Rendezvous Points-Based Scalable Content Discovery with Load Balancing Jun Gao Peter Steenkiste...

Rendezvous Points-Based Scalable Content Discovery with Load Balancing

Jun Gao Peter Steenkiste

Computer Science DepartmentCarnegie Mellon University

October 24th, 2002

NGC 2002, Boston, USA

Jun Gao Carnegie Mellon University 2

Outline

Content Discovery System (CDS) Existing solutions CDS system design Simulation evaluation Conclusions


Content Discovery System (CDS)

Example: a highway monitoring service Cameras and sensors monitor

road and traffic status Users issue flexible queries

CDS enables content discovery Locate contents that match

queries

Example services Service discovery; P2P;

pub/sub; sensor networks

Snapshot from traffic.com


Comparison of Existing Solutions

Searchability

Centralized

Distributed

Registrationflooding

Querybroadcasting Hash-based

Graph-basedTree-based

Scalability

Robustness

yes yes yesHierarchicalnames

Look-up

yes? no no

yes

yes

no yes yes

yes

no

Load balancing yes? no no yes?no

DesignGoals

Solutions


CDS Design

Attribute-value pair based naming scheme Enable searchability

Peer-to-peer system architecture Robust distributed system

Rendezvous Points-based content discovery Improve scalability

Load Balancing Matrix Dynamic balance load


Naming Scheme

Based on Attribute-Value pairs CN: {a1=v1, a2=v2,..., an=vn}

Not necessarily hierarchical Attribute can be dynamic

Searchable via subset matching Q CN Number of matches for a CN is large

2n-1

Camera ID = 5562Highway = I-279 Exit = 4City = PittsburghSpeed = 25mphRoad condition = Icy

Highway = I-279 Exit = 4City = Pittsburgh

Q1

Q2

CN1

City = PittsburghSpeed = 25mph


Distributed Infrastructure

Hash-based overlay substrate Routing, forwarding, management Node ID Hash function H(node)

Application layer publishes contents or issues queries

CDS layer determines where to register contents and send queries Centralized and network-wide

flooding are not scalable Idea: use a small set of nodes

as Rendezvous Points

TCP/IP

Hash-based Overlay

CDS

Application

N1

N6

N4N5

N3N2

N9

N8N7


RP-based Scheme

Hash each AV-pair to get a set of RPs | RP | = n

RP node stores names that share the same pair Maintain with soft state

Query is sent directly to an RP node Use the least loaded RP RP node fully resolves

locally

N3N5

CN1

N4 N6

CN1: {a1=v1, a2=v2, a3=v3, a4=v4}

RP2

CN2

CN2: {a1=v1, a2=v2, a5=v5, a6=v6}

N2N1

RP1

Ni H(ai=vi)

N7N8

N9

?

Q:{a1=v1, a2=v2}


System Properties

Efficient registration and query O(n) registration messages; n small O(m) messages for query with probing

Hashing AV-pair individually ensures subset matching Query may contain only 1 AV-pair

No inter-RP node communication for query resolution Tradeoff between CPU and Bandwidth

Load is spread across nodes Different names use different RP set


Load Concentration Problem

RP node may be overloaded Some AV-pairs more popular than

others Speed=55mph vs. Speed=95mph P2P keyword follows Zipf

distribution However, many nodes are

underutilized Intuition: use a set of nodes to

share load caused by popular pairs

Challenge: accomplish load balancing in a distributed and self-adaptive fashion

10

100

1000

10000

100000

1 10 100 1000 10000

AV-pair rank

#of names

Example Zipf distribution


Load Balancing Matrix (LBM)

Organize nodes into a logical matrix Each column holds a partition Rows are replicas of each

other

Node IDs are determined by: H(a1=v1, p, r) N1

(p,r)

Matrix expands itself to accommodate extra load Increase P when registration

load reaches threshold Query load R

1,1 2,1 3,1

1,2 2,2 3,2

1,3 2,3 3,3

CN1CN2 CN3 CN4 CN3 CN4

Q1Q2

Q3Q4

Q3

Q4

LBM for AV-pair: {a1=v1}

R

P


Registration and Query with LBM

Determine LBM size Register with one random

column of each matrix Compute IDs locally

1,1

1,2

2,1

2,2

1,3 2,3

CN1:{a1v1, a2v2, a3v3}

0,0

P=?, R=?

P=3, R=3

LBM1

LBM2

LBM3

3,1

3,2

3,3

Q:{a1v1, a2v2}Load is balanced within an LBM

Registration

Query can be sent to the matrix of any AV-pair Use LBM with min(P)

Sent to one random node in each column

Query


System Properties with LBM

Registration and query cost for one pair increases O(R) registration messages O(P) query messages Matrix size depends on current load

LBM must be kept small for efficiency Query optimization helps, e.g., large P small R Matrix shrinking mechanism E.g., May query a subset of the partitions

Load on each RP node is upper-bounded Efficient processing

Underutilized nodes are recruited as LBM expands


Simulation Evaluation

Implement in an event-driven simulator Each node monitors its registration and query load Assume Chord-like underlying routing mechanism

Experiment setup 10,000 nodes in the CDS network 10,000 distinct AV-pairs (50 attributes, 200 values/attribute) Use synthetic registration and query workload

Performance metric: success rate System should maintain high success rate as load increases


Workload

10

100

1000

10000

100000

1 10 100 1000 10000

Uniform

Skewed

1

10

100

1000

10000

100000

1 10 100 1000 10000

Rank of AV-pairs Rank of AV-pairs

Num

ber

of n

ames

Num

ber

of q

uerie

s

Registration load Query load


Registration Success Rate Comparison

0

20

40

60

80

100

0.01 0.1 1 10

Uniform w/o LBM

Uniform w. LBM

Skewed w/o LBM

Skewed w. LBM

Avg. registration arrival rate (1000 reg/sec)

Reg

istr

atio

n su

cces

s ra

te (

%)

Threshold = 50 reg/secPoisson arrivalPmax = 10

100,000 names 1 registration every 10 secs.


40

60

80

100

0.01 0.1 1 10 100

Random

Random w. LBM

With opt.

Opt. with LBM

Query Success Rate Comparison

Avg. query arrival rate (1000q/sec)

Que

ry s

ucce

ss r

ate

(%)

1 million users, 1 query every 10 secs.

Threshold = 200 q/secPoisson arrivalRmax = 10


Conclusions

Proposed a distributed and scalable design to the content discovery problem

RP-based approach addresses scalability Avoid flooding

LBMs improve system throughput Balance load

Distributed algorithms Decisions are made locally

Rendezvous Points-Based Scalable Content Discovery with Load Balancing Jun Gao Peter Steenkiste...

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