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Beyond Music Sharing: An Evaluation of Peer-to-Peer Data Dissemination Techniques in

Beyond Music Sharing: An Evaluation of Peer-to-Peer Data Dissemination Techniques in Large Scientific Collaborations. Thesis defense: Samer Al-Kiswany. /26. Samer Al-Kiswany. Introduction.

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Beyond Music Sharing: An Evaluation of Peer-to-Peer Data Dissemination Techniques in

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  1. Beyond Music Sharing: An Evaluation of Peer-to-Peer Data Dissemination Techniques in Large Scientific Collaborations Thesis defense: Samer Al-Kiswany

  2. /26 Samer Al-Kiswany Introduction • Data-intensive science: large-scale simulations and new scientific instruments generate huge volumes of data (PetaBytes). • User communities: large, geographically dispersed Requirement : Efficient data dissemination tools

  3. /26 Samer Al-Kiswany Introduction - Example

  4. /26 Samer Al-Kiswany Question ? Data dissemination solutions: IP-Multicast, Bullet, BitTorrent, SPIDER, OMNI, ALMI, Logistical-Multicast, Narada, Scribe, Grido, FastReplica… and many others. What data dissemination strategies perform best in today's Grids deployments?

  5. Workload characteristics Evaluation Recommendations Deployment platform characteristics Data dissemination proposed solutions /26 Samer Al-Kiswany Roadmap What data dissemination strategies perform best in today's Grids deployments?

  6. /26 Samer Al-Kiswany Workload and Deployment Platform Data-intensive scientific collaboration characteristics: • Scale of data: massive data collections (TeraBytes) • Data usage: Uniform popularity distributions, and co‑usage • Near real time processing. Deployment platform characteristics: • Resource availability: low churn rate, high node availability, well-provisioned networks. • Collaborative environments: no freeriding, • thus less effort is needed to control fair resource sharing.

  7. /26 Samer Al-Kiswany Roadmap What data dissemination strategies perform best in today's Grids deployments? Workload characteristics Evaluation Recommendations Deployment platform characteristics Data dissemination proposed solutions

  8. /26 Samer Al-Kiswany Classification of Approaches • Base Cases: • IP-Multicast. • Parallel transfers: separate data channels from the source to each destination.

  9. Drawbacks: • Overwhelms the source – does not scale • Generates high duplicate traffic at the links around the source • Does not exploit all available transport capacity. Separate Transfer from the Source to every Destination /26

  10. 10 10 10 10 5 5 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 IP Multicasting /26

  11. IP Multicast Drawbacks: • Limited deployment • Vulnerability to nodes failures • Does not exploit all available transport capacity. • Throughput limited by bottleneck link 10 10 5 10 10 10 10 10 10 10 10 10 /26

  12. Source 1 5 6 4 3 2 ALM Tree Tree Based Techniques: Application Level Multicast (ALM) Source 6 1 5 2 4 3 /26

  13. Source Source Drawbacks: 1 5 • Vulnerability to nodes failures • Does not exploit all possible routes in the network. 6 4 3 2 6 ALM Tree 1 5 2 4 3 Tree Based Techniques: Application Level Multicast (ALM) /26

  14. Swarming Techniques: BitTorrent and Bullet 4 1 2 3 Complete file 1 2 3 4 /26

  15. Swarming Techniques: BitTorrent and Bullet 1 Complete file 1 2 3 4 4 4 1 2 1 3 2 3 /26

  16. Complete file 1 2 3 4 Drawbacks: • Generates high duplicate traffic. 3 4 1 2 1 2 1 3 4 Swarming Techniques: BitTorrent and Bullet /26

  17. Logistical Multicasting /26

  18. Workload characteristics Recommendations Deployment platform characteristics Data dissemination proposed solutions /26 Samer Al-Kiswany Roadmap Question: What data dissemination strategies perform best in today's Grids deployments? Evaluation Approaches: Evaluation • Analytical Modeling • Deployment based • Simulation

  19. Inputs: • Real topologies of three deployed Grid testbeds: LCG, GridPP, EGEE. • Generated topologies: 100 (using BRITE) Methodology • Simulator Design: • Block-level simulation. • Simulates physical layer link-contention /26 Samer Al-Kiswany

  20. /26 Samer Al-Kiswany Methodology

  21. /26 Samer Al-Kiswany TransferTime Number of destinations that have completed the file transfer for the original EGEE topology.

  22. /26 Samer Al-Kiswany Transfer Time – With reduced core-link bandwidth • Conclusions: • On well-provisioned topologies even naïve algorithms perform well. • On constrained topologies application‑level techniques perform uniformly well: are among the first to finish the transfer with good intermediate progress. Number of destinations that have completed the file transfer – EGEE topology with core bandwidth reduced to 1/8 of the original one.

  23. /26 Samer Al-Kiswany Summary Motivating question: What data dissemination strategies perform best in today's Grids deployments? In this project, we: • Simulated representative solutions. • Considering the characteristics of the workload and deployed platforms • Our results provide guidelines for selecting the data dissemination technique, depending on the: • Target environment. • Overall system workload characteristics. • Success Criteria.

  24. /26 Samer Al-Kiswany Research Publications This work resulted in two refereed publications, and one journal submission: • Beyond Music Sharing: An Evaluation of Peer-to-Peer Data Dissemination Techniques in Large Scientific Collaborations, S. Al-Kiswany, M. Ripeanu, A. Iamnitchi, and S. Vazhkudai, Submitted to the Journal of Grid Computing. • Are P2P Data-Dissemination Techniques Viable in Today's Data-Intensive Scientific Collaborations?, S. Al-Kiswany, M. Ripeanu, A. Iamnitchi, and S. Vazhkudai, EuroPar, 2007, France.( acceptance rate = 26%) • A Simulation Study of Data Distribution Strategies for Large-scale Scientific Data Collaborations, S. Al-Kiswany and M. Ripeanu, IEEE CCECE 2007.

  25. /26 Samer Al-Kiswany Other Research Work I am involved in another two research projects: Scavenged Storage System • stdchk: A Checkpoint Storage System for Desktop Grid Computing • A High-Performance GridFTP Server at Desktop Cost StoreGPU Exploiting the GPU for computationally intensive storage system operations.

  26. Thank you www.ece.ubc.ca/~samera

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