Replication and Consistency Chapter 6. Data-Centric Consistency Models The general organization of a logical data store, physically distributed and replicated.

Slides:



Advertisements
Similar presentations
DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S
Advertisements

Synchronization and Deadlocks
Principles of Transaction Management. Outline Transaction concepts & protocols Performance impact of concurrency control Performance tuning.
Consistency and Replication Chapter Introduction: replication and scalability 6.2 Data-Centric Consistency Models 6.3 Client-Centric Consistency.
Consistency and Replication Chapter Topics Reasons for Replication Models of Consistency –Data-centric consistency models: strict, linearizable,
1 CS6320 – Performance L. Grewe 2 The Servlet Interface Java provides the interface Servlet Java provides the interface Servlet Specific Servlets implement.
Principles and Paradigms Consistency and Replication
Consistency and Replication
Synchronization Chapter clock synchronization * 5.2 logical clocks * 5.3 global state * 5.4 election algorithm * 5.5 mutual exclusion * 5.6 distributed.
Consistency and Replication Chapter 6. Object Replication (1) Organization of a distributed remote object shared by two different clients.
Consistency Models Based on Tanenbaum/van Steen’s “Distributed Systems”, Ch. 6, section 6.2.
Consistency and Replication Chapter 7. n Most of the lecture notes are based on slides by u Prof. Jalal Y. Kawash at Univ. of Calgary u Prof. Kenneth.
Consistency and Replication. Replication of data Why? To enhance reliability To improve performance in a large scale system Replicas must be consistent.
Computer Science Lecture 14, page 1 CS677: Distributed OS Consistency and Replication Today: –Introduction –Consistency models Data-centric consistency.
Distributed Systems1 Chapter 8: Replication and Consistency  Replication: A key to providing good performance, high availability and fault tolerance in.
Consistency And Replication
Consistency and Replication
Distributed Systems Spring 2009
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved DISTRIBUTED SYSTEMS.
Computer Science Lecture 14, page 1 CS677: Distributed OS Consistency and Replication Today: –Introduction –Consistency models Data-centric consistency.
Consistency and Replication. Why Replicate Data? Enhance reliability. Improve performance. But: if there are many replicas of the same thing, how do we.
Consistency and Replication Chapter 6. Reasons for Replication Data replication is a common technique in distributed systems. There are two reasons for.
Replication and Consistency CS-4513 D-term Replication and Consistency CS-4513 Distributed Computing Systems (Slides include materials from Operating.
Computer Science Lecture 14, page 1 CS677: Distributed OS Consistency and Replication Introduction Consistency models –Data-centric consistency models.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved Chapter 7 Consistency.
1 Consistency and Replication Reasons for Replication Reliability Performance –Scaling with respect to size of geographic area Negative Points Having multiple.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved DISTRIBUTED SYSTEMS.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved DISTRIBUTED SYSTEMS.
Memory Consistency Models Some material borrowed from Sarita Adve’s (UIUC) tutorial on memory consistency models.
Consistency & Replication Chapter No. 6
Contents Clock Synchronization Logical Clocks Global State
Consistency and Replication Chapter Concepts Reasons for Replication Reliability Earthquake, flood Misoperation Performance Place copies of data.
Consistency and Replication CSCI 4780/6780. Chapter Outline Why replication? –Relations to reliability and scalability How to maintain consistency of.
Consistency and Replication
C. Xu, Replication and Consistency Data and Object Replication Data Consistency Model Implementation Issues.
Consistency And Replication
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved Chapter 7 Consistency.
Consistency and Replication Chapter 7. n Most of the lecture notes are based on slides by Prof. Jalal Y. Kawash at Univ. of Calgary n Some notes are based.
Computer Architecture 2015 – Cache Coherency & Consistency 1 Computer Architecture Memory Coherency & Consistency By Yoav Etsion and Dan Tsafrir Presentation.
Consistency and Replication Chapter 6. Release Consistency (1) A valid event sequence for release consistency. Use acquire/release operations to denote.
Consistency and Replication. Replication of data Why? To enhance reliability To improve performance in a large scale system Replicas must be consistent.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved DISTRIBUTED SYSTEMS.
Consistency and Replication Distributed Software Systems.
Ch 10 Shared memory via message passing Problems –Explicit user action needed –Address spaces are distinct –Small Granularity of Transfer Distributed Shared.
第5讲 一致性与复制 §5.1 副本管理 Replica Management §5.2 一致性模型 Consistency Models
Synchronization Chapter 5.
Shared Memory Consistency Models. SMP systems support shared memory abstraction: all processors see the whole memory and can perform memory operations.
Consistency and Replication Chapter 6 Presenter: Yang Jie RTMM Lab Kyung Hee University.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved DISTRIBUTED SYSTEMS.
Distributed Systems Replication. Why Replication Replication is Maintenance of copies at multiple sites Enhancing Services by replicating data Performance.
DISTRIBUTED COMPUTING
Consistency and Replication Chapter 6. Topics Reasons for Replication Models of Consistency –Data-centric consistency models –Client-centric consistency.
Shyh-In Hwang in Yuan Ze University1 Chapter 6 Consistency and Replication.
CS3773 Software Engineering Lecture 06 UML State Machines.
Consistency and Replication. Outline Introduction (what’s it all about) Data-centric consistency Client-centric consistency Replica management Consistency.
Consistency and Replication Chapter 6 Presenter: Yang Jie RTMM Lab Kyung Hee University.
Synchronization Chapter 5. Clock Synchronization When each machine has its own clock, an event that occurred after another event may nevertheless be assigned.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved DISTRIBUTED SYSTEMS.
Consistency and Replication CSCI 6900/4900. FIFO Consistency Relaxes the constraints of the causal consistency “Writes done by a single process are seen.
OS2 –Sem 1, Rasool Jalili Consistency and Replication Chapter 6.
Consistency and Replication (1). Topics Why Replication? Consistency Models – How do we reason about the consistency of the “global state”? u Data-centric.
CS6320 – Performance L. Grewe.
Consistency and Replication
Consistency and Replication
Consistency Models.
Consistency and Replication
Ch 5 Replication and Consistency
DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S
Distributed Systems CS
Consistency and Replication
Presentation transcript:

Replication and Consistency Chapter 6

Data-Centric Consistency Models The general organization of a logical data store, physically distributed and replicated across multiple processes.

Strict Consistency Behavior of two processes, operating on the same data item. A strictly consistent store. A store that is not strictly consistent.

Linearizability and Sequential Consistency (1) a)A sequentially consistent data store. b)A data store that is not sequentially consistent.

Linearizability and Sequential Consistency (2) Three concurrently executing processes. Process P1Process P2Process P3 x = 1; print ( y, z); y = 1; print (x, z); z = 1; print (x, y);

Linearizability and Sequential Consistency (3) Four valid execution sequences for the processes of the previous slide. The vertical axis is time. x = 1; print ((y, z); y = 1; print (x, z); z = 1; print (x, y); Prints: Signature: (a) x = 1; y = 1; print (x,z); print(y, z); z = 1; print (x, y); Prints: Signature: (b) y = 1; z = 1; print (x, y); print (x, z); x = 1; print (y, z); Prints: Signature: (c) y = 1; x = 1; z = 1; print (x, z); print (y, z); print (x, y); Prints: Signature: (d)

Casual Consistency (1) Necessary condition: Writes that are potentially casually related must be seen by all processes in the same order. Concurrent writes may be seen in a different order on different machines.

Casual Consistency (2) This sequence is allowed with a casually-consistent store, but not with sequentially or strictly consistent store.

Casual Consistency (3) a)A violation of a casually-consistent store. b)A correct sequence of events in a casually-consistent store.

FIFO Consistency (1) Necessary Condition: Writes done by a single process are seen by all other processes in the order in which they were issued, but writes from different processes may be seen in a different order by different processes.

FIFO Consistency (2) A valid sequence of events of FIFO consistency

FIFO Consistency (3) Statement execution as seen by the three processes from the previous slide. The statements in bold are the ones that generate the output shown. x = 1; print (y, z); y = 1; print(x, z); z = 1; print (x, y); Prints: 00 (a) x = 1; y = 1; print(x, z); print ( y, z); z = 1; print (x, y); Prints: 10 (b) y = 1; print (x, z); z = 1; print (x, y); x = 1; print (y, z); Prints: 01 (c)

FIFO Consistency (4) Two concurrent processes. Process P1Process P2 x = 1; if (y == 0) kill (P2); y = 1; if (x == 0) kill (P1);

FIFO Consistency (4) Two concurrent processes. Process P1Process P2 x = 1; if (y == 0) kill (P2); y = 1; if (x == 0) kill (P1);

Weak Consistency (1) Properties: Accesses to synchronization variables associated with a data store are sequentially consistent No operation on a synchronization variable is allowed to be performed until all previous writes have been completed everywhere No read or write operation on data items are allowed to be performed until all previous operations to synchronization variables have been performed.

Weak Consistency (2) a)A valid sequence of events for weak consistency. b)An invalid sequence for weak consistency.

Release Consistency (1) A valid event sequence for release consistency.

Release Consistency (2) Rules: Before a read or write operation on shared data is performed, all previous acquires done by the process must have completed successfully. Before a release is allowed to be performed, all previous reads and writes by the process must have completed Accesses to synchronization variables are FIFO consistent (sequential consistency is not required).

Entry Consistency (1) Conditions: An acquire access of a synchronization variable is not allowed to perform with respect to a process until all updates to the guarded shared data have been performed with respect to that process. Before an exclusive mode access to a synchronization variable by a process is allowed to perform with respect to that process, no other process may hold the synchronization variable, not even in nonexclusive mode. After an exclusive mode access to a synchronization variable has been performed, any other process's next nonexclusive mode access to that synchronization variable may not be performed until it has performed with respect to that variable's owner.

Entry Consistency (2) A valid event sequence for entry consistency.

Summary of Consistency Models a)Consistency models not using synchronization operations. b)Models with synchronization operations. ConsistencyDescription StrictAbsolute time ordering of all shared accesses matters. Linearizability All processes must see all shared accesses in the same order. Accesses are furthermore ordered according to a (nonunique) global timestamp Sequential All processes see all shared accesses in the same order. Accesses are not ordered in time CausalAll processes see causally-related shared accesses in the same order. FIFO All processes see writes from each other in the order they were used. Writes from different processes may not always be seen in that order (a) ConsistencyDescription WeakShared data can be counted on to be consistent only after a synchronization is done ReleaseShared data are made consistent when a critical region is exited EntryShared data pertaining to a critical region are made consistent when a critical region is entered. (b)

References Lecture slides of Distributed Systems principles and paradigms by Andrew Tannenbaum and Van Steen, Prentice Hall India, 2002.