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Applied Architectures

Applied Architectures. Eunyoung Hwang. Objectives. How principles have been used to solve challenging problems How architecture can be used to explain and analyze common commercial systems. Outline. Distributed Network-based Applications Limitations REST

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Applied Architectures

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  1. Applied Architectures Eunyoung Hwang

  2. Objectives • How principles have been used to solve challenging problems • How architecture can be used to explain and analyze common commercial systems

  3. Outline • Distributed Network-based Applications • Limitations • REST • Commercial Internet Scale Applications: Google • Decentralized Architecture • Grid Computing • Cloud Computing • Peer-2-Peer: Napster, Grutella, Skype

  4. Fallacies of Distributed Systems Viewpoint • The network is reliable • Latency is zero • Bandwidth is infinite • The network is secure • Topology does not change • There is one administrator • Transport cost is zero • The network is homogeneous

  5. WWW Architecture • World Wide Web is distributed, decentralized, hypermedia application.

  6. Representational State Transfer (REST) Style • A set of constraints based on WWW architectural style User agent Origin Server Request Proxies Gateways Response C C C C Intermediaries

  7. Process View of a REST-based Architecture

  8. Derivation of REST

  9. REST (cont.) • Constraints • Client-server • Context-free (stateless) • Cache • Code on demand • Layered • Uniform Interface • Benefits • Efficiency • Scalability • User perceived performance

  10. Commercial Internet-Scale Applications • Google strategy • Simpler storage system offering fewer features • Data Storage and manipulation • A high fault-tolerant platform • Cost effective manner • Google design • Google distributed file system (GFS) • MapReduce

  11. Google File System Architecture

  12. MapReduce • Large-scale data processing • Map • Take input key/value pair, generate a set of intermediate pair • Reduce • Merge all intermediate values associated with the same intermediate key Map (k1, v1) -> list(k2, v2) Reduce (k2, list(v2)) -> list(v2) • E.g., word frequency • map (URL, contents) -> set of (word, 1) • Reduce (word, 1) -> set of (word, sum)

  13. MapReduce Execution Flow

  14. MapReduce (cont.) • Architecture Provides • Automatic parallelization & distribution • Fault tolerance • Walker failure • Master failure

  15. Decentralized Architecture • Networked applications where there are multiple authorities • Not a new idea • E.g., web sites, international postal mail • Designing challenges

  16. Grid Computing • Coordinated resource sharing and computation in a decentralized environment • Technologies that allow consumers to obtain computing power on demand • Starts with large-scale federated resources • Issues • Interoperability • Security

  17. Grid Architecture

  18. Globus Grid Architecture (recovered)

  19. Cloud Computing • A key computing platform for sharing resources • A specialized distributed computing paradigm • Massively scalable • Different level of service • Driven by economies of scale • Dynamically configured and delivered on demand • What makes Cloud Computing interesting now? • Not a new concept!

  20. Cloud Computing

  21. Three level Services by Cloud Computing • Infrastructure as a Service(IaaS) • Amazon EC2 • Platform as a Service (PaaS) • Google App Engine • Software as a Service (Saas) • Salesforce

  22. Grid vsCloud Computing

  23. Peer-to-Peer: LL

  24. Napster • Hybrid client-server/P2P

  25. Gnutella • Pure decentralized P2P

  26. Skype • Overlayed P2P

  27. Review • Distributed Network-based Applications • REST • - Google: GFS, MapReduce • Decentralized Architecture • - Grid Computing • - Cloud Computing • - Peer-2-Peer: Napster, Grutella, Skype

  28. References • Principled Design of The Modern Web Architecture. • Fielding, Roy T., and Richard N. Taylor. • Cloud Computing and Grid Computing 360-Degree Compared. • Foster, Ian, and Zhao Yong. • MapReduce: Simplied Data Processing on Large Clusters • Dean, Jeffrey, and Sanjay Ghemawat. • The Google File System. • Ghemawat, Sanjay, Howard Gobioff, and Shun-Tak Leung

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