distributed systems cs 15-440 fault tolerance- part iii lecture 19, nov 25, 2013 mohammad hammoud 1

Distributed SystemsCS 15-440

Fault Tolerance- Part III

Lecture 19, Nov 25, 2013

Mohammad Hammoud

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Today… Last Two Sessions:

Fault Tolerance – Part II Reliable request-reply communication

Quiz 2

Today’s Session: Fault Tolerance – Part III

Reliable group communication Atomicity Recovery

Announcements: Quiz 2 grades are out PS4 (the last assignment) is due on Dec 2, 2013 by 11:59PM P4 (the last project) is due on Dec 5, 2013 by 11:59PM Final Exam is on Sunday Dec 8, 2013 at 9:00AM, Room 2051 (all topics are included-

Open book, open notes)

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Objectives

Discussion on Fault Tolerance

General background on fault tolerance

Process resilience, failure detection and reliable communication

Atomicity and distributed commit protocols

Recovery from failures

Reliable Communication

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Reliable Communication

Reliable Request-Reply Communication

Reliable Group Communication


As we considered reliable request-reply communication, we need also to consider reliable multicasting services

E.g., Election algorithms use multicasting schemes

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1 2

3

4

5

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A Basic Reliable-Multicasting Scheme Atomic Multicasting

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Reliable Multicasting

Reliable multicasting indicates that a message that is sent to a group of processes should be delivered to each member of that group

A distinction should be made between:

Reliable communication in the presence of faulty processesReliable communication when processes are assumed to operate correctly

In the presence of faulty processes, multicasting is considered to be reliable when it can be guaranteed that all non-faulty group members receive the message

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Basic Reliable Multicasting Questions

What happens if during multicasting a process P joins or leaves a group? Should the sent message be delivered? Should P (if joining) also receive the message?

What happens if the (sending) process crashes during multicasting?

What about message ordering?

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A Simple Case: Reliable Multicasting with Feedback Messages

Consider the case when a single sender S wants to multicast a message to multiple receivers

An S’s multi-casted message may be lost part way and delivered to some, but not to all, of the intended receivers

Assume that messages are received in the same order as they are sent

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Reliable Multicasting with Feedback Messages

M25

Last = 24

Receiver

Last = 24

Receiver

Last = 23

Receiver

Last = 24

Receiver

Network

Sender

HistoryBuffer

M25M25M25M25M25M25M25M25M25M25M25M25M25M25

Last = 24

Receiver

Last = 24

Receiver

Last = 23

Receiver

Last = 24

ReceiverSender

M25 M25 M25 M25

ACK25Missed 24ACK25ACK25

12An extensive and detailed survey of total-order broadcasts can be found in Defago et al. (2004)



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Atomic Multicast

C1: What is often needed in a distributed system is the guarantee that a message is delivered to either all processes or none at all

C2: It is also generally required that all messages are delivered in the same order to all processes

Satisfying C1 and C2 results in what we call atomic multicast

Atomic multicast:

Ensures that non-faulty processes maintain a consistent view

Forces reconciliation when a process recovers and rejoins the group

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Virtual Synchrony

A multicast message m is uniquely associated with a list of processes to which it should be delivered

This delivery list corresponds to a group view (G)

In principle, the delivery of m is allowed to fail: When a group-membership-change is the result of the sender

of m crashing Accordingly, m may either be delivered to all remaining processes, or

ignored by each of them

Or when a group-membership-change is the result of a receiver of m crashing

Accordingly, m may be ignored by every other receiver-- which corresponds to the situation that the sender of m crashed before m was sent

15A reliable multicast with this property is said to be “virtually synchronous”

The Principle of Virtual Synchrony

P1

P2

P3

P4

Reliable multicast by multiple point-to-point messages

P3 crashes

G = {P1, P2, P3, P4} G = {P1, P2, P4}

P3 rejoins

G = {P1, P2, P3, P4}Time

Partial multicast from P3 is discarded

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Message Ordering

Four different virtually synchronous multicast orderings are distinguished:

1.Unordered multicasts

2.FIFO-ordered multicasts

3.Causally-ordered multicasts

4.Totally-ordered multicasts

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1. Unordered multicasts

A reliable, unordered multicast is a virtually synchronous multicast in which no guarantees are given concerning the order in which received messages are delivered by different processes

Process P1 Process P2 Process P3

Sends m1 Receives m1 Receives m2

Sends m2 Receives m2 Receives m1

Three communicating processes in the same group

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2. FIFO-Ordered Multicasts

With FIFO-Ordered multicasts, the communication layer is forced to deliver incoming messages from the same process in the same order as they have been sent

Process P1 Process P2 Process P3 Process P4

Sends m1 Receives m1 Receives m3 Sends m3

Sends m2 Receives m3 Receives m1 Sends m4

Receives m2 Receives m2

Receives m4 Receives m4

Four processes in the same group with two different senders.

