Declarative Networking:

Language, Execution and Optimization

Boon Thau Loo1, Tyson Condie1, Minos Garofalakis2, David E. Gay2, Joseph M. Hellerstein1, Petros Maniatis2, Raghu Ramakrishnan3, Timothy Roscoe2, Ion Stoica1

1UC Berkeley, 2Intel Research Berkeley, 3University of Wisconsin-Madison

Declarative Networking

Database query language, execution and optimization for the design and implementation of networksSuccess of database research: 70’s – today: Database research has

revolutionized data management Today: Similar opportunity to

revolutionize the Internet architecture

Why now?

Internet faces many challenges today: Unwanted, harmful traffic Complexity/fragility in Internet routing Proliferation of new applications

Efforts at improving the Internet: Evolutionary: App-level “Overlay” networks Revolutionary: Clean-slate designs

NSF GENI initiative, FIND program

Opportunity: Software tools that can significantly accelerate network

innovation

Opportunity: Software tools that can significantly accelerate network

innovation

A Declarative Network

Distributed recursive query

Traditional Networks Declarative NetworksNetwork State Distributed

databaseNetwork protocol Recursive Query Execution

Network messages Distributed Dataflow

DataflowDataflow

messages

Dataflow

Dataflow

Dataflow

Dataflow messagesmessages

Previous Work and Use Cases

Declarative routing [SIGCOMM ’05]: Recursive queries as a compact, high-level

representation of routing protocols Good balance between router extensibility and

safetyBeyong routing: Declarative overlays [SOSP ’05]:

P2 declarative networking system implementation Chord overlay network (47 lines)

System being used: Code pre-release (http://p2.cs.berkeley.edu) Cambridge, Harvard, MPI, Rice, UT-Austin Distributed consensus protocols, replication

protocols, debugging networks, content-based routing

Focus of this paper:

Important unresolved issues: Query language and semantics Asynchronous, distributed query

processing New challenges in query

optimizations Semantics in dynamic networks

Outline

BackgroundQuery language by exampleQuery processingOptimizationsConclusion

Review of Datalog

<result> <condition1>, <condition2>, … , <conditionN>.

Datalog rule syntax:

Types of conditions in body: Input tables: link(src,dst) predicate Arithmetic and list operations

Head is an output table Recursive rules: result of head in rule body

BodyHead

Review: All-Pairs Reachability

R2: reachable(S,D) link(S,Z), reachable(Z,D)

R1: reachable(S,D) link(S,D)

Input: link(source, destination)Output: reachable(source, destination)

“For all nodes S,D, If there is a link from S to D, then S can reach D”.

link(a,b) – “there is a link from node a to node b”

reachable(a,b) – “node a can reach node b”

Review: All-Pairs Reachability

R2: reachable(S,D) link(S,Z), reachable(Z,D)

R1: reachable(S,D) link(S,D)

Input: link(source, destination)Output: reachable(source, destination)

“For all nodes S,D and Z, If there is a link from S to Z, AND Z can reach D, then S can reach D”.

All-Pairs Reachability

R1: reachable(@S,D) link(@S,D)

R2: reachable(@S,D) link(@S,Z), reachable(@Z,D)

Network Datalog

Query: reachable(@M,N)

@S

D

@a

b

@a

c

@a

d

reachableOutput

table:

Input table:

Query: reachable(@a,N)

@S

D

@c b

@c d

link@

SD

@b

c

@b

a

link@

SD

@a

b

link @

SD

@d

c

link

b dca

@S

D

@b

a

@b

c

@b

d

reachable @

SD

@c a

@c b

@c d

reachable @

SD

@d

a

@d

b

@d

c

reachable

Location Specifier “@S”

Query: reachable(@a,N)

Implicit Communication

A networking language with no explicit communication:

All communication happens among neighbors: Link-restricted rules enforced via syntactic restrictions

R2: reachable(@S,D) link(@S,Z), reachable(@Z,D)

Data placement induces communication

b catuple(@b,…)

Path Vector Protocol Example

Advertisement: entire path to a destinationEach node receives advertisement, add itself to path and forward to neighbors

path=[c,d]path=[b,c,d]path=[a,b,c,d]

c advertises [c,d]b advertises [b,c,d]

b dca

Path Vector in Network Datalog

Input: link(@source, destination)Query output: path(@source, destination, pathVector)

R1: path(@S,D,P) link(@S,D), P=(S,D).

R2: link(@Z,S), path(@S,D,P)

P=SP2. path(@Z,D,P2),

Query: path(@S,D,P)

Add S to front of P2

R1: path(@S,D,P) link(@S,D), P=(S,D).

R2: path(@S,D,P) link(@Z,S), path(@Z,D,P2), P=SP2.

