CA267 Ray Walshe 2015
Cloud Computing
CA267 Ray Walshe 2015
Computing in the Clouds
•
Summary
Think-Pair-Share
According to Aaron Weiss, what are the different
shapes
the Cloud can take?
What are the
implications
of these different
shapes
?
Computing in the Clouds provides a high-level overview of what
Cloud computing is. It does so, by discussion the different
shapes
the Cloud can appear to users.
Questions
What are some key
terms
that
characterize
the Cloud? commodity,
distributed, elasticity, scalability, rent, out-sourcing
CA267 Ray Walshe 2015
Shapes of Cloud
1. Data Center
:
In this shape, the cloud is an architecture that eschews expensive high-end hardware for many commodity machines.
Utilization is maximized through the use of wide-spread virtualization.
2. Distributed Computing
:
In this shape, the cloud allows us to tackle large-scale problems by utilizing hundreds to thousands of machines simultaneously.
Unfortunately, harnessing all these resources is difficult, and we need specialized programming models, frameworks, and tools.
3. Utility Grid
:
In this shape, the cloud is an elastic and scalable resource pool. There are trade-offs involved with this arrangement.
4. Software as a Service
:
In this shape, the cloud is a way of delivering software and services over the internet. Rather than storing data locally, users store their data on the cloud service.
CA267 Ray Walshe 2015
Data Center
Large centralized computer systems are not new.
In the beginning there was the mainframe. Eventually, this gave away to the mini-computer, and then the micro-computer (aka personal computer).
"I think there's a world market for maybe five computers." -- Thomas Watsom, IBM Chairman 1943
Pictures
IBM System 360 ,, DEC PDP-11 ,, Apple II
According to the article, networking and the growing power of data centers has lead us back to the idea of
centralized computing.
Questions
What economic and technological factors lead to decentralized computing?
What economic and technological factors are leading us back to centralized computing?
Computing technology often goes in cycles (what is old is new again). That is what is acceptable for one era is deprecated in the next and then acceptable again.
Questions
What are some other examples of cyclic technology trends in computer science? garbage collection, out-of-order execution, interpreted languages, portability.
What factors influence these shifts?
CA267 Ray Walshe 2015
Modern Data Centre
•
Today's data centers are
unlike
yesterday's mainframes or data centers:
•
Old:
Expensive high-performance enterprise hardware.
• Vertical Scaling: scale up by improving the performance of a single node.
•
New:
Lots of inexpensive commodity hardware.
• Horizontal Scaling: scale out by adding more machines.
•
New data center involves lots of machines (upwards of a million) working
together. This poses
challenges
:
• Connection and hardware failures common.
• Because of large numbers of machines, energy costs are high (50% of data center costs, 1.5% of world electricity)
•
Pictures
•
Google Data Center
CA267 Ray Walshe 2015
Virtualization
•
New data centre relies heavily
virtualization
to maximize utilization
• A virtual machine provides a virtual hardware interface to the operating system such that the guest OS interacts with virtual devices rather than physical ones.
• VMs provide sandboxing, that is isolation, from other VMs on the same hardware. Each VM is an independent machine with its own copy of the OS.
• By executing many VMs on one machine, adminstrators can improve utilization since most VMs are generally idle.
• Data center requires some coordination layer to manage resources.
•
Distributed Computing
•
Questions
•
What is distributed computing?
•
What are some examples of distributed computing?
CA267 Ray Walshe 2015
Distributed computing
• Many autonomous and independent machines
working together to accomplish a common goal.
•
Coordinating and mapping tasks to system resources
is a complex problem.
•
Developing algorithms and implementing applications
that execute on such distributed systems is difficult
(See:
Eight Fallacies of Distributed Computing
).
•
To tackle these problems we need new systems:
•
HDFS: distributed storage
CA267 Ray Walshe 2015
Utility Grid
•
Building your own data centre
is costly: real estate, hardware,
power, cooling, and maintenance
.
•
What if you only need 99% of the computing capacity 10% of the
time -> lots of u
nderutilization
.
•
Amazon faced this problem, so they decided to rent their excess
capacity to third-parties ->
Amazon EC2
.
•
With Amazon EC2, websites such as
Reddit, Dropbox, and
Netflix
can utilize the resources provided by Amazon to scale up
and down as required and alleviate the need to maintain their own
infrastructure.
•
The cloud allows companies to rent and utilize computing
resources on-demand, that is, only when they need them:
•
Elasticity
: resources grow and shrink as demand requires
CA267 Ray Walshe 2015
Software as a Service
•
Rather than having a local application, users utilize remote software (i.e. web
sites) as if they were services.
•
Put most of the heavy computation on remote servers and consume the content
on local light-weight devices.
•
"The network is the computer." -- John Gage, Sun Microsystems.
Questions
• What are the advantages and disadvantages of this model?
• Is this model feasible? Is this model inevitable?
Obstacles
Cloud is here and likely to stay, but issues remain:
1. Network: US market not really ready to handle network load.
2. Privacy: Can we trust Cloud providers to trust our data?
3. Lock-in: There are no standards for interoperability between Cloud vendors.
