eec 688/788 secure and dependable computing lecture 14 wenbing zhao department of electrical and...

EEC 688/788EEC 688/788Secure and Dependable ComputingSecure and Dependable Computing

Lecture 14Lecture 14

Wenbing ZhaoWenbing ZhaoDepartment of Electrical and Computer EngineeringDepartment of Electrical and Computer Engineering

Cleveland State UniversityCleveland State University

[email protected]@ieee.org

22

04/18/2304/18/23EEC688/788: Secure & Dependable EEC688/788: Secure & Dependable

ComputingComputing Wenbing ZhaoWenbing Zhao

OutlineOutline• Group communication systems

– Ordered multicast– Techniques to implement ordered multicast– Group membership service– Agreed and safe delivery

• Checkpointing and recovery• Reference:

– Reliable distributed systems, by K. P. Birman, Springer; Chapter 14-16

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Group Communication SystemGroup Communication System

• Services provided by the GCS– Membership service: who is up and who is down

• Deals with failure detection and more

– Reliable, ordered, multicast service• FIFO, causal, total

– Virtual synchrony service• Virtual synchrony synchronizes membership change with

multicasts

• GCS is often used to build fault tolerant systems

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Reliable MulticastReliable Multicast• Reliable multicast – the message is targeted to

multiple receivers, and all receivers receive the message reliably– Positive or negative acknowledgement– Need to avoid ack/nack implosion

• Distinguish receiving from delivery!

Application

Middleware

Receiving

Delivering

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Ordered Reliable MulticastOrdered Reliable Multicast• Ordered reliable multicast – if many messages are

multicast by many senders, in what order the messages are delivered at the receivers?– First in first out (FIFO)– Causal – the causal relationship among msgs preserved– Total – all msgs are delivered at all receivers in the same

order

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FIFO Ordered MulticastFIFO Ordered Multicast

• FIFO or sender ordered multicast:Messages are delivered in the order they were sent (by any single sender)

p

q

r

s

a

b c d

e

delivery of c to p is delayed until after b is delivered

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Causally Ordered MulticastCausally Ordered Multicast

• Causal or happens-before ordering:If send(a) send(b) then deliver(a) occurs before deliver(b) at common destinations

p

q

r

s

a

b

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p

q

r

s

a

b cdelivery of c to p is delayed until after b is delivered

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p

q

r

s

a

b c

e

delivery of c to p is delayed until after b is deliverede is sent (causally) after b

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p

q

r

s

a

b c d

e

delivery of c to p is delayed until after b is delivereddelivery of e to r is delayed until after b&c are delivered

1111



Totally Ordered MulticastTotally Ordered Multicast

• Total ordering:Messages are delivered in same order to all recipients (including the sender)

p

q

r

s

a

b c d

e

all deliver a, b, c, d, then e

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Implementing Total OrderingImplementing Total Ordering

• Use a token that moves around– Token has a sequence number– When you hold the token you can send the next burst

of multicasts

• Use a sequencer to order all multicast– Message is first multicast to all, including the

sequencer; then the sequencer determines the order for the message and informs all

– Or send to the sequencer and the sequencer multicast with total order information

– Each sender can take turn to serve as the sequencer

1313



Group membership serviceGroup membership service

• Input:– Process “join” events– Process “leave” events– Apparent failures

• Output:– Membership views for group(s) to which those

processes belong

1414



Issues?Issues?

• The service itself needs to be fault-tolerant– Otherwise our entire system could be crippled

by a single failure!– Hence Group Membership Service (GMS)

must run some form of protocol (GMP)

1515



ApproachApproach

• Assume that GMS has members {p,q,r} at time t• Designate the “oldest” of these as the protocol

“leader”– To initiate a change in GMS membership, leader will

run the GMP– Others can’t run the GMP; they report events to the

leader

1616



GMP ExampleGMP Example

• Example:– Initially, GMS consists of {p,q,r}– Then q is believed to have crashed

p

q

r

1717



Unreliable Failure DetectionUnreliable Failure Detection

• Recall that failures are hard to distinguish from network delay– So we accept risk of mistake– If p is running a protocol to exclude q because

“q has failed”, all processes that hear from p will cut channels to q

• Avoids “messages from the dead”

– q must rejoin to participate in GMS again

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Basic GMPBasic GMP• Someone reports that “q has failed”• Leader (process p) runs a 2-phase commit

protocol– Announces a “proposed new GMS view”

• Excludes q, or might add some members who are joining, or could do both at once

– Waits until a majority of members of current view have voted “ok”

– Then commits the change

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• Proposes new view: {p,r} [-q]• Needs majority consent: p itself, plus one more (“current”

view had 3 members)• Can add members at the same time

p

q

r

Proposed V1 = {p,r}

V0 = {p,q,r}OK

Commit V1

V1 = {p,r}

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Special Concerns?Special Concerns?

• What if someone doesn’t respond?– P can tolerate failures of a minority of

members of the current view• New first-round “overlaps” its commit:

– “Commit that q has left. Propose add s and drop r”

– P must wait if it can’t contact a majority• Avoids risk of partitioning

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What If Leader Fails?What If Leader Fails?

• Here we do a 3-phase protocol– New leader identifies itself based on age ranking

(oldest surviving process)– It runs an inquiry phase

• “The adored leader has died. Did he say anything to you before passing away?”

• Note that this causes participants to cut connections to the adored previous leader

– Then run normal 2-phase protocol but “terminate” any interrupted view changes leader had initiated

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• New leader first sends an inquiry• Then proposes new view: {r,s} [-p]• Needs majority consent: q itself, plus one more (“current”

view had 3 members)• Again, can add members at the same time

p

q

r

Proposed V1 = {r,s}

V0 = {p,q,r}OK

Commit V1

V1 = {r,s}

Inquire [-p]

OK: nothing was pending

2323



Safe and Agreed DeliverySafe and Agreed Delivery

• For totally ordered reliable multicast, there are two delivery policies– Safe delivery: a message is delivered only

when all correct processes have received it– Agreed delivery: a message is delivered as

long as it is the next message in total order

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Safe and Agreed DeliverySafe and Agreed Delivery

• Safe delivery guarantees the uniformity of multicast:– If a message is delivered to any process, it is

delivered by all correct processes

• Agreed delivery does not: – It is possible that a message is delivered in

one (or more) process, but is not delivered by some correct process

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Checkpointing and RecoveryCheckpointing and Recovery

• Faults occur over time. How to ensure a fault tolerant system remain operational for extensive period of time?– Recover failed replicas, or replace failed

replicas with new one => Recovery is needed

• How to recover a failed replica or install a new replica?– Checkpointing a correct replica and transfer

the state to the recovering replica



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CheckpointingCheckpointing

• Checkpointing: the act of taking a snapshot of an entity so that we can restore it later

• A replica is a process running in an operating system. The state of a process– Processes' memory, stack and registers– Threads – Open or mmap'ed files – Current working directory– Interprocess communication:

• Semaphores, shared memory, pipes, sockets

– Dynamic Load Libraries – …

2727



CheckpointingCheckpointing

• Many tools are available to perform checkpointing transparently or semi-transparently– http://www.checkpointing.org/– Condor, libckpt, etc.– Checkpoints taken in general are not portable– Checkpoint size might be big

2828



Checkpointing of Application StateCheckpointing of Application State

• Sometimes it is more efficient to save and store the application state only – Checkpoints can be very portable and compact in size– class Counter {

int counter; Counter(int initVal) { counter = initVal; }

void increment() {counter++; } void decrement() {counter--; } void setState(int c) {counter = c; }

int getState() { return counter;}|}

2929



LoggingLogging

• Logging of messages– Checkpointing in general is expensive– Logging of messages is cheaper

=> we can periodically do checkpointing, or do checkpointing on demand and log all messages in between

• Logging of other non-deterministic activities– Access order to shared data

3030



Roll-Forward RecoveryRoll-Forward Recovery

• With replication in space, it is possible to recover a fault while the system is progressing ahead

• Roll-forward recovery is made possible by– Checkpointing of replica state– Logging of incoming messages– Reliable, totally ordered group communication

system

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• We want to ensure the newly admitted replica to have a consistent state with others when it starts

• Steps of adding a new replica into a group (with on-demand checkpointing)– A recovered (or a new) replica joins a group– A join message is multicast in total order– On receiving the join message, it is put into incoming

message queue and wait for processing– When the join message is at the head of the queue, a

checkpoint is taken and it is transferred to the new replica

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– At the new replica, it starts queueing messages after it receives the join messages (sent by itself)

– When the checkpoint is received by the new replica, its state is restored using the received checkpoint (the checkpoint is delivered out of order!)

– The queued messages are delivered in order, at the new replica

– Other replicas do not stop and wait for the new replica

• Steps of adding a new replica into a group with periodic checkpointing is similar

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Steps of Roll-Forward Steps of Roll-Forward RecoveryRecovery

ExistingReplica

Recovery_Start Recovery_Start

Recovery_Start

Recovery_Starttriggers queueingof messages

Recovery_Startis queued, just like a regular message

Checkpoint

op1

op2Recovery_Start

New or restartedReplica

(i)

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op3

op3

op3

op3

Recovery_S tart

ExistingReplica

New messageop3 is queuedwhile waiting forreply of op2

Checkpoint

op1

op2

Recovery_S tart


(ii)

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ExistingReplica

op3

op3

Recovery_S tart

Loggedmessages beforeRecovery_Startare consolidatedand multicast

op2returns

Checkpoint

op1

op2Recovery_S tart


(iii)

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ExistingReplica

op3 op3


Normal operationis resumed andqueued messages aredelivered

O utgo ing messages as a result of opera tions beforeRecovery_Start aresuppressed

Transferredlog is expandedand the checkpo int is app lied

Checkpoint Checkpoint

op1 op1op2 op2

(iv)

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Roll-backward RecoveryRoll-backward Recovery• Roll-backward recovery is used for systems

relying on replication in time for fault tolerance– When a failure occurs, roll back using the most recent

checkpoint (and retry)



3838

Roll-backward Recovery in a Roll-backward Recovery in a Distributed SystemDistributed System

• Performing roll-backward recovery in a distributed system is non-trivial– Need to solve the distributed snapshot problem– It is easy to perform a local checkpoint of a process,

but in a distributed system, when one process rolls back, other processes must also roll back to a consistent state



eec 688/788 secure and dependable computing lecture 14 wenbing zhao department of electrical and...

Documents

multicast causal

multicast fifo

b slide

multicast techniques

multicast service fifo

multicast total ordering

order slide

delivery of e