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1 AReNA: Adaptive Distributed Catalog Infrastructure Based On Relevance Networks Vladimir Zadorozhny, University of Pittsburgh, Pittsburgh, PA Avigdor.

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Presentation on theme: "1 AReNA: Adaptive Distributed Catalog Infrastructure Based On Relevance Networks Vladimir Zadorozhny, University of Pittsburgh, Pittsburgh, PA Avigdor."— Presentation transcript:

1 1 AReNA: Adaptive Distributed Catalog Infrastructure Based On Relevance Networks Vladimir Zadorozhny, University of Pittsburgh, Pittsburgh, PA Avigdor Gal, Technion, Haifa Louiqa Raschid, University of Maryland, College Park, MD Quiang Ye, University of Pittsburgh, Pittsburgh, PA Nebula Project: http://db.sis.pitt.edu/projects/Nebula

2 query optimization evaluation output Statistics about data data Relevant statistics: response time, network delay, data transfer rate, etc. Data sources are remote, distributed, heterogeneous Network is not (well) predictable Statistics is not reliable Networked Query Processing

3 data Statistics about data query optimization evaluation output Networked Queries with Distributed Catalog Scalability ?

4 4 Performance Monitoring for Server Selection performance monitor client LEGEND: Handle system Object handle: 101.1 content server Object 101.1 Objective: maintaining comprehensive performance repository for WANs (e.g., access latencies). Motivated, in part, by the evolution of information-centric name resolution services, e.g., CNRI Handle system.Handle system Challenge: scaling to the presence of hundreds of servers and thousands of clients, managing millions of constantly changing Performance Profiles.

5 performance monitor performance profile-based cluster content server client LEGEND: Profile-Based Performance Monitoring PM Aggregation ?

6 6 Aggregated Latency Profiles A client/server pair is characterized by Individual Latency Profiles (iLP). iLPs capture latency distributions experienced by clients when connecting to a server. iLP1 = iLP2 = iLP3 = Similar non-randomly associated iLPs are aggregated in Relevance Networks iLP similarity measures: Correlation and Mutual Information iLP1 iLP3 iLP2 0.8 0.2

7 7 Discovering Non-random Associations with Relevance Networks (RNs) LP1 LP4LP2 LP3 Threshold=0.4 0.75 0.9 0.8 0.5 0.45 LP1 LP4LP2 LP3 Threshold=0.7 0.75 0.9 0.8 We adopt RNs as a management tool, to manage large numbers of iLPs.

8 8 Relevance Networks

9 9 AReNA: Architecture Data Collection Data Preparation RN Generation and Analysis Performance Prediction VIZUALIZERVIZUALIZER AReNA dynamically analyzes and visualizes meaningful relationships among client/ server pairs using Relevance Networks (RNs). Relationships are evaluated using passive measurements made by client applications and gathered on a continuous basis. RNs allow AReNA managing thousands of constantly changing iLPs Large-Scale Experimental Testbeds CNRI Handle System PlanetLab Overlay Around 50 000 Latency Profiles

10 10 AReNA: Screenshot

11 11 Demo Tuesday: 16:00-17:30 Friday: 09:00-10:30


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