Presentation is loading. Please wait.

Presentation is loading. Please wait.

A Prediction-based Fair Replication Algorithm in Structured P2P Systems Xianshu Zhu, Dafang Zhang, Wenjia Li, Kun Huang Presented by: Xianshu Zhu College.

Published byJulia Diane Chambers Modified over 9 years ago

Similar presentations

Presentation on theme: "A Prediction-based Fair Replication Algorithm in Structured P2P Systems Xianshu Zhu, Dafang Zhang, Wenjia Li, Kun Huang Presented by: Xianshu Zhu College."— Presentation transcript:

1 A Prediction-based Fair Replication Algorithm in Structured P2P Systems Xianshu Zhu, Dafang Zhang, Wenjia Li, Kun Huang Presented by: Xianshu Zhu College of Computer & Communication, Hunan University, P.R.China

2 Outline IntroductionContribution PFR (Prediction-based Fair Replication) Performance Evaluation Conclusion and Future Work

3 Introduction Query Hotspot Structured Peer-to-Peer Network Summary of Replication Schemes

4 Query Hotspot F G I J C D E H B FileFile Query Hotspot: the number of requests for popular objects increases dramatically, and leads to consequent dropping queries and severe performance failures. Query Hotspot

5 Structured P2P Network Advantage ： - Scalability - Scalability - Efficient Searching - Efficient Searching Disadvantage ： The Implementation of Structured P2P Network Assumes that All Data Items are of the Same Popularity. No Mechanism Can Handle Hotspot Problem

6 Replication Schemes Basic Idea ： - Distribute Replicas of the Popular Data Items to Various Light-loaded Nodes - Distribute Replicas of the Popular Data Items to Various Light-loaded Nodes - Fairly Distribute Load onto Each Node. - Fairly Distribute Load onto Each Node. When Apply Replication Technique: － Replica Creation: Time, Number, Location － Replica Creation: Time, Number, Location － Replica Utilization － Replica Utilization

7 Replication Schemes Classification According to Replica Location: - Path Replication - Path Replication - Owner Replication - Owner Replication - Random Replication - Random Replication A BCDEF FileFileFileFileFileFileFileFileFileFileFileFile High Replication Overhead

8 Replication Schemes A BCDEF File A 1.New Query Hotspot 2.Low Replication Speed Classification According to Replica Location: - Path Replication - Path Replication - Owner Replication: Gopalakrishnan proposed LAR - Owner Replication: Gopalakrishnan proposed LAR - Random Replication - Random Replication File B File D File B File A

9 Replication Schemes A BCDEF FileFileFileFileFileFileFileFile Classification According to Replica Location: - Path Replication - Path Replication - Owner Replication - Owner Replication - Random Replication - Random Replication

10 Outline IntroductionContribution PFR (Prediction-based Fair Replication) Performance Evaluation Conclusion and Future Work

11 Contribution Design Goals: - Dropped Queries by Only Introducing Minimum Replication Overhead - Dropped Queries by Only Introducing Minimum Replication Overhead - Minimize the Drawbacks of LAR Algorithm (Owner Replication) - Minimize the Drawbacks of LAR Algorithm (Owner Replication) Prediction-based Fair Replication Algorithm (PFR) that Can Almost Fairly Distribute Load onto Each Node, So As to Meet the Above Design Goal.

12 Contribution Fairness Goal of PFR -Adaptively Determine the Replication Speed and Replication Location According to Node’s Predicted Load Fraction A BCDEFG

13 Outline IntroductionContribution PFR (Prediction-based Fair Replication) Performance Evaluation Conclusion and Future Work

14 Predict(n+1) PFR- Appropriate Replication Time To keep the System Performance at a High Level, Preventive Actions Should be Taken Before Query Hotspot Really Happens Period Exponential Weight Prediction Algorithm Predict(n+1)=Current(n) + PredictDiff(n+1) Predict(n+1)=Current(n) + PredictDiff(n+1) 12 nn+1n-1 Current Time Predicted Possible Traffic Difference Between nth and (n+1)th Interval

15 Period Exponential Weight Prediction Algorithm - 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 The Predicted load PFR- Appropriate Replication Time

16 Replication Speed: A BCDEF FileFile FileFileFileFileFileFile 3/6 Replication Speed=(the Number of Nodes Chosen to Hold Replicas)/(the Number of All Nodes that Have Encountered Along the Query Path) PFR- Fairly-decided Replication Speed