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3-4. Causally-Ordered and Total-Ordered Multicasts

Causally-ordered multicasts preserve potential causality between different messages

If message m1 causally precedes another message m2, regardless of whether they were multicast by the same sender or not, the communication layer at each receiver will always deliver m1 before m2

Total-ordered multicasts require that when messages are delivered, they are delivered in the same order to all group members (regardless of whether message delivery is unordered, FIFO-ordered, or causally-ordered)

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Virtually Synchronous Reliable Multicasting

A virtually synchronous reliable multicasting that offers total-ordered delivery of messages is what we refer to as atomic multicasting

Multicast Basic Message Ordering Total-Ordered Delivery?

Reliable multicast None No

FIFO multicast FIFO-ordered delivery No

Causal multicast Causal-ordered delivery No

Atomic multicast None Yes

FIFO atomic multicast FIFO-ordered delivery Yes

Causal atomic multicast Causal-ordered delivery Yes

Six different versions of virtually synchronous reliable multicasting




































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Distributed Commit

Atomic multicasting problem is an example of a more general problem, known as distributed commit

The distributed commit problem involves having an operation being performed by each member of a process group, or none at all

With reliable multicasting, the operation is the delivery of a message

With distributed transactions, the operation may be the commit of a transaction at a single site that takes part in the transaction

Distributed commit is often established by means of a coordinator and participants

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One-Phase Commit Protocol

In a simple scheme, a coordinator can tell all participants whether or not to (locally) perform the operation in question

This scheme is referred to as a one-phase commit protocol

The one-phase commit protocol has a main drawback that if one of the participants cannot actually perform the operation, there is no way to tell the coordinator

In practice, more sophisticated schemes are needed The most common utilized one is the two-phase commit protocol

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Two-Phase Commit Protocol

Assuming that no failures occur, the two-phase commit protocol (2PC) consists of the following two phases, each consisting of two steps:

Phase I: Voting Phase

Step 1• The coordinator sends a VOTE_REQUEST message to all

participants.

Step 2

• When a participant receives a VOTE_REQUEST message, it returns either a VOTE_COMMIT message to the coordinator telling the coordinator that it is prepared to locally commit its part of the transaction, or otherwise a VOTE_ABORT message



participants.

Step 2

• When a participant receives a VOTE_REQUEST message, it returns either a VOTE_COMMIT message to the coordinator telling the coordinator that it is prepared to locally commit its part of the transaction, or otherwise a VOTE_ABORT message



participants.

Step 2

• When a participant receives a VOTE_REQUEST message, it returns either a VOTE_COMMIT message to the coordinator indicating that it is prepared to locally commit its part of the transaction, or otherwise a VOTE_ABORT message.

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Phase II: Decision Phase

Step 1

Step 2

Two-Phase Commit ProtocolPhase II: Decision Phase

Step 1

• The coordinator collects all votes from the participants.

Step 2


Step 1


• If all participants have voted to commit the transaction, then so will the coordinator. In that case, it sends a GLOBAL_COMMIT message to all participants.

Step 2


Step 1



• However, if one participant had voted to abort the transaction, the coordinator will also decide to abort the transaction and multicasts a GLOBAL_ABORT message.

Step 2


Step 1




Step 2

• Each participant that voted for a commit waits for the final reaction by the coordinator.


Step 1




Step 2


• If a participant receives a GLOBAL_COMMIT message, it locally commits the transaction.


Step 1




Step 2


• If a participant receives a GLOBAL_COMMIT message, it locally commits the transaction.

• Otherwise, when receiving a GLOBAL_ABORT message, the transaction is locally aborted as well.

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2PC Finite State Machines

INIT

WAIT

COMMITABORT

CommitVote-request

Vote-abortGlobal-abort

Vote-commitGlobal-commit

INIT

WAIT

COMMITABORT

Vote-requestVote-commit

Global-abortACK

Global-commitACK

Vote-requestVote-abort

The finite state machine for the coordinator in 2PC

The finite state machine for aparticipant in 2PC

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2PC Algorithm

write START_2PC to local log;

multicast VOTE_REQUEST to all participants;

while not all votes have been collected{

wait for any incoming vote;

if timeout{

write GLOBAL_ABORT to local log;

multicast GLOBAL_ABORT to all participants;

exit;

}

record vote;

}

If all participants sent VOTE_COMMIT and coordinator votes COMMIT{

write GLOBAL_COMMIT to local log;

multicast GLOBAL_COMMIT to all participants;

}else{

write GLOBAL_ABORT to local log;

multicast GLOBAL_ABORT to all participants;

}

Actions by coordinator:

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write INIT to local log;

Wait for VOTE_REQUEST from coordinator;

If timeout{

write VOTE_ABORT to local log;

exit;

}

If participant votes COMMIT{

write VOTE_COMMIT to local log;

send VOTE_COMMIT to coordinator;

wait for DECISION from coordinator;

if timeout{

multicast DECISION_RQUEST to other participants;

wait until DECISION is received; /*remain blocked*/

write DECISION to local log;

}

if DECISION == GLOBAL_COMMIT { write GLOBAL_COMMIT to local log;}

else if DECISION == GLOBAL_ABORT {write GLOBAL_ABORT to local log};

}else{

write VOTE_ABORT to local log;

send VOTE_ABORT to coordinator;

}

Actions by participants:

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/*executed by separate thread*/

while true{

wait until any incoming DECISION_REQUEST is received; /*remain blocked*/

read most recently recorded STATE from the local log;

if STATE == GLOBAL_COMMIT

send GLOBAL_COMMIT to requesting participant;

else if STATE == INIT or STATE == GLOBAL_ABORT

send GLOBAL_ABORT to requesting participant;

else

skip; /*participant remains blocked*/

}

Actions for handling decision requests:

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Objectives






Recovery

So far, we have mainly concentrated on algorithms that allow us to tolerate faults

However, once a failure has occurred, it is essential that the process where the failure has happened can recover to a correct state

In what follows we focus on:

What it actually means to recover to a correct state

When and how the state of a distributed system can be recorded and recovered, by means of checkpointing and message logging

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Recovery

Error Recovery Checkpointing Message Logging

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Recovery


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Error Recovery

Once a failure has occurred, it is essential that the process where the failure has happened can recover to a correct state

Fundamental to fault tolerance is the recovery from an error

The idea of error recovery is to replace an erroneous state with an error-free state

There are essentially two forms of error recovery:

1. Backward recovery

2. Forward recovery

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Backward Recovery

In backward recovery, the main issue is to bring the system from its present erroneous state “back” to a previously correct state

It is necessary to record the system’s state from time to time onto a stable storage, and to restore such a recorded state when things go wrong

Each time (part of) the system’s present state is recorded, a checkpoint is said to be made

Some problems with backward recovery: Restoring a system or a process to a previous state is generally expensive

(in terms of performance) Some states can never be rolled back (e.g., typing in UNIX rm –fr *)

Forward Recovery

When the system detects that it has made an error, forward recovery reverts the system state to error time and corrects it, to be able to move forward

Forward recovery is typically faster than backward recovery but requires that it has to be known in advance which errors may occur

Some systems make use of both forward and backward recovery for different errors or different parts of one error

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Recovery


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Why Checkpointing?

In fault-tolerant distributed systems, backward recovery requires that systems “regularly” save their states onto stable storages

This process is referred to as checkpointing

Checkpointing consists of storing a “distributed snapshot” of the current application state, and later on, use it for restarting the execution in case of a failure

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Recovery Line

In capturing a distributed snapshot, if a process P has recorded the receipt of a message, m, then there should be also a process Q that has recorded the sending of m

Initial state A snapshot

Message sent from Q to P

A recovery line Not a recovery line

A failure

They jointly form a distributedsnapshot

We are able to identify both, senders and receivers.

P

Q

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m

Checkpointing

Checkpointing can be of two types:

1. Independent Checkpointing: each process simply records its local state from time to time in an uncoordinated fashion

2. Coordinated Checkpointing: all processes synchronize to jointly write their states to local stable storages

Which algorithm among the ones we’ve studied can be used to implement coordinated checkpointing? A simple solution is to use 2PC

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Domino Effect Independent checkpointing may make it difficult to find a recovery line,

leading potentially to a domino effect resulting from cascaded rollbacks

With coordinated checkpointing, the saved state is automatically globally consistent, hence, domino effect is inherently avoided

A failure

P

Q

Not a Recovery LineRollback

Not a Recovery LineNot a Recovery Line

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Recovery


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Why Message Logging?

Considering that checkpointing is an expensive operation, techniques have been sought to reduce the number of checkpoints, but still enable recovery

An important technique in distributed systems is message logging

The basic idea is that if transmission of messages can be replayed, we can still reach a globally consistent state, yet without having to restore that state from stable storage

In practice, the combination of having fewer checkpoints and message logging is more efficient than having to take many checkpoints

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Message Logging

Message logging can be of two types:

1. Sender-based logging: A process can log its messages before sending them off

2. Receiver-based logging: A receiving process can first log an incoming message before delivering it to the application

When a sending or a receiving process crashes, it can restore the most recently checkpointed state, and from there on “replay” the logged messages (Is it fine for non-deterministic behaviors?)

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Replay of Messages and Orphan Processes

Caveat: Incorrect replay of messages after recovery can lead to orphan processes

P

Q

R

M1

Logged Message

Unlogged Message

M2M3

Q crashes Q recovers

M1

M1 is replayed

M2

M2 can never be replayed

M3

M3 becomes an orphan

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Objectives






All Covered!

Next Class

Distributed File Systems-Part I

Thank You!

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distributed systems cs 15-440 fault tolerance- part iii lecture 19, nov 25, 2013 mohammad hammoud 1

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