@S

D P @S

D P

@c d [c,d]

Query Execution

@S

D P @S

D P

Query: path(@a,d,P)

Neighbor table:

@S

D

@c b

@c d

link@S D

@b c

@b a

link@

SD

@a

b

link @S D

@d c

link

b dca

path path path

Forwarding table:

@S

D P @S

D P @S

D P

@c d [c,d]

Query Execution

Forwarding table:

@S

D P

@b

d [b,c,d]

b dca

path(@b,d,[b,c,d])

R1: path(@S,D,P) link(@S,D), P=(S,D).

R2: path(@S,D,P) link(@Z,S), path(@Z,D,P2), P=SP2.

Query: path(@a,d,P)

Neighbor table:

@S

D

@c b

@c d

link@S D

@b c

@b a

link@

SD

@a

b

link @S D

@d c

link

path path path@S

D P

@a

d [a,b,c,d]

path(@a,d,[a,b,c,d])

Communication patterns are identical to those in the actual path vector

protocol

Communication patterns are identical to those in the actual path vector

protocol

Matching variable Z = “Join”

Outline

BackgroundQuery language by exampleQuery ProcessingOptimizationsConclusion

Recursive Query Evaluation

Semi-naïve evaluation: Iterations (rounds) of synchronous computation Results from iteration ith used in (i+1)th

Path Table

87

3-hop

109

21

1-hop3

65 2-hop4

Link Table Network

510

021

3

4

6

8

7

Problem: Unpredictable delays and failures

9

Pipelined Semi-naïve (PSN)Fully-asynchronous evaluation:

Computed tuples in any iteration pipelined to next iteration

Natural for network protocols

Path Table

41

7

Link Table Network

25836910

510

021

3

4

6

8

79

Relaxation of semi-naïve

Relaxation of semi-naïve

Pipelined Evaluation

Challenges: Does PSN produce the correct answer? Is PSN bandwidth efficient?

I.e. does it make the minimum number of inferences?

In paper, proofs for

Basic technique: local timestamps

p(x,z) :- p1(x,y), p2(y,z), …, pn(y,z), q(z,w)

recursive w.r.t. p

lookup

lookup

Dem

ux

link

Local Tables

path ...

UD

P

Tx

Round

Robin

Queue

CC

T

x

Queue

UD

P

Rx

CC

R

x

Execution Plan

Nodes in execution plan (“operators”): Network operators (send/recv, cc, retry, rate limitation) Relational operators (selects, projects, joins, aggregates) Flow operators (mux, demux, queues)

Messages

Network In

Messages

Network Out

Single Node

Localization RewriteRules may have body predicates at different locations:

R2: path(@S,D,P) link(@S,Z), path(@Z,D,P2), P=SP2.

R2b: path(@S,D,P) linkD(S,@Z), path(@Z,D,P2), P=SP2.

R2a: linkD(S,@D) link(@S,D)

Matching variable Z = “Join”

Rewritten rules:

Matching variable Z = “Join”

Localized Rule Compilation

Execution Plan

path Joinpath.Z = linkD.Z

linkD

Projectpath(S,D,P) Send to

path.S

R2b: path(@S,D,P) linkD(S,@Z), path(@Z,D,P2), P=SP2.

Netw

ork

In

Netw

ork

Ou

t

linkD

JoinlinkD.Z =

path.Z

path

Projectpath(S,D,P) Send to

path.S

Outline

BackgroundQuery language by exampleQuery Processing OptimizationsConclusion

Role of Query Optimizations

Network protocols = query executionCan query optimizations help implement efficient protocols?

Our First Steps

Traditional: evaluate in the NW context Aggregate Selections Predicate Reordering Magic Sets rewrite

New: motivated in the NW context Multi-query optimizations Cost-based optimizations based on

network statistics

Predicate Reordering

R1: path(@S,D,P) link(@S,D), P= (S,D). R2: path(@S,D,P) Query: path(@S,D,P)

link(@S,Z), path(@Z,D,P2),P=SP2.

Predicate Reordering

R1: path(@S,D,P) link(@S,D), P= (S,D). R2: path(@S,D,P) Query: path(@S,D,P)

path(@S,Z,P1), link(@Z,D), P=SP2.P=P1D.

Predicate reordering: path vector protocol dynamic source routingInteresting variants: Predicate reordering + magic-sets rewrite Cost-based optimizations (work-in-progress)

Network statistics (neighborhood density, rate of change of links)

Evaluation Overview

Setup: Routing protocols implemented using P2 Emulab testbed Metrics: Convergence latency,

communication

Results in paper: Aggregate selections Magic sets & predicate reordering Multi-query optimizations

ConclusionDeclarative Networking Database techniques for network design and

implementation Important role to play in the innovation of networks

Paper focuses on important unresolved issues: Query language, query processing, optimizations,

semantics in dynamic networks

Raises several interesting research challenges: Language and semantics Runtime cost-based optimizations Interaction between query processing and

networking

Thank Youhttp://p2.cs.berkeley.edu