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Data Placement in P2P Systems: Leveraging Databases for Improved Scalability and Performance

This paper explores the data placement problem in Peer-to-Peer (P2P) systems, highlighting the need for improved scalability and performance. It discusses the advantages of leveraging database technologies in P2P environments and the design choices involved in data placement. The complexity of the problem and the use of cooperative spheres in query optimization are also addressed.

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Data Placement in P2P Systems: Leveraging Databases for Improved Scalability and Performance

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  1. What Can Databases Do for Peer-to-Peer Steven Gribble, Alon Halevy,Zachary Ives, Maya Rodrig, Dan Suciu Presented by: Ryan Huebsch CS294-4 P2P Systems – 11/03/03

  2. Outline • Disclaimer: This is a position paper, not a technical/system paper (no graphs) • Author’s Mindset • Data Placement • Complexity • Piazza

  3. Why P2P? • Desirable properties of P2P system amplified with new peers • Robustness • Availability • Performance • Decentralization for trust reasons & administration • No proprietary interests • Trust is diffused over all participants

  4. What is the problem? • Gnutella failed to attract people because of • Weak application semantics (search for filename, what does the filename mean?) • Technical flaws limit scaling (short term problem?) • Ad-hoc membership • Difficult to predict resources and load • Thus, data placement is demand driven (for lack of better mechanism) • May cause fundamental limits on consistency and availability

  5. Why Databases? • The problem is placement and retrieval of data… that would be a data management (or DB) problem • P2P world is lacking • Semantics • Data transformation • Data relationships • All of which are core strengths of the DB community • P2P brings a new environment for DB query processing systems • increased scalability, reliability, and performance • This paper focuses on the data placement problem

  6. Data Placement Problem • Setup • Set of cooperating nodes (no adversaries) • Bottlenecks: network, CPU, or memory • Nodes serve four roles • Data Origin – producers • Storage Provider • Query Evaluator • Query Initiator – consumers • Cost of query = Origin or Storage  Evaluator + Evaluator  Initiator

  7. Design Choices • Score of decision making • Global (hard, optimal) or local (easy, short-sided) • Similar to multi-query optimization • Extent of knowledge sharing • Knowledge of materialized views on other nodes (a catalog) • Centralized or distributed? Hierarchical (like DNS)? • Heterogeneity of information sources • Few authoritative sources, lots of data producers • Heterogeneous data  different schemas

  8. Design Choices II • Dynamicity of participants • Node churn • Some nodes act like servers, some like workstations • Could place all data on servers  reduced flexibility and performance • Data granularity • Atomic granularity  indivisible objects (complete file) • Hierarchical granularity  groups (albums, directories) • Value based granularity  Objects composed of atomic value (tuples composed of values)

  9. Design Choices III • Degrees of replication • One copy all the way to fully replicated • More replicas make updates harder • Also makes retrieval harder (more choices) • Consistency is harder, typical solution is to have a master replica • Freshness and update consistency • Invalidation messages, pushed by server on update or pulled by client on request • Timeout based, lower overhead, looser guarantees about freshness and consistency

  10. Complexity of Problem • The papers goes to some trouble to formally define the problem • Defines a small sub-problem of data placement, • Static P2P network • Queries are zero-cost • Problem: Which nodes an item go on? • Problem is NP complete, proof comes from vertex-cover, not in this paper

  11. Piazza • Peers form small groups called spheres of cooperation. • May follow administrative boundaries • Spheres of cooperation are nested • Query Optimization problems: • Exploit commonalities between queries • Decide where to place data • What queries to materialize (store answers) • To make the problem tractable, optimization occurs within a sphere of cooperation.

  12. Piazza II

  13. Piazza III • Propagating Information • Node advertises its materialized views to its neighbors • Nodes consolidate info they receive and propagate • Type of gossiping protocol • Consolidating Queries • Some queries can not be evaluated if data is not locally available • Broadcast all un-evaluatable queries to local sphere of cooperation, and try to answer them collectively

  14. Where is Piazza now? • Focusing more on data semantics and information integration • Every nodes has its view of what the data schema is • Very Difficult problem that most people in the database community have ignored.

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