1. Francisco Matias Cuenca-Acuna Christopher Peery Thu D. Nguyen http://www.panic-lab.rutgers.edu/ Usando algoritmos probabilísticos para construir sistemas distribuidos federados

2. Federated Computing Rising Internet connectivity is driving a new model of federated computing −Computing systems that span multiple organizations −Sharing of resources, including data and services Federated computing appearing at every level −Social group-based sharing −P2P: Gnutella, KaZaA −Web-based: Ebay, Google groups, Yahoo groups, DMOZ −Scientific computing −Research grids: The European Data Grid −Seti@home −E-commerce −Federated web ecommerce (Amazon WS) −Distributed supply chains (Travelocity, Sabre)

3. The Challenge Federated computing is a natural model for harnessing inherently distributed resources −Consider data generation and storage −Users produce 740TB of information per year 1 −The European Data Grid has 100’s of nodes hosting PB’s of data Challenge: how do we build systems that are −Inherently decentralized −Widely distributed −Widely heterogeneous −Resilient to uncontrolled node behavior 1 Source http://www.sims.berkeley.edu/research/projects/how-much-info/

4. The PlanetP Project Infrastructure support for federated systems −Communication and distributed state maintenance −Loosely replicated state −Global index over all data stores −Global membership −One to many data propagation −Information sharing −Content addressing & ranking of results −Provide predictable data availability −Deployment, monitoring, and management of federated services −Provide a common runtime environment −Self-managing and self-configuring given quality of service goals

5. Approach & Status PlanetP Principles −Autonomous actions −Loosely synchronized global information −Randomized algorithms −resources vs. predictability PlanetP today … −Multidimensional indexed data store −Accommodates communities of 1000’s nodes −Content ranking comparable to centralized text-based solution −~4% loss of recall and precision when compared to centralized TFxIDF implementation −Practical data availability −Environment modeled after Gnutella, avg availability 24%, can achieve 99.9% data availability with 6x excess storage −Successfully help a replicated service adapt to a volatile environment −Maintains a UDDI service running on Planetlab across 100 nodes

6. The PlanetP Architecture Node X Hoarded Set F1F1 F2F2 FiFi Excess Storage FjFj FkFk Global Data Index MembershipInfo. Gossiping Information Search & Ranking

7. Communication and State Maintenance

8. Nodes push and pull randomly from each others −Unstructured communication  resilient to failures −Predictable convergence time Novel combination of previously known techniques −Rumoring, anti-entropy, and partial anti-entropy −Introduce partial anti-entropy to reduce variance in propagation time for dynamic communities −Batch updates into communication rounds for efficiency −Dynamic slow-down in absence of updates to save bandwidth Epidemic Communication   ___

9. [K 1,..,K n ] Local Objects Bloom filter Local Index Global Directory Gossiping [K 1,..,K n ] Local Objects Bloom filter Local Index Global Directory NicknameStatusIPKeys AliceOnline…[K 1,..,K n ] BobOffline…[K 1,..,K n ] CharlesOnline…[K 1,..,K n ] Globally Indexed Data Store Each node maintains a local index of its shared objects −Objects can be accessed through handles or keys −Summarize the set of keys in its index using a Bloom filter The global index is the set of all summaries −Key-to-peer mappings (but don’t know the exact set of keys) −List of online peers NicknameStatusIPKeys AliceOnline…[K 1,..,K n ] BobOffline…[K 1,..,K n ] CharlesOnline…[K 1,..,K n ]

10. Data Propagation Performance Arrival and departure experiment (LAN)Propagation speed experiment (DSL)

11. Automatic replication for availability

12. Increasing Data Availability GOAL: provide predictable data availability in P2P systems −E.g., for file systems, we want to reason about minimum file availability Wide range of node availability −Node MTTF no longer determined by hardware reliability but by users’ on-line behavior −Fixed number of replicas too wasteful −E.g., small number of replicas on highly available nodes equivalent to many replicas on low available nodes −Gnutella span from 0.1% to 100%, with an average of 24% −Also, we need to recreate replicas as nodes join and leave Long term dynamic membership −In fact, a fixed number doesn’t work at all because availability profile will likely change over time

13. Our Approach  Use replication but −Vary number of replicas based on estimated file availability −Take advantage of nodes going offline as opposed to failing −Loosely monitor availability −Use erasure codes to minimize space requirements and spread file to more nodes

14. Internet The Strategy Advertise: - Availability 20% - Files F1, F2 - Fragments Fi, Fj, Fk Node A Global Data Index Hoarded Set F1F1 F2F2 FiFi Excess Storage FjFj FkFk MembershipInfo. Gossiping Node B Global Data Index Hoarded Set F3F3 F4F4 FxFx Excess Storage FyFy FzFz MembershipInfo. Gossiping

15. Internet Node A Global Data Index Hoarded Set F1F1 F2F2 FiFi Excess Storage FjFj FkFk MembershipInfo. Gossiping The Strategy PlanetP Hoarded Set F3F3 F4F4 FxFx Excess Storage FyFy FzFz MembershipInfo. Gossiping Based on Node’s B view of F3: - Pick a random node - Create a new fragment for F3 - Push it  F3F3 Node B Global Data Index Hoarded Set F3F3 F4F4 FxFx Excess Storage FyFy FzFz MembershipInfo. Gossiping

16. Dealing with Decentralization Nodes replicate and evict autonomously All decisions are probabilistic −Weighted by availability estimates Target nodes control their own storage space −Protects system against greedy and faulty nodes Erasure codes plus −Use a modified version of Reed Solomon −Provide a large fragment space −Don’t re-create lost fragments −Prevents duplicates due to autonomous and misinformed decisions

17. Availability-based Replacement Estimating file availability −Probability of finding an online copy or being able to reconstruct the file from the erasure coded fragments Evict fragments of files with “too much” availability −Note that “too much” is in comparison only to files in local excess storage (don’t have to know about all files in system) Why does it work? −Randomized placement decisions  local sample of file availabilities reflect global distribution −This approximation drives space allocation and allows files with insufficient availability to gain fragments

18. Evaluation Evaluate three significantly different environments The file sharing environment −1000 nodes hosting a total of 25000 files −Node availability avg:24%, min:0.1%, 90th perc:75%, max:100% −Target 99.9% availability −10 minute refresh rate Sources −Saroiu et al. (Gnutella, Napster), DirectConnect at Rutgers OMNI −Centralized knowledge with no limitation on replica placement Base −What happens if you do not have availability estimates?

19. Availability Comparison

22. Effect of Av. Based Replacement

23. Conclusions Explored infrastructural support for applications running on federated systems −Membership, content addressing & ranking, service management −Scale well to thousands of peers −Extremely tolerant to unpredictable dynamic peer behaviors Gossiping with partial anti-entropy is reliable −Information always propagate everywhere −Propagation time has small variance Practical data availability −We can achieve 3 in spite of low node availability and decentralized environment −CO: 80% avg. av.  1X −FS: 24% avg. av.  6X −WG: 33% avg. av.  9X −Having some global information is critical −But can do quite well with loosely synchronized data

24. The PlanetP Project http://www.panic-lab.rutgers.edu/ Thank you Questions?