a prediction-based fair replication algorithm in structured p2p systems xianshu zhu, dafang zhang,...

A Prediction-based Fair Replication Algorithm in Structured P2P Systems

Xianshu Zhu, Dafang Zhang, Wenjia Li, Kun HuangXianshu Zhu, Dafang Zhang, Wenjia Li, Kun Huang

Presented by: Xianshu ZhuPresented by: Xianshu ZhuCollege of Computer & Communication, Hunan University, P.R.ChinaCollege of Computer & Communication, Hunan University, P.R.China

OutlineOutline

IntroductionIntroductionContributionContributionPFR (Prediction-based Fair PFR (Prediction-based Fair Replication)Replication)Performance EvaluationPerformance EvaluationConclusion and Future WorkConclusion and Future Work

IntroductionIntroduction

Query Hotspot Query Hotspot

Structured Peer-to-Peer NetworkStructured Peer-to-Peer Network

Summary of Replication SchemesSummary of Replication Schemes

Query HotspotQuery Hotspot

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Query Hotspot: the number of requests for Query Hotspot: the number of requests for popular objects increases dramatically, and popular objects increases dramatically, and leads to consequent dropping queries and leads to consequent dropping queries and severe performance failures.severe performance failures.

Query Hotspot

Structured P2P Structured P2P NetworkNetwork

AdvantageAdvantage ：： - Scalability - Scalability - Efficient Searching- Efficient Searching

DisadvantageDisadvantage ：： The Implementation of StructureThe Implementation of Structured P2P Network Assumes that All Data Items are of d P2P Network Assumes that All Data Items are of the Same Popularity. No Mechanism Can Handle Hthe Same Popularity. No Mechanism Can Handle Hotspot Problemotspot Problem

Replication SchemesReplication Schemes

Basic IdeaBasic Idea ：： - Distribute Replicas of the Popular Data Items to Vari- Distribute Replicas of the Popular Data Items to Vari

ous Light-loaded Nodesous Light-loaded Nodes - - FairlyFairly Distribute Load onto Each Node.Distribute Load onto Each Node.

When Apply Replication Technique: When Apply Replication Technique: －－ Replica Creation: Time, Number, LocationReplica Creation: Time, Number, Location －－ Replica UtilizationReplica Utilization

Replication SchemesReplication Schemes

Classification According to Replica Location:Classification According to Replica Location:

- Path Replication- Path Replication

- Owner Replication- Owner Replication

- Random Replication- Random Replication

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High Replication High Replication OverheadOverhead

Replication SchemesReplication Schemes

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1.New Query Hotspot1.New Query Hotspot2.Low Replication Speed2.Low Replication Speed

Classification According to Replica Location:Classification According to Replica Location: - Path Replication- Path Replication - Owner Replication: Gopalakrishnan proposed LAR- Owner Replication: Gopalakrishnan proposed LAR - Random Replication- Random Replication

File BFile BFile BFile B File DFile DFile DFile D File DFile DFile DFile DFile BFile BFile BFile BFile AFile AFile AFile A

Replication SchemesReplication Schemes

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Classification According to Replica Location:Classification According to Replica Location:

- Path Replication- Path Replication

- Owner Replication- Owner Replication

- Random Replication- Random Replication

OutlineOutline

IntroductionIntroductionContributionContributionPFR (Prediction-based Fair PFR (Prediction-based Fair Replication)Replication)Performance EvaluationPerformance EvaluationConclusion and Future WorkConclusion and Future Work

Contribution

Design Goals:Design Goals: - Dropped Queries by Only Introducing - Dropped Queries by Only Introducing

Minimum Replication OverheadMinimum Replication Overhead - - Minimize the Drawbacks of LAR AlgorithmMinimize the Drawbacks of LAR Algorithm

(Owner Replication)(Owner Replication)

Prediction-based Fair Replication Algorithm Prediction-based Fair Replication Algorithm (PFR) that Can Almost Fairly Distribute Load (PFR) that Can Almost Fairly Distribute Load onto Each Node, So As to Meet the Above onto Each Node, So As to Meet the Above Design Goal. Design Goal.

Contribution

Fairness Goal of PFR -Adaptively Determine the Replication Speed and Replication

Location According to Node’s Predicted Load Fraction

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OutlineOutline

IntroductionIntroductionContributionContributionPFR (Prediction-based Fair PFR (Prediction-based Fair Replication)Replication)Performance EvaluationPerformance EvaluationConclusion and Future WorkConclusion and Future Work

Predict(n+1)Predict(n+1)

PFR- PFR- Appropriate Replication Appropriate Replication

TimeTime

To keep the System Performance at a High Level, PrTo keep the System Performance at a High Level, Preventive Actions Should be Taken Before Query Hoteventive Actions Should be Taken Before Query Hotspot Really Happensspot Really HappensPeriod Exponential Weight Prediction AlgorithmPeriod Exponential Weight Prediction Algorithm

Predict(n+1)=Current(n) + PredictDiff(n+1)Predict(n+1)=Current(n) + PredictDiff(n+1)

12nn+1

n-1

Current Current TimeTime

Predicted Possible Traffic Difference Between nth and (n+1)th Predicted Possible Traffic Difference Between nth and (n+1)th IntervalInterval

Period Exponential Weight Prediction Period Exponential Weight Prediction AlgorithmAlgorithm

- Only Incurs Low Computation Overhead- Only Incurs Low Computation Overhead - Applicable to Online Prediction- Applicable to Online Prediction

Our Replication Strategy is Set Based on Our Replication Strategy is Set Based on The Predicted loadThe Predicted load

PFR- Appropriate Replication PFR- Appropriate Replication

TimeTime

Replication Speed:Replication Speed:

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3/63/6

Replication SpeedReplication Speed=(the Number of Nodes Chosen =(the Number of Nodes Chosen to Hold Replicas)/(the Number of All Nodes that to Hold Replicas)/(the Number of All Nodes that Have Encountered Along the Query Path)Have Encountered Along the Query Path)

PFR- Fairly-decided Replication PFR- Fairly-decided Replication SpeedSpeed

Replication Level:Replication Level:

NN

N/2N/23N/43N/4

N/4N/411DON’T create DON’T create replicasreplicas

N: Total Number of Nodes Along a Query N: Total Number of Nodes Along a Query PathPath

PFR- Fairly-decided Replication PFR- Fairly-decided Replication SpeedSpeed

Replication Replication SpeedSpeed

Predicted Load Predicted Load FractionFraction

(0.5)(0.5)

(0.3)(0.3)

(0.6(0.6))

(0.7(0.7))

(0.8(0.8))(1)(1)

Node Homogeneity

PFR- Replication & Replica PFR- Replication & Replica UtilizationUtilization

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C: C: FileFile

F:0.25F:0.25E:0.15E:0.15F:0.25F:0.25E:0.15E:0.15

F:0.25F:0.25

D:0.3D:0.3C:0.55C:0.55

E:0.15E:0.15F:0.25F:0.25

D:0.3D:0.3B:0.3B:0.3C:0.55C:0.55

E:0.15E:0.15F:0.25F:0.25

D:0.3D:0.3

A:0.9A:0.9B:0.3B:0.3C:0.55C:0.55

E:0.15E:0.15F:0.25F:0.25

D:0.3D:0.3

RS:N/4=1RS:N/4=1

A: FileA: FileA: FileA: File A: FileA: FileA: FileA: File

A: FileA: FileA: FileA: File

A: FileA: FileA: FileA: File A: FileA: FileA: FileA: File

RS:NRS:N

E:CE:CE:CE:C

E:CE:C

B,D,E,F:AB,D,E,F:A

B,D,E,F:AB,D,E,F:AB,D,E,F:AB,D,E,F:A

B,D,E,F:AB,D,E,F:A

B,D,E,F:AB,D,E,F:A

B,D,E,F:AB,D,E,F:A

D:AD:A N=6N=6

OutlineOutline

IntroductionIntroductionContributionContributionPFR (Prediction-based Fair PFR (Prediction-based Fair Replication)Replication)Performance EvaluationPerformance EvaluationConclusion and Future WorkConclusion and Future Work

Performance EvaluationPerformance Evaluation

Highly modified Chord Simulator from MIT and Highly modified Chord Simulator from MIT and LAR Implementation CodeLAR Implementation Code ：：

System SizeSystem Size 10001000 The Time Each The Time Each Network hop Network hop takestakes

25ms25ms

Number of Number of datadata

3276732767 Average system Average system loadload

25%25%

Node capacityNode capacity 10 per 10 per secsec

Number of Number of Queries Queries Generate per Generate per SecSec

500500

Node’s queue Node’s queue lengthlength

3232 Prediction Prediction intervalinterval

1s1s

Performance EvaluationPerformance Evaluation

Number of Queries Dropped Over Number of Queries Dropped Over TimeTime

28%28%

90% of the input queries are directed to 190% of the input queries are directed to 1 itemitem

LARLAR

PFRPFR

Performance EvaluationPerformance Evaluation

Total Number of Documents ReplicatedTotal Number of Documents Replicated

LARLAR

PFRPFR

Performance EvaluationPerformance Evaluation

Total Number of Finger Tables Total Number of Finger Tables ReplicatedReplicated

LARLAR

PFRPFR

Performance EvaluationPerformance Evaluation

Total Number of Replica Location Hints Total Number of Replica Location Hints CreatedCreated

PFRPFR

LARLAR

OutlineOutline

IntroductionIntroductionContributionContributionPFR (Prediction-based Fair PFR (Prediction-based Fair Replication)Replication)Performance EvaluationPerformance EvaluationConclusion and Future WorkConclusion and Future Work

ConclusionConclusion

Prediction-based Fair Replication Algorithm Prediction-based Fair Replication Algorithm Can Conduct Fair Replication through:Can Conduct Fair Replication through:

- Appropriate Replication Time- Appropriate Replication Time - Fairly-decided Replication Speed- Fairly-decided Replication Speed - Fairly-decided Replication Location- Fairly-decided Replication Location - High Replica Utilization Rate- High Replica Utilization Rate

Performance Evaluation:Performance Evaluation: - Notably Decrease the Number of Dropped - Notably Decrease the Number of Dropped

QueriesQueries - Low Replication Overhead- Low Replication Overhead

Future WorkFuture Work

Taking Node Heterogeneity into ConsiderationTaking Node Heterogeneity into Consideration

Thank you!Thank you!