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1.4. Safety and Liveness

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Cloud Computing Concepts, Part 1

Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-Champaign

4.5 (721 classificações) | 63K alunos inscritos

Curso 1 de 6 no Computação em nuvemComputação em Nuvem Programa de cursos integrados

Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing. Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more! Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required. The course also features interviews with leading researchers and managers, from both industry and academia.

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Habilidades que você aprenderá

Distributed Algorithm, Distributed Computing, C++, Cloud Computing

Avaliações

4.5 (721 classificações)

5 stars
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4 stars
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3 stars
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1 star
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WK

Feb 27, 2018

This is a great course with a good balance of just theory and concepts, with practical ideas and exercises. The programming exercise can be a bit annoying, but was enjoyable nonetheless.

VR

Dec 31, 2017

very happy with the course. got to know new concepts. assignments are awesome. enjoyed working on them. enjoyed the course. it would be good if we get a color and original certificate.

Na lição

Week 5: Classical Distributed Algorithms

Lesson 1: This module covers how to calculate a distributed snapshot, leveraging causality again to circumvent the synchronization problem. Lesson 2: This lecture teaches how to order multicasts in any distributed system. Algorithms for assigning timestamp tags to multicasts using various flavors of ordering – FIFO, Causal, and Total – are covered. The module also covers virtual synchrony, a paradigm that combines reliable multicasts with membership views. Lesson 3: Consensus is one of the most important problems in a distributed system, enabling multiple machines to agree. This module uses Paxos, one of the most popular consensus solutions used in the industry today. Paxos is not perfect because consensus cannot be solved completely – an optional lecture presents the famous FLP proof of impossibility of consensus.

1.1. What is Global Snapshot?7:13

1.2. Global Snapshot Algorithm10:36

1.3. Consistent Cuts6:15

1.4. Safety and Liveness7:32

Ministrado por

Indranil Gupta
Professor

Transcrição

Uh, in this lecture we'll see two very important properties

that are, uh, desired in distributed systems;

these properties are called safety and liveness,

and along the way we'll also discuss

the kinds of, um, properties

that, uh, the global snapshot algorithm can be used

to detect in a distributed system.

So correctness in distributed systems is, uh, highly desired,

and there are two properties that can be used to,

uh, specify correctness requirements.

These properties are known as liveness and safety.

Uh, the definitions are distinct,

uh, but they are often confused with each other,

so its' very important for us to be able to distinguish

one from the other.

Let's look at liveness first.

Uh, in a nutshell, liveness is a guarantee that something good

will happen eventually, okay?

Uh, eventually does not imply a time bound,

but if you let the-the system run long enough,

then it-it guarantees, uh, liveness.

Uh, so examples of this, uh, are, in the real world,

uh, the guarantee that in an Olympics 100 meter dash,

at least one of the athletes will win the gold medal.

This is a liveness guarantee, something good,

which is winning the medal, happens eventually.

Uh, a criminal will eventually be jailed

is a liveness guarantee

that legal systems in many countries, uh, try to guarantee.

It's hard to guarantee it, but it-it is a desired property.

In a distributed system, uh, the termination property

is a liveness property.

You want a distributed computation-computation

to terminate,

and, uh, this is the, uh, guarantee that something good,

which is termination, will happen eventually.

Completeness and failure detectors,

uh, is a liveness property.

This is a property that says that eventually,

every failure will be detected

by the other non-faulty processes in the system.

In consensus, uh, liveness says

that all processes eventually decide on a value.

Safety, on the other hand, is a guarantee that something bad

will never happen in the system, okay?

So notice that there we are using "bad" instead of "good"

earlier in the liveness,

and we are saying will never happen,

uh, because we desire that it never happens in the system.

Again, in the real world, a peace treaty between two nations

is an example of a safety property,

uh, which guarantees that war

will never happen between those two nations.

Again, this is a desired property;

this is not always what happens.

Uh, wars have happened in spite of treaties,

um, so treaties are clearly not safe all the time,

but it's an example of a desired safety property.

Uh, legal systems try to guarantee the safety property

that an innocent person will never be jailed;

that would be really bad.

In a distributed system,

uh, the fact that we do not want deadlocks

in a distributed transaction system

is an example of a safety property which we desire.

The fact that we do not want objects to be orphaned,

meaning they don't have any pointers pointing to them,

is an example of a safety property.

Accuracy and failure detectors is a safety property

because it says that we do not want any mistaken detections

of non-faulty processes being marked as failed.

And in consensus, we do not want two different processes

to decide on two different values,

uh, because that would be bad and that would be unsafe.

Uh, guaranteeing both liveness and safety is very hard.

In failure detectors guaranteeing,

um, uh, liveness and safety,

which is completeness and accuracy,

is, uh, impossible in an asynchronous distributed system,

especially if you want these to be time-bounded.

Uh, in c-in the consensus problem, uh, making decisions,

uh, within a time bound and, uh, correct decisions,

um, uh, making sure that

the correct decisions are correct

cannot both be guaranteed

in an asynchronous distributed system.

You can guarantee eventual liveness,

which is what algorithms like Paxos, uh, guarantee,

but time-bounded liveness cannot be guaranteed.

And of course, as many of us know,

it's very hard for legal systems to guarantee both,

uh, liveness and safety,

and in fact to guarantee either liveness or safety

is very hard.

Um, it's-it does happen

that some criminals never serve any jail time,

and it also does happen that innocents are jailed.

So in the language of global states;

remember that a distributed system moves

from one global state to another via causal steps,

um, which you saw in an earlier lecture

in the snapshot series,

uh, liveness with respect to a property Pr

in a given state S essentially means that,

uh, the state S satisfies Pr,

and if this is, uh, not true, then there is

some causal path that goes from the state S to another state S',

global state S', where S' would satisfy Pr.

Okay, so all you require here is that there is some way to get

from the current state to another state,

um, that, uh, satisfies liveness.

If this is true for every state that you are in,

that whate-whatever state you are in,

you can reach another state that

um, uh, that satisfies liveness

uh, the liveness property Pr,

then this system is said to be satisfying

the liveness property.

Notice that you do not require all the states, uh, reachable

to satisfy this property Pr,

just some causal path and some end state

to satisfy the property Pr.

Safety, on the other hand, with respect to property Pr,

says that, um, in state S, S satisfies the property Pr,

and all global states S' that are reachable from S

via causal paths also satisfy Pr.

If this is true, then the safety property is true.

So how do we use a global snapshot algorithm

to detect global properties?

Well, the snapshot algorithm

can be used to detect global properties that are stable.

When I say stable, I mean properties that,

once they are true,

they st-they stay true forever afterwards.

Uh, these could be either stable liveness properties

or stable non-safety properties.

A stable liveness property might be of the kind

the computation has terminated.

Once a computation has terminated,

it stays terminated forever,

and this is obviously an example of a good property

that we want to be true,

so it's a liveness property that is stable,

and it can be u-and it can be detected

using the global snapshot algorithm.

Stable non-safety properties include, um, uh,

the fact that there is a deadlock.

Once a deadlock happens in the system,

it will stay forever until you do something about it.

Also, uh, once an object is orphaned, uh, that is,

it has no pointers pointing to it,

it will stay that way until you do something about it.

Both these are examples of non-safety properties

that are stable,

and so the global snapshot, uh, algorithm which we discussed,

the Chandy-Lamport algorithm,

can be used to detect, uh, these stable properties.

Uh, why can you use to dete-

these to detect the sto-stable properties?

Because Chandy-Lamport algorithm

is guaranteed to be causally correct.

Even though the Chandy-Lamport algorithm does not, uh,

guarantee that the snapshot calculated by it

holds at any physical point of time in the past,

it does guarantee causal correctness,

uh, which means that it does not violate

our, uh, human being's understanding of

what happens causally in the distributed system,

and so it can be used to detect, uh, stable properties.

Wrapping up our discussion of snapshots,

the ability to calculate a global snapshot

is really important in a distributed system,

uh, but you want to calculate the snapshot,

uh, concurrently and parallelly

while allowing the application to continue proceeding

and sending its application messages,

and the Chandy-Lamport algorithm allows us to do exactly that.

The output of a Chandy-Lamport algorithm is

a, uh, global snapshot that obeys causality.

In other words, it satisfies a consistent cut,

it's equivalent to a consistent cut,

and it can be used to detect stable global properties

which are either, uh, liveness properties

or non-safety properties.

We've also discussed two very important definitions,

liveness and safety,

which, um, are relevant of course

to the snapshot- snapshots discussion.

These are properties that appear, uh, elsewhere

and in other parts of this course,

uh, because these are very important,

uh, classes of properties

that we desire in distributed systems.

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