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CS 5204 Operating Systems Lecture 9

CS 5204 Operating Systems Lecture 9. Godmar Back. Announcements. Still working on project proposals Look for reply email from me with word “approved” in it Out of town Oct 2-6, Oct 16-18: No class on Oct 3 & 5 ; Presentations move back; I’ve updated reading list with new tentative dates

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CS 5204 Operating Systems Lecture 9

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  1. CS 5204Operating SystemsLecture 9 Godmar Back

  2. Announcements • Still working on project proposals • Look for reply email from me with word “approved” in it • Out of town Oct 2-6, Oct 16-18: • No class on Oct 3 & 5; • Presentations move back; I’ve updated reading list with new tentative dates • Midterm: Oct 17 CS 5204 Fall 2005

  3. Plan for Today • Techniques for scalability • Consistency models • Openness & Flexibility • Discussion on End-to-End argument CS 5204 Fall 2005

  4. Continuing on Scalability • Recall: main problem that limited scalability was centralization (in services, in data, in centralized algorithms) • Aside from using decentralized algorithms (where possible), what else can be done to increase scalability? CS 5204 Fall 2005

  5. Scaling Techniques • Hide communication latencies • Use asynchronous communication whenever possible Sender REQUEST REPLY Receiver synchronous vs. asynchronous CS 5204 Fall 2005

  6. Deferred synchronous RPC • Combines two asynchronous RPC. CALL, WAIT FOR ACCEPTANCE, CONTINUE Client REQUEST ACK RESULTS ACK Server ACCEPT CALL LOCAL PROCEDURE CS 5204 Fall 2005

  7. Scaling Techniques (cont’d) • Minimize communication • Through distribution • Through piggybacking • Through careful placement of computation • Examples of these? • Note shift in focus over time • as bandwidth becomes cheaper stronger focus on avoiding relative latency penalty CS 5204 Fall 2005

  8. Latency lagsBandwidth • Patterson [2004] • Answers: • Caching • Replication • Prediction CS 5204 Fall 2005

  9. Workload & Data Distribution • DNSZones com edu gov de stanford vt utah banner registrar cs eng cs www www fbox www flux gradlab CS 5204 Fall 2005

  10. Consistency Models • Scalability goal when using caching/replication: • minimize synchronization requirements • use relaxed consistency models when possible • Consistency Models • Strict consistency • Sequential consistency; linearizability • Causal consistency • FIFO consistency • Weak consistency • Refinements: Release consistency, Entry consistency CS 5204 Fall 2005

  11. Strict Consistency • Any read on a data item x returns the value most recently written to x. • Ideal model for programmers • Requires global clock (example: leases) CS 5204 Fall 2005

  12. Sequential Consistency • The result of the execution is the same as if reads and writes were executed in some sequential order; reads and writes of each process are executed in program order within that sequence CS 5204 Fall 2005

  13. Sequential Consistency (cont’d) • Note that sequential consistency requires • Maintaining constraints by program order • Data coherence within global sequence (“history”) • Updates must be synchronous • Write update vs. write invalidate • Performance: it has been shown that r+w > t where r: read time, w: write time, t: message time • Optimizing writes makes reads slower & vice versa CS 5204 Fall 2005

  14. Causal Consistency • Not all processes need to see all writes in the same order • Causal consistency – only if writes are causally related (as in happens before relship) This sequence is causally consistent, but not sequentially or strictly consistent CS 5204 Fall 2005

  15. Causal Consistency (II) • Example of a violation: W(x)a happens before W(x)b, so P3 and P4 must see results in same order CS 5204 Fall 2005

  16. Weaker Consistency Models • Idea: don’t propagate all updates, only propagate consistent state between updates to distributed synchronization variables • Provide sequential consistency, but only with respect to sync points Sync Sync Sync Sync CS 5204 Fall 2005

  17. Release Consistency • Propagate writes when releasing a distributed synchronization variable • Can be done eagerly or lazily • Also possible: entry consistency • Only update those that will be accessed after entry Acquire Release Acquire Release CS 5204 Fall 2005

  18. E2E (cont’d) • Note that endpoint != application • Endpoint can also be a layer • How to identify the endpoints? • Reasons for violating E2E: • Performance • Cost • Software engineering/Code Reuse (?) • E2E is only a guiding principle, a type of “Occam’s Razor” CS 5204 Fall 2005

  19. Summary • Transparency goal • Techniques for scalability • Consistency models • Fault tolerance approaches & results CS 5204 Fall 2005

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