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: Reading
Client: Writing

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. Eventually consistent
ACID vs. BASE Model
ACID
BASE
Atomic
Basically Available
Consistency
Eventually consistent
Isolation
Soft state
Durability

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 #
decree
Quorum & Voters A B Γ ∆ E 2 a No
Yes - 5 b 14 27 29
“Paxos Made Simple” Leslie Lamport 01 Nov. 2001
“The Part-Time Parliament” Leslie Lamport, ACM Transactions on Computer Systems 16,2(May 1998),

19. PAXOS
Phase 1.
A proposer selects a proposal number n and sends a prepare request with number n to a majority of acceptors.
If an acceptor receives a prepare request with number n greater than that of any prepare request to which it has already responded, then it responds to the request with a promise not to accept any more proposals numbered less than n and with the highest numbered proposal (if any) that it has accepted.

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-Carvalho Algorithm

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 Business Open Source
Amazon Simple Storage Service
MongoDB
Windows Azure Storage
MemcacheDB
Google MegaStore
ThruDB
Google BigTable
Hbase
Alibaba's Cloud Storage
Cassandra
IBM XIV
Scalaris
Chubby
ZooKeeper

33. Thank you!!!