17 Replication Level: NN/2 3N/4 N/4 1 DON’T create replicas N: Total Number of Nodes Along a Query Path PFR- Fairly-decided Replication Speed Replication Speed Predicted Load Fraction (0.5) (0.3) (0.6) (0.7) (0.8) (1) Node Homogeneity

18 PFR- Replication & Replica Utilization ABCDEF G C: File F:0.25E:0.15F:0.25 E:0.15 F:0.25D:0.3 C:0.55E:0.15 F:0.25 D:0.3B:0.3C:0.55 E:0.15 F:0.25 D:0.3A:0.9B:0.3 C:0.55 E:0.15 F:0.25 D:0.3 RS:N/4=1 A: File RS:N E:C E:C E:C B,D,E,F:A B,D,E,F:A B,D,E,F:A B,D,E,F:A B,D,E,F:A B,D,E,F:A D:A N=6

19 Outline IntroductionContribution PFR (Prediction-based Fair Replication) Performance Evaluation Conclusion and Future Work

20 Performance Evaluation Highly modified Chord Simulator from MIT and LAR Implementation Code ： Highly modified Chord Simulator from MIT and LAR Implementation Code ： System Size 1000 The Time Each Network hop takes 25ms Number of data 32767 Average system load 25% Node capacity 10 per sec Number of Queries Generate per Sec 500 Node’s queue length 32 Prediction interval 1s

21 Performance Evaluation Number of Queries Dropped Over Time 28% 90% of the input queries are directed to 1 item LAR PFR

22 Performance Evaluation Total Number of Documents Replicated LAR PFR

23 Performance Evaluation Total Number of Finger Tables Replicated LAR PFR

24 Performance Evaluation Total Number of Replica Location Hints Created PFR LAR

25 Outline IntroductionContribution PFR (Prediction-based Fair Replication) Performance Evaluation Conclusion and Future Work

26 Conclusion Prediction-based Fair Replication Algorithm 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: - Notably Decrease the Number of Dropped Queries - Notably Decrease the Number of Dropped Queries - Low Replication Overhead - Low Replication Overhead

27 Future Work Taking Node Heterogeneity into Consideration

28 Thank you!

Download ppt "A Prediction-based Fair Replication Algorithm in Structured P2P Systems Xianshu Zhu, Dafang Zhang, Wenjia Li, Kun Huang Presented by: Xianshu Zhu College."

Similar presentations

Ch. 12 Routing in Switched Networks

Ch. 12 Routing in Switched Networks
Supporting Cooperative Caching in Disruption Tolerant Networks

Supporting Cooperative Caching in Disruption Tolerant Networks
Ch. 12 Routing in Switched Networks. 12.1 Routing in Packet Switched Networks Routing Algorithm Requirements –Correctness –Simplicity –Robustness--the.

Ch. 12 Routing in Switched Networks Routing in Packet Switched Networks Routing Algorithm Requirements –Correctness –Simplicity –Robustness--the.
Data and Computer Communications Ninth Edition by William Stallings Chapter 12 – Routing in Switched Data Networks Data and Computer Communications, Ninth.

Data and Computer Communications Ninth Edition by William Stallings Chapter 12 – Routing in Switched Data Networks Data and Computer Communications, Ninth.
Modeling and Analysis of Random Walk Search Algorithms in P2P Networks Nabhendra Bisnik, Alhussein Abouzeid ECSE, Rensselaer Polytechnic Institute.

Modeling and Analysis of Random Walk Search Algorithms in P2P Networks Nabhendra Bisnik, Alhussein Abouzeid ECSE, Rensselaer Polytechnic Institute.
Technion –Israel Institute of Technology Computer Networks Laboratory A Comparison of Peer-to-Peer systems by Gomon Dmitri and Kritsmer Ilya under Roi.

Technion –Israel Institute of Technology Computer Networks Laboratory A Comparison of Peer-to-Peer systems by Gomon Dmitri and Kritsmer Ilya under Roi.
Search and Replication in Unstructured Peer-to-Peer Networks Pei Cao, Christine Lv., Edith Cohen, Kai Li and Scott Shenker ICS 2002.

Search and Replication in Unstructured Peer-to-Peer Networks Pei Cao, Christine Lv., Edith Cohen, Kai Li and Scott Shenker ICS 2002.
What’s the Problem Web Server 1 Web Server N Web system played an essential role in Proving and Retrieve information. Cause Overloaded Status and Longer.

What’s the Problem Web Server 1 Web Server N Web system played an essential role in Proving and Retrieve information. Cause Overloaded Status and Longer.
Farnoush Banaei-Kashani and Cyrus Shahabi Criticality-based Analysis and Design of Unstructured P2P Networks as “ Complex Systems ” Mohammad Al-Rifai.

Farnoush Banaei-Kashani and Cyrus Shahabi Criticality-based Analysis and Design of Unstructured P2P Networks as “ Complex Systems ” Mohammad Al-Rifai.
Small-world Overlay P2P Network

Small-world Overlay P2P Network
Mohamed Hefeeda 1 School of Computing Science Simon Fraser University, Canada Multimedia Streaming in Dynamic Peer-to-Peer Systems and Mobile Wireless.

Mohamed Hefeeda 1 School of Computing Science Simon Fraser University, Canada Multimedia Streaming in Dynamic Peer-to-Peer Systems and Mobile Wireless.
1 A Vehicle Route Management Solution Enabled by Wireless Vehicular Networks Kevin Collins and Gabriel-Miro Muntean IEEE INFOCOM 2008.

1 A Vehicle Route Management Solution Enabled by Wireless Vehicular Networks Kevin Collins and Gabriel-Miro Muntean IEEE INFOCOM 2008.
1 Data Persistence in Large-scale Sensor Networks with Decentralized Fountain Codes Yunfeng Lin, Ben Liang, Baochun Li INFOCOM 2007.

1 Data Persistence in Large-scale Sensor Networks with Decentralized Fountain Codes Yunfeng Lin, Ben Liang, Baochun Li INFOCOM 2007.
Technion –Israel Institute of Technology Software Systems Laboratory A Comparison of Peer-to-Peer systems by Gomon Dmitri and Kritsmer Ilya under Roi Melamed.

Technion –Israel Institute of Technology Software Systems Laboratory A Comparison of Peer-to-Peer systems by Gomon Dmitri and Kritsmer Ilya under Roi Melamed.
Beneficial Caching in Mobile Ad Hoc Networks Bin Tang, Samir Das, Himanshu Gupta Computer Science Department Stony Brook University.

Beneficial Caching in Mobile Ad Hoc Networks Bin Tang, Samir Das, Himanshu Gupta Computer Science Department Stony Brook University.
Data-Centric Energy Efficient Scheduling for Densely Deployed Sensor Networks IEEE Communications Society 2004 Chi Ma, Ming Ma and Yuanyuan Yang.

Data-Centric Energy Efficient Scheduling for Densely Deployed Sensor Networks IEEE Communications Society 2004 Chi Ma, Ming Ma and Yuanyuan Yang.
Peer-to-Peer Based Multimedia Distribution Service Zhe Xiang, Qian Zhang, Wenwu Zhu, Zhensheng Zhang IEEE Transactions on Multimedia, Vol. 6, No. 2, April.

Peer-to-Peer Based Multimedia Distribution Service Zhe Xiang, Qian Zhang, Wenwu Zhu, Zhensheng Zhang IEEE Transactions on Multimedia, Vol. 6, No. 2, April.
Design and Performance Evaluation of Queue-and-Rate-Adjustment Dynamic Load Balancing Policies for Distributed Networks Zeng Zeng, Bharadwaj, IEEE TRASACTION.

Design and Performance Evaluation of Queue-and-Rate-Adjustment Dynamic Load Balancing Policies for Distributed Networks Zeng Zeng, Bharadwaj, IEEE TRASACTION.
Efficient, Proximity-Aware Load Balancing for DHT-Based P2P Systems Yingwu Zhu, Yiming Hu Appeared on IEEE Trans. on Parallel and Distributed Systems,

Efficient, Proximity-Aware Load Balancing for DHT-Based P2P Systems Yingwu Zhu, Yiming Hu Appeared on IEEE Trans. on Parallel and Distributed Systems,
Efficient Content Location Using Interest-based Locality in Peer-to-Peer Systems Presented by: Lin Wing Kai.

Efficient Content Location Using Interest-based Locality in Peer-to-Peer Systems Presented by: Lin Wing Kai.

Similar presentations

Ads by Google