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Distributed Storage System Survey

Distributed Storage System Survey

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Distributed Storage System Survey

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  1. Distributed Storage System Survey Yang Kun

  2. Agenda • 1. History of DSS • 2. Definition & Terminology • 3. Basic Factors • 4. DSS Common Design • 5. Basic Theories • 6. Popular Algorithms • 7. Replication Strategies • 8. Implementations • 9. Open Source & Business

  3. History of DSS • Network File System (1980s)

  4. History of DSS • Storage Area Network(SAN)File System(1990s)

  5. History of DSS • Object oriented parallel file system (2000s)

  6. History of DSS • Cloud Storage

  7. Definition & Terminology • Transparency • network-transparency, user-mobility • Performance Measurement • The amount of time needed to satisfy service requests. • The performance should be comparable to that of a conventional file system.

  8. Definition & Terminology • Fault Tolerance: 1. Communication faults, machine failures ( of type fail stop), storage device crashes, decays of storage media. • Scalability: A scalable system should react more gracefully to increased load • The performance should degrade more moderately than that of a non-scalable system. • The resources should reach a saturated state later compared with a non-scalable system.

  9. Definition & Terminology Consistency: Consistency requires that there must exist a total order on all operations such that each operation looks as if it were completed at a single instant. Availability: Every request received by a non-failing node in the system must result in a response. Reliability

  10. Basic Factors • Location Transparency • User mobility • Security • Performance • Scalability • Availability • Failure Tolerance

  11. DSS Common Design Client: Writing Client: Reading

  12. Basic Theories • CAP Theory • ACID vs. BASE Model • Quorum NRW

  13. CAP Theory

  14. CAP Theory • In a partition network(both in synchronous and partially synchronous), it is impossible for a web service to provide consistency, availability and partition-tolerance at the same time. • Consistency • Availability • Partition-tolerance

  15. CAP Theory • CP: All data in only one node, and other node read/write from this node. • CA: Database System • AP: Make sure that returns the value every time. • Cassandra = A + P + Eventually Consistency

  16. ACID vs. BASE Model

  17. Quorum NRW • N: Replica's mount, that is how many backup for each data object. • R: The minimum mount of successful reading, that is the minimum mount for identifying a reading operation is successful. • W: The minimum mount of successful writing, that is the minimum mount for identifying a writing operation is successful. • The three factors decide the availability, consistency and fault-tolerance. And Strong consistency can be guaranteed only if W + R > N.

  18. Popular Algorithms • PAXOS Algorithms • Roles: Proposer, Acceptor, Learner • Phases: Accept, Learn

  19. PAXOS

  20. PAXOS

  21. Popular Algorithms • Consistent Hashing

  22. Popular Algorithms • Mutual Algorithms • Lamport Algorithm (3*(n - 1)) • Improved Lamport Algorithm (3*(n - 1)) • Ricart–Agrawala algorithm (2*(n - 1)) • Maekawa Algorithm • Roucairol-CarvalhoAlgorithm

  23. Popular Algorithms • Election Algorithms • Chang-Roberts Algorithm ( n log n) • Garcia-Molina's bully Algorithm • Non-based on Comparison Algorithms

  24. Popular Algorithms • Bidding Algorithms • Self Stabilization Algorithms

  25. Replication Strategies • Asynchronous Master/Slave Replication Log appends are acknowledged at the master in parallel with transmission to slaves. (Not support ACID) • Synchronous Master/Slave Replication A master waits for changes to be mirrored to slaves before acknowledging them. (Need timely detection) • Optimistic Replication Any member of a homogeneous replica group can accept mutations (Order is not known, transaction is impossible)

  26. Chain Replication

  27. CRAQ • Chain Replication with Apportioned Queries

  28. Funnel Replication • Topology • Vector Clock • Total Order • Write Request (key, value, vector clock, originating head replica)

  29. Atomic Commit Protocol • Two-PC 1. Voting phase The coordinator requests all participating sites to prepare to commit. 2. Decision phase The coordinator either commits the transaction if all participants are prepared-to-commit (voted “yes”), or aborts the transaction if any participant has decided to abort (voted “no”).

  30. Atomic Commit Protocol • Presumed Abort Protocol It is designed to reduce the cost associated with aborting transactions. • Presumed Commit Protocol It is designed to reduce the cost associated with committing transactions through interpret missing information about transactions as commit decisions. One-PC One-Phase Commit protocol consists of only a single phase which is the decision phase of 2PC. One-Two-PC

  31. Implementations • BigTable • Windows Azure Storage • Google MegaStore • Chubby

  32. Open Source & Business

  33. Thank you!!!