet application”

Music sharing is intrinsically data-centric- data never associated with hosts

Centralized searchable database- listed IP addresses where content could be found- analogous to Google+DNS in the web

Legal problems forced decentralization- Led to Gnutella and other distributed programs

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Gnutella-style File Sharing

Gnutella nodes form an overlay network- each node has a few “neighbors” in a virtual network

- virtual link: node knows other’s IP address

- do app-level “networking” on this graph

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Gnutella-style Searching

Keyword queries are flooded (within scope)- query is processed locally at each node- all nodes having hits respond to source- many variations on this theme (freenet, etc.)

Clearly not scalable

P2P traffic now sizable fraction of overall load

We finally realize that we need a scalable way to find data for data-centric applications

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Is there life outside the Internet?

Yes, and we should have been listening!

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Sensornets (predating P2P)

Vision:- Many sensing devices with radio and processor

- Enable fine-grained measurements over large areas

- Huge potential impact on science, and society

Technical challenges:- untethered: power consumption must be limited

- unattended: robust and self-configuring

- wireless: ad hoc networking

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Conceptual Challenge

Sensornets are inherently data-centric- Users know what data they want, not where it is

- Estrin, Govindan, Heidemann (2000, etc.)

Centralized database infeasible- vast amount of data, constantly being updated

- small fraction of data will ever be queried

- sending to single site expends too much energy

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Flood-then-Aggregate

General class of methods: Flood query to all nodes (or in region) Nodes with data matching query respond Responses are aggregated as appropriate

Examples: Directed diffusion: reinforce based on data TAG: tree for flood and return-path aggregation Etc....

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Scaling Problems

This approach suffers as: systems get bigger queries more frequent and more specific

For current deployments, not an issue: systems are small, queries primitive

But if technology progresses as hoped: want to get relevant data without flooding similar to situation in Internet

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Is Data-centric Flooding Necessary?

The initial decentralized data-centric designs (in both Internet and sensornets) used flooding

- unscalable and unsustainable

Since data-centrism is here to stay, we can’t ignore this problem

We had to broaden our research charter

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Our Revised Mission

Get data from here to thereGet data from here to there

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A DNS for Data?

Can we map data names into addresses?- a data-centric DNS, distributed and scalable

- doesn’t alter net protocols, but aids data location

- not just about stolen music, but a general facility

A formidable challenge:- Data does not have a clear administrative hierarchy

- Likely need to support a flat namespace

- Can one do this scalably?

Data-centrism requires scalable flat lookups

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Distributed Hash Tables (DHTs)

The latest networking fad....

Presented from the Internet perspective but applies to sensornets as well

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An Internet-scale Distributed Index

Interface: put(key,object), get(key)

DHTs form a structured overlay network

nodes choose particular neighbors

all objects have keys, usually hash(name)

each node responsible for range of keys

puts/gets routed to appropriate node

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Example Design: Chord

Node and object keys: - random location around a circle

Neighbors: - nodes 2-i around the circle

- found by routing to desired key

Routing: greedy- pick nbr closest to destination

Storage: “own” interval- node owns key range between

her key and previous node’s key

Ownershiprange

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Key Properties

Large aggregate capacity: O(n) storage/b’width

Scalable: - O(log n) routing hops and state

- O((log n)2) update costs for node join/leaves

Robust: self-configuring and resilient to failures

Nonproperty: strict guarantees when failures

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Our Version of “Data Independence”

DHT interface allows us to get data by name- We no longer care where data is

A radical transition in databases- perhaps it will be one in networking as well

Apologies to Joe Hellerstein...- see latest SIGMOD Record for his article

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Caveat!

DHTs are a work-in-progress

A flurry of research activity on:- security

- replication

- proximity

- real operational experience

- .....

For rest of talk, we put these worries aside...

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Why Not Centralized Solutions?

Ugh! (and infeasible for sensornets)

Fault tolerance: avoid single point of failure

Economic:- DNS: “donated” machines, scales organically- Centralized solutions require business model

Issue still open....but irrelevant to data-centrism- need to support interface- DHTs allow us to choose between cent. and decent.

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Multiple Roles for DHTs

Application-specific- rolled into P2P application, run on “peers”

General-purpose service- run on managed nodes

Intrinsic part of Internet architecture- run on managed nodes

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Multiple Roles for DHTs

Application-specific- rolled into P2P app, run on “peers”

General-purpose service- run on managed nodes

Intrinsic part of Internet architecture- run on managed infrastructure nodes

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Some Applications using DHTs

Partial list: File sharing Storage repositories and file systems Backup systems Event notification systems Electronic mail App-layer multicast and streaming media .....

Useful substrate for many (not all) large distributed applications because HTs are useful

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Multiple Roles for DHTs

Application-specific- rolled into P2P app, run on “peers”

General-purpose service- run on managed nodes

Intrinsic part of Internet architecture- run on managed infrastructure nodes

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Internet-scale Query Processing

Superficial motivation:- Joins can be implemented with hash tables so...

- Distributed joins can be implemented with DHTs

- Scaling: latency O(log n) while computation O(n)

PIER (talk later today in session A9!):- joins, aggregation, recursive and continuous queries

Intended targets:- data “in the wild” (filesharing, net monitoring, etc.)

- schema provided by standardized protocols

- no need for ACID semantics

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More Complex Queries

Range search: using “prefix hash table” no need to walk tree

Keyword search: engineering the boolean approach

Active research on DHT-based distributed data structures for search (net and db communities)

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Multiple Roles for DHTs

Application-specific- rolled into P2P app, run on “peers”

General-purpose service- run on managed nodes

Intrinsic part of Internet architecture- run on managed infrastructure nodes

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Cleaning Up the Architecture

Making URNs a reality- webNG based on flat and opaque DHT keys

- enables persistence and eliminates branding

Host identifiers versus routing information- IP addresses currently (and stupidly) serve as both

- DHT key = host id, resolves to routing address

- Architectural challenge for basic protocols

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Subverting the Architecture

Use DHT for forwarding, not just lookup!- e.g., Internet Indirection Infrastructure (i3)- similar in spirit to multicast (logical addressing)- transcends current naming/addressing structures

Make overlay the real “network” layer- turn IP into a link layer technology

Leverages, not limited by, current infrastructure

New network layer is still simple, but not IP

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New Generation of Networking?

Current Internet relies on hierarchies to scale:- DNS naming, IP addressing, etc.

Hierarchies limit flexibility:- addresses and names have to fit given structure

- need to care “where” data/machines are

Scalable flat lookup avoids hierarchy- network would be “structure independent”

Less of a distinction between hosts and data

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Do DHTs Apply to Sensornets?

Can we build them?

Do they help?

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Finding Sensornet Data w/o Flooding

Extract high-level features or “events”- Temperature spikes, toxins, animal sightings- Name these events

Store/Access events with DHT-like structure- Can later get detailed data from specific nodes

Call this “data-centric storage” (DCS)- Good for frequent specific queries- Not good for long-running or aggregate queries

But how do you build a sensornet DHT?

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Geographic Routing

Nodes know own and neighbors’ positions Packets routed to geographic destination

- Greedy forwarding, when possible

- If greedy fails at a void, use the right hand rule to navigate around the void

A(x,y)

B

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“Geographic” Hash Table (GHT)

Keys hashed to random coordinates Likely no node exists at that location! Forwarding ends at node closest to destination Closest node stores the data

A (x,y)

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Additional Algorithms

Caching and replication- Cache around perimeter, replicate independently

Structured replication (SR)- Hierarchical decomposition of key space

- Tree of “mirror images”

Hash(event) Mirror Images

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More Complex Queries

Using GHT+SR: (which has spatial structure) Range searches in space and value Wavelet analysis

New data structures: Higher-dimensional range searches

Active research in distributed data structures for sensornet queries (in net and db communities)

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We are finally on our way to the land of “data independence”...

We ask for your guidance....

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Areas of Common Interest

Algorithmic:- distributed data structures for search

Metaphoric:- thinking about data independence