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1
Principles of Reliable Distributed Systems
Lecture 10: Atomic Shared
Memory Objects and Shared Memory Emulations
Spring 2007
Prof. Idit Keidar
2
Material
• Attiya and Welch, Distributed Computing– Ch. 9 & 10
• Nancy Lynch, Distributed Algorithms– Ch. 13 & 17
• Linearizability slides adapted from Maurice Herlihy
3
Shared Memory Model
• All communication through shared memory!– No message-passing.
• Shared memory registers/objects.
• Accessed by processes with ids 1,2,…
• Note: we have two types of entities: objects and processes.
4
Motivation
• Multiprocessor architectures with shared memory• Multi-threaded programs• Distributed shared memory (DSM)• Abstraction for message passing systems
– We will see how to emulate shared memory in message passing systems.
– We will see how to use shared memory for consensus and state machine replication.
5
LinearizabilitySemantics for Concurrent
Objects
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FIFO Queue: Enqueue Method
q.enq( )
Process
7
FIFO Queue: Dequeue Method
q.deq()/
Process
8
Sequential Objects
• Each object has a state– Usually given by a set of fields– Queue example: sequence of items
• Each object has a set of methods– Only way to manipulate state– Queue example: enq and deq methods
9
Methods Take Time
time
Method call
invocation 12:00
q.enq(...
)
response 12:01
void
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Split Method Calls into Two Events
• Invocation– method name & args– q.enq(x)
• Response– result or exception– q.enq(x) returns void– q.deq() returns x– q.deq() throws empty
11
A Single Process (Thread)
• Sequence of events
• First event is an invocation
• Alternates matching invocations and responses
• This is called a well-formed interaction
12
Concurrent Methods Take Overlapping Time
time
Method call Method call
Method call
13
Concurrent Objects
• What does it mean for a concurrent object to be correct?
• What is a concurrent FIFO queue?– FIFO means strict temporal order– Concurrent means ambiguous temporal order
• Help!
14
Sequential Specifications
• Precondition, say for q.deq(…)– Queue is non-empty
• Postcondition:– Returns & removes first item in queue
• You got a problem with that?
15
Concurrent Specifications
• Naïve approach– Object has n methods– Must specify O(n2) possible interactions– Maybe more
If the quque is empty and then enq begins and deq begins after enq(x) begins but before enq(x) ends then …
• Linearizability: same as it ever was
16
Linearizability
• Each method should –– “Take effect”
• Effect defined by the sequential specification
– Instantaneously• Take 0 time
– Between its invocation and response events.
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Linearization
• A linearization of a concurrent execution is– A sequential execution
• Each invocation is immediately followed by its response
• Satisfies the object’s sequential specification
– Looks like • Responses to all invocations are the same as in
– Preserves real-time order• Each invocation-response pair occurs between the
corresponding invocation and response in
18
Linearizability and Atomicity
• A concurrent execution that has a linearization is linearizable.
• An object that has only linearizable executions is atomic.
19
Why Linearizability?
• “Religion”, not science
• Scientific justification:– Facilitates reasoning– Nice mathematic properties
• Common-sense justification– Preserves real-time order– Matches my intuition (sorry about yours)
20
Example
time
q.enq(x)
q.enq(y) q.deq(x)
q.deq(y)
time
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Example
time
q.enq(x)
q.enq(y)
q.deq(y)
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Example
time
q.enq(x)
q.deq(x)
time
23
Example
time
q.enq(x)
q.enq(y)
q.deq(y)
q.deq(x)
time
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Read/Write Variable Example
time
read(1)write(0)
write(1)
time
read(0)
25
Read/Write Variable Example
time
read(1)write(0)
write(1)
write(2)
time
read(1)
26
Read/Write Variable Example
time
read(1)write(0)
write(1)
write(2)
time
read(2)
27
Concurrency
• How much concurrency does linearizability allow?
• When must a method invocation block?
• Focus on total methods– defined in every state– why?
28
Concurrency
• Question: when does linearizability require a method invocation to block?
• Answer: never
• Linearizability is non-blocking
29
Non-Blocking Theorem
If method invocationA q.invoc()
is pending in linearizable history H, then there exists a responseA q:resp()
such thatH + A q:resp()
is legal
30
Note on Non-Blocking
• A given implementation of linearizability may be blocking
• The property itself does not mandate it– for every pending invocation, there is always a
possible return value that does not violate linearizability
– the implementation may not always know it…
31
Atomic Objects
• An object is atomic if all of its concurrent executions are linearizable
• What if we want an atomic operation on multiple objects?
32
Serializability
• A transaction is a finite sequence of method calls
• A history is serializable if – transactions appear to execute serially
• Strictly serializable if– order is compatible with real-time
• Used in databases
33
Serializability is Blocking
x.read(0)
y.read(0) x.write(1)
y.write(1)
34
Comparison
• Serializability appropriate for– fault-tolerance– multi-step transactions
• Linearizability appropriate for– single objects– multiprocessor synchronization
35
Critical Sections
• Easy way to implement linearizability– take sequential object– make each method a critical section
• Like synchronized methods in Java™
• Problems?– Blocking– No concurrency
36
Linearizability Summary
• Linearizability– Operation takes effect instantaneously between
invocation and response
• Uses sequential specification– No O(n2) interactions
• Non-Blocking– Never required to pause method call
• Granularity matters
37
Atomic Register Emulation in a Message-Passing System
[Attiya, Bar-Noy, Dolev]
38
Distributed Shared Memory (DSM)
• Can we provide the illusion of atomic shared-memory registers in a message-passing system?
• In an asynchronous system?
• Where processes can fail?
39
Liveness Requirement
• Wait-freedom (wait-free termination): every operation by a correct process p completes in a finite number of p’s steps
• Regardless of steps taken by other processes– In particular, the other processes may fail
or take any number of steps between p’s steps
– But p must be given a chance to take as many steps as it needs. (Fairness).
40
Register
• Holds a value
• Can be read
• Can be written
• Interface: – int read(); /* returns a value */
– void write(int v); /* returns ack */
41
Take I: Failure-Free Case
• Each process keeps a local copy of the register
• Let’s try state machine replication– Step1: Implement atomic broadcast (how?)
• Recall: atomic broadcast service interface:– broadcast(m)– deliver(m)
42
Emulation with Atomic Broadcast (Failure-Free)
• Upon client request (read/write),– Broadcast the request
• Upon deliver write request – Write to local copy of register– If from local client, return ack to client
• Upon deliver read request– If from local client, return local register value to client
• Homework questions: – Show that the emulated register is atomic– Is broadcasting reads required for atomicity?
43
What If Processes Can Crash?
• Does the same solution work?
44
ABD: Fault-Tolerant Emulation[Attiya, Bar-Noy, Dolev]
• Assumes up to f<n/2 processes can fail
• Main ideas: – Store value at majority of processes before
write completes
– read from majority
– read intersects write, hence sees latest value
45
Take II: 1-Reader 1-Writer (SRSW)
• Single-reader – there is only one process that can read from the register
• Single-writer – there is only one process that can write to the register
• The reader and writer are just 2 processes;– The other n-2 processes are there to help
46
Trivial Solution?
• Writer simply sends message to reader – When does it return ack?– What about failures?
• We want a wait-free solution: – if the reader (writer) fails, the writer (reader)
should be able to continue writing (reading)
47
SRSW Algorithm: Variables
• At each process:– x, a copy of the register– t, initially 0, unique tag associated with latest
write
48
SRSW AlgorithmEmulating Write
• To perform write(x,v)– choose tag > t– set x ← v; t ← tag– send (“write”, v, t) to all
• Upon receive (“write”, v, tag) – if (tag > t) then set x ← v; t ← tag fi– send (“ack”, v, tag) to writer
• When writer receives (“ack”, v, t) from majority (counting an ack from itslef too)– return ack to client
49
SRSW AlgorithmEmulating Read
• To perform read(x,v)– send (“read”) to all
• Upon receive (“read”) – send (“read-ack”, x, t) to reader
• When reader receives (“read-ack”, v, tag) from majority (including local values of x and t)– choose value v associated with largest tag– store these values in x,t– return x
50
Does This Work?
• Only possible overlap is between read and write– why?
• When a read does not overlap any write –– it reads at least one copy that was written by the latest
write (why?)– this copy has the highest tag (why?)
• What is the linearization order when there is overlap?
• What if 2 reads overlap the same write?
51
Example
time
read(1) read(?)
write(1)
time
52
Wait-Freedom
• Only waiting is for majority of responses
• There is a correct majority
• All correct processes respond to all requests– Respond even if the tag is smaller
53
Take III: n-Reader 1-Writer (MRSW)
• n-reader – all the processes can read
• Does the previous solution work?
• What if 2 reads by different processes overlap the same write?
54
Example
time
read(1)
read(?)
write(1)
time
55
MRSW Algorithm Extending the Read
• When reader receives (“read-ack”, v, tag) from majority – choose value v associated with largest tag– store these values in x,t– send (“propagate”, x, t) to all (except writer)
• Upon receive (“propagate”, v, tag) from process i– if (tag > t) then set x ← v; t ← tag fi– send (“prop-ack”, x, t) to process i
• When reader receives (“prop-ack”, v, tag) from majority (including itself)– return x
56
The Complete Read
S1S1 S1
S2
Sn
.
.
.
S1
S2
Sn
.
.
.
S1
(“read”) (“read-ack”,v, t)
Phase 1 Phase 2 : Multi-reader only
read() return
(“propagate”, v, t)(“prop-ack”)
57
Take IV: n-Reader n-Writer (MRMW)
• n-writer – all the processes can write to the register
• Does the previous solution work?
58
Playing Tag
• What if two writers use the same tag for writing different values?
• Need to ensure unique tags– That’s easy: break ties, e.g., by process id
• What if a later write uses a smaller tag than an earlier one?– Must be prevented (why?)
59
MRMW Algorithm Extending the Write
• To perform write(x,v)– send (“query”) to all
• Upon receive (“query”) from i– send (“query-ack”, t) to i
• When writer receives (“query-ack”, tag) from majority (counting its own tag)– choose unique tag > all received tags– continue as in 1-writer algorithm
• What if another writer chooses a higher tag before write completes?
60
The Complete Write
S1S1 S1
S2
Sn
.
.
.
S1
S2
Sn
.
.
.
S1
(“query”) (“query-ack”, t)
Phase 1: Multi-writer only Phase 2
write(v) ack
(“write”, v, t) (“ack”)
61
How Long Does it Take?
• The write emulation– Single-writer: 2 rounds (steps)– Multi-writer: 4 rounds (steps)
• The read emulation– Single-reader: 2 rounds (steps)– Multi-reader: 4 rounds (steps)
62
What if A Majority Can Fail?
• You guessed it!
• Homework question
63
Can We Emulate Every Atomic Object the Same Way?
64
Difference from Consensus
• Works even if the system is completely asynchronous
• In Paxos, there is no progress when there are multiple leaders
• Here, there is always progress – multiple writers can write concurrently– One will prevail (Which?)