1.3. Consistent Cuts

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Del curso dictado por University of Illinois at Urbana-Champaign

Cloud Computing Concepts, Part 1

524 calificaciones

University of Illinois at Urbana-Champaign

Cloud Computing Concepts, Part 1

524 calificaciones

Curso 1 de 6 en SpecializationCloud Computing

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.

De la lección

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

Conoce a los instructores

Indranil Gupta
Professor
Department of Computer Science

[MUSIC]

In this lecture we continue discussion of the Chandy-Lamport Global Snapshots

algorithm, and see why it was causally correct.

So we discuss this notion known as consistent cuts.

Before we discuss consistent cuts, let's discuss a cut.

A cut is a time frontier at each process and at each channel in the system.

So essentially at each process and at each channel, you decide a time point, and

everything to the left of that time point on that process or

channel is considered to be in the cut.

And anything to the right of that time point is considered to be out of the cut.

So, that is a cut.

Some cuts are consistent, and in order for

a cut to be consistent you need to make sure that every pair of events, e and f,

that occurred in the system at any processes, they need not be at

the same process, such that e is in the cut and f happened before e,

if these two conditions are true then it is also true that f is also in the cut.

Okay, if this is true then the cut obeys causality and

is said to be a consistent cut.

So let's see two examples of cuts.

The green cut on the left is a consistent cut because it obeys the two conditions.

Notice here, the messages over here, B happened before E,

and E is in the cut and so B is also in the cut.

H happened before F, F is out of the cut, H is in the cut, and that's okay.

So in this particular cut, the message H to F is set to be in transit and

is captured by the cut itself as being in transit.

The red cut on the other hand, shown in the right, is not a consistent cut,

because consider this message that has a send event G and the receive event D.

D is in the cut, but G which happened before D is out of the cut.

And that is not allowed by the consistency predicated,

so this red line I've drawn out here is a cut, but it is not a consistent cut.

So our Global Snapshot algorithm which you saw in the last lecture, calculated this

particular snapshot over here which consists of each of the process states,

S1, S2, S3, and the states for each of the channels C, I, J,

which are six channels in the system.

So our Global Snapshot algorithm in fact results in a snapshot that

is causally correct in the sense that it corresponds to a consistent cut.

By the green dotted line here I am showing the consistent cut that is captured by our

Global Snapshot algorithm.

You'll notice that the place where the cut cuts across each process

timeline is in fact the time at which that process state is calculated,

S1 for P1, S2 for P2 and S3 for P3.

And also this consistent cut cuts across the message G to D which is also

a part of the Global Snapshot calculated by the Chandy-Lamport algorithm.

So the consistent cut shown by this green dotted line is in fact

the same as the state captured by the Global Snapshot algorithm.

In fact, you can show that in any invested system,

any run of the Chandy-Lamport Global Snapshot algorithm

always results in a global snapshot that corresponds to a consistent cut.

Let's see why this is true.

Let's look at the proof.

Let ei be an event occurring at Pi, and

ej be an event occurring at Pj, such that ei happens before ej.

This is Lamport's happens before relationship.

The snapshot algorithm ensures that if ej is in the cut,

then ei is also in the cut, okay?

We are going to prove this fact.

If this fact were true, then this would mean that the consistency condition for

the consistent cut is obeyed and so

the result of the snapshot algorithm is in fact a consistent cut.

In other words,

what we want to show is that if ej happens before Pj records its own state, meaning

ej actually belongs to the cut calculated by the Global Snapshot algorithm,

then it must also be true that ei happens before Pi records its own state, that is

ei is also a part of the cut calculated by the Global Snapshot algorithm.

So let's prove this by contradiction.

Let's assume that the first condition is true, which is that ej is a part of

the cut, so ej happens before Pj records its own state.

But that the second condition is not true which is that Pi records its own state is

in fact happens before ei, okay?

So, because all the events at Pi are ordered linearly because Pi's clock,

if its not true that ei happened before Pi records its own state, the reverse

must be true which is that Pi records its own state must happen before ei.

Along with this, you also have the fact that ei happened before ej.

So, consider the causal path that goes from ei to ej,

along that causal path consider all the sequences of process timelines, and

all the channels on which messages pass along that causal path.

Due to the FIFO ordering on the causal paths and

the linear ordering of events at each process, it must be true that

the markers on each link above proceed the regular application messages.

Why do the markers proceed the regular application messages?

Because Pi records its own state before ei happens and so

the markers are sent out when Pi records its own state, and so the markers which

proceed on the causal path from ei to ej will proceed the ei to ej messages.

But then this automatically means that the marker is received at

the process Pj before the event ej happens that crosses Pj.

But then this means that Pj must have received the marker before ej, and

so it cannot be true that ej happened before Pj records its own state.

And so we have a contradiction here.

And so what we assumed in the green statement over here at the top of

the slide must be false.

And so it must be true that if ej belongs to the cut,

then ei also belongs to the cut.

That completes our proof.

In the next lecture,

we'll see some examples of kinds of properties which the Chandy-Lamport

algorithm is used to detect in the global distributed system itself.

[MUSIC]

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