Cloud Computing Dr. Elise de Doncker CS6260 Yazeed K. Almarshoud.

Cloud Cloud ComputingComputing

Dr. Elise de Doncker CS6260

Yazeed K. Almarshoud

RoadmapRoadmap Introduction Parallel vs. Distributed

Grid computing structure Flynn’s Taxonomy

Cloud vs. Grid Cloud Computing

Possibilities Some Characteristics of Cloud Computing SaaS and Cloud Computing Supercomputing & Cloud Computing

Clouds Examples Conclusions References

During the good economic times, enterprises do huge investment in Information Technology (IT) infrastructure to achieve faster and reliable response to users’ queries.

The concept of parallel computing & distributing systems widely used and enhanced in many related environments (.i.e Grids)

What is exactly the difference when we say Parallel or Distributed?

IntroductionIntroduction

Parallel vs. DistributedParallel vs. Distributed

Parallel computing generally means: Vector processing of data Multiple CPUs in a single computer

Distributed computing generally means: Multiple CPUs across many computers

Flynn’s Taxonomy

InstructionsSingle (SI) Multiple (MI)

Data

Mu

ltip

le

(MD

)SISD

Single-threaded process

MISDPipeline

architecture

SIMDVector

Processing

MIMDMulti-

threaded Programmin

g

Sin

gle

(S

D)

SISDSISD

D D D D D D D

Processor

Instructions

SIMDSIMD

D0

Processor

Instructions

D0D0 D0 D0 D0

D1

D2

D3

D4

…

Dn

D1

D2

D3

D4

…

Dn

D1

D2

D3

D4

…

Dn

D1

D2

D3

D4

…

Dn

D1

D2

D3

D4

…

Dn

D1

D2

D3

D4

…

Dn

D1

D2

D3

D4

…

Dn

D0

MIMDMIMD

D D D D D D D

Processor

Instructions

D D D D D D D

Processor

Instructions

Parallel vs. DistributedParallel vs. Distributed

SharedMemory

Parallel: Multiple CPUs within a shared memory machine

Distributed: Multiple machines with own memory connected over a network

Ne

two

rk c

on

ne

ctio

nfo

r d

ata

tra

nsf

er

D D D D D D D

Processor

Instructions

D D D D D D D

Processor

Instructions

Divide and ConquerDivide and Conquer

“Work”

w1 w2 w3

r1 r2 r3

“Result”

“worker” “worker” “worker”

Partition

Combine

Grid Computing Structure (big picture)

Cloud computingCloud computing

“Cloud computing is a computing paradigm shift where computing is moved away from personal computers or an individual application server to a “cloud” of computers. Users of the cloud only need to be concerned with the computing service being asked for, as the underlying details of how it is achieved are hidden. This method of distributed computing is done through pooling all computer resources together and being managed by software rather than a human.“

Cloud vs. GridCloud vs. Grid

Cloud Computing is an infrastructure that virtualizes hardware and software resources

Grid Computing are patterns, tools and frameworks to distribute computing or data

A cloud can be the platform to run a computing or data grid

Cloud Computing Cloud Computing

Cloud computing is a novel platform for computing and storage.

Cloud computing provisions and configures servers as needed.

It allows for more efficient use of the enterprise resources and applications.

It introduces accountability and streamlines computing needs of an enterprise.

PossibilitiesPossibilities It is possible to consolidate all the needs of an

organization in a systematic and accountable fashion. It is possible to procure computing related resources

similar to how you rent a place for living. For example,

you can buy storage on demand from amazon.com in a service it offers called the “S3”

You can buy computation service from amazon.com in its “elastic cloud computing” service (EC2)

Usage example: You are in charge of IT in a local company. You have an immediate need for backing up entire set up for a short period of time as a mock up for disaster recovery. What would you do?

What is driving Cloud ComputingWhat is driving Cloud Computing

Fast growth of connected mobile devices

Skyrocketing costsof power, space,

maintenance, etc.

Advances in multi-corecomputer architecture

Explosion of data intensive applications

on the Internet

Growth of Web 2.0-enabled PCs, TVs,

etc.

• Technology advances that support massive scