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. This course builds on the material covered in the Cloud Computing Concepts, Part 1 course.
4.1. Single-processor Scheduling
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From the course by University of Illinois at Urbana-Champaign
Cloud Computing Concepts: Part 2
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University of Illinois at Urbana-Champaign
161 оценки
Course 2 of 6 in the Specialization Cloud Computing
From the lesson
Week 3: Emerging Paradigms
Lesson 1: We study the emerging area of stream processing, touching on key design aspects of Apache Storm. Lesson 2: We study how enormous graphs can be processed in clouds. Lesson 3: We study various types of networks/graphs that are both natural and artificial, and their surprising commonalities. Lesson 4: This module presents classical scheduling algorithms that have been used in operating systems since the inception of computers. We then cover two popular scheduling algorithms for Hadoop.
Meet the Instructors
Indranil GuptaProfessor
Department of Computer Science
[MUSIC]
So in this next series of lectures we're going to see the important topic of
scheduling.
In particular, today we're going to see single-processor scheduling.
But let's start from a, a higher level.
So why do you need scheduling at all?
Well, many cloud computing system, you have multiple tasks to schedule.
For instance, if we just consider your laptop or workstation or even your mobile
device, there are multiple processes that are running on that device.
On your laptop, you know, you have your browser running,
your your editor running you, you maybe you have an IDE running.
And essentially the operating system needs to
schedule these processes across the limited resources that it has.
Primarily the main resource is the processor or the CPU and
if you have a single core OS or
a single core machine, then you have a particular series of scheduling problems.
And of course you might have a multicore machine where you might be able to
schedule multiple processes simultaneously.
But once again, typically the number of processes
is much more than the number of processors that you have,
the number of CPU that you have, and so scheduling here becomes a challenge.
In a cloud computing cluster, you might have a Hadoop job running.
And that Hadoop job has multiple tasks,
so the cluster needs to decide where to schedule each task of the Hadoop job.
You might have multiple Hadoop jobs, each with their own tasks.
And that they might be competing for resources and
now the cloud scheduler needs to decide
where to schedule these different tasks of these different Hadoop jobs.
You have a large number of tasks and there are limited resources that these tasks
absolutely need so each task typically needs some amount of processing capacity,
a processor some amount of memory, and of course it needs disk and network as well.
But typically scheduling algorithms vary about processor and
memory because these are the resources for where which there is most connection.
There are two goals of scheduling algorithms.
The first is to
increase the throughput on the number of jobs you can execute per time unit.
And related to that is releasing the response time or
releasing the time that it takes one job to complete in your system.
Essentially we'll just take into account the completion time of jobs.
The second is high utilization of resources.
You want your resource to be utilized as much as possible.
You want your, all of your processors to be utilized 100% of the time,
all of your memory to be utilized 100% of the time, so
that you're making the most out of the resources that you invested your money in.
So, let's look at single-processor scheduling in the lecture.
So, you have just one processor in this particular problem and
you might have multiple tasks that are trying to compete for these resources.
In this example, I have three tasks.
Task 1, Task 2, Task 3.
Task 1 arrives at times 0.
It has a running time of 10 time units.
Task 2 arrives at time 6.
It has a running of 5 time units.
And Task 3 arrives at time 8.
It has a running time of 3 time units.
So you need to decide when to schedule these,
each of these tasks at what time on the single processor.
So one of the basic scheduling algorithm's is what is known as fi, FIFO, or
first in first out.
This is also known as FCFS or first come first serve.
So in this algorithm you maintain the tasks in a queue,
in the order in which the tasks arrive.
So tasks that arrived earlier are ahead in the queue.
Tasks that arrived later are later on in the queue.
Whenever a processor is free, you de-queue the task at the head of the queue and
you schedule it on the processor.
So for instance, at 0 when Task 1 arrives, place it in the queue.
It's the only task right now and so it executes until time inter, time time 6.
At the point Task 2 arrives, it's placed in the queue, but then Task 1
is still executing on the processor so Task 1 is allowed to complete.
At time 8 the Task 3 arrives and it's added to the queue but after Task 2.
At time 10 when Task 1 finishes, the head of the queue is Task 2, so
that's dequeued and it's scheduled on the processor.
It runs until completion, which happens at time 15, at which time, once again,
the header of the queue,
which is Task 3 now, is dequeued as is scheduled on the processor.
Task 3 finishes at time 18.
So Task 1 finished at time 10.
Task 2, which arrived second, finished at time 15.
Task 3, which arrive, which arrived last, finished at time 15.
This is the FIFO algorithm and even though, we are discussing it for
a single processor now you're here, FIFO is in fact used by a variety of different
cloud computing schedulers, including the Hadoop scheduler.
So how about the performance of FIFO or FCFS?
Well the average completion time, which is really what we care about,
we want the completion time to be as small as possible may be kind of high for FIFO.
For instance, for example, the average completion time can be calculated by
adding the completion times for the three tasks and then divided, dividing them by
3, so that's 10 plus 15 plus 18 divided by 3, or 43 by 3, or 14.33 time units.
This is not, this is on the higher side, I'm telling you.
But, you know, can we do better than 14.33?
What is another scheduling algorithm that is better, that does better than 14.33?
Well that better scheduling algorithm is known as shortest task first.
So in this particular approach, again you maintain all the tasks in a queue,
but not in the order of arrival.
Instead you maintain the the tasks in increasing order of running time.
So tasks that are shorter are maintained towards the head of the queue and
tasks that are longer are maintained at the tail of the queue.
Whenever the processor is free, you de-queue the head task and
you schedule it on the processor.
So once again let's go to an example of your three tasks, Tasks 1, 2, and 3,
with task lengths of 10, 5, and 3.
The arrival times here are all zeros because I want to show you an example
where the STF scheduling algorithm in fact has to make decisions among tasks.
And so here on Task 3 which has the shortest length is chosen to run first.
It starts running at time 0 and ends at time 3.
At which point Task 2 which is the next shortest task is scheduled.
It runs from time 3, finishes at time 8, and finally Task 1,
the longest task is run from time 8 to time 18.
So this is the shortest task first scheduling algorithm, STF,
sometimes this is, this algorithm, is also known as SJF, or
shortest job first scheduling algorithm, but we'll stick with STF
because I want to maintain the uniformity of terminology here.
So why does STF do better than FIFO?
Well STF in fact that can be proved, formally proved, to create the shortest or
the smallest average completion time among all possible scheduling approaches for
a single processor system.
So given a single processor system and a bunch of tasks that you need to schedule,
among all the possible scheduling strategies that you might have,
the STF strategy gives you the smallest average completion time.
For instance, for our previous example, the, the average completion time of STF
is the sum of the three completion times for the three tasks, divided by 3, which
happens to be, 18 plus 8 plus 3, divided by 3, which is 29 by 3, which is 9.66.
And this is much smaller than the completion time of 14.33 which we had for
the FIFO/FCFS approach.
The other nice thing about STF is that it is
actually a special case of what is known as priority scheduling.
In this particular STF scheduling approach, we sorted the tasks
according to the running time, and in fact we treated the running time as a priority.
But instead the priority might be specified by the user, for
instance the user might specify a higher priority for interactive tasks that
involve keyboard and mouse input, but a lower priority for background batch tasks
that don't require any user input but have an output at the end of the long run.
You always place the higher priority tasks at the head of the queue and
whenever those tasks, I wanted to compute you let them run on the processor.
A third scheduling algorithm is what is known as round-robin scheduling algorithm.
In this case scheduling algorithm you allow tasks to be run
in pieces or in chunks.
In other words you split up a task into small quanta, and the quanta may be used
as small time units as just say one time unit as in our example here.
And you run only a portion of the task at the head of the queue.
When that portion of the task is done you preempt that task.
In other words you save the task of that, and the state of that task and
then you add it add the task back again to the, to the end of the queue.
You take the head of the queue task again, whatever task happens to be at the head of
the queue, and then you start running that task at the processor.
In order to run this again we need to bring back in the saved
state of that task if it was preempted in the past.
So again in our example, we go back to our example of three tasks, 1, 2 and
3 which arrive at times 0, 6, and 8 respectively.
So Task 1 arrives first, it's the only one so it's run so
that it occupies the entire processor until time 6.
At which point of time Task 2 arrives, and so Task 2 is now run for one time unit.
And then when it's run for one time unit, it is put back at the end of the queue and
Task 1 is allowed to run, at this point of time, Task 1 has run for
6 plus one 7 time units.
Task 2 has run for only one time unit.
Then at time 8, Task 3 arrives and now it is allowed to run for
one time unit because it's at the head of the queue, now after it runs for
one time unit it's put at the tail of the queue and now Task 1 and
Task 2 are allowed to run, and then again Task 3.
So this pattern repeats again.
Then again you have other three tasks to run.
And at this point of time, Task 1 is run for nine time units.
You have 6 plus 3 greens, that's 9 greens.
Task 2 is run for 3 time units.
And Task 3, it has the blue ones, has run, has run for 3 time units.
And units 1, 2, and 3.
And so Task 3 is actually done at time 15, 'kay?
So after this you have a Task 1 and Task 2 to cycle through, so
you round-robin through them, and then this continues for a while.
At this point of time actually Task 1 is done at time extent of 17.
Round-robin essentially gives a fair share of the resource,
the processor, to all the tasks that are present in the system.
So when do you prefer round-robin and when do you prefer shortest task first or FIFO?
Well, round-robin is more preferable for interactive applications,
applications that need, periodically need to, a, access to the processor so
that they can run and process things like the keyboard input and
the mouse input the and so on and so forth, 'kay?
These are applications where the user needs quick responses from the system.
On the other hand, FIFO or shortest tasks first is more preferable for
batch applications where the user submits a job, goes away for a while, and
comes back later on to get the result.
For these you don't necessarily need to have applications that
that run in quanta, but it can give them access to the CPU all at once.
And in fact, there are many hybrid scheduling approaches that combine both
the round-robin that we have seen and the FIFO/STF that we have seen,
these are known as hierarchical approaches.
So to wrap up our discussion of single processor schedule algorithms we've
discussed only classical algorithms including FIFO/FCFS which
schedules in the order of arrival.
Shortest task first which schedules the smaller tasks first.
And priority, which is a generalized case of the shortest task first scheduling
where each task is scheduled in the order of the user specified priority.
Also round-robin scheduling algorithm's which are well-matched for
interactive applications where each task is given small shares to run.
And of course this is only, this is only a sliver of the many scheduling algorithm's
that are out there, which I encourage you to look up.
We haven't discussed cloud scheduling yet, and
that's what we're going to discuss in the next lecture in this series.
[MUSIC]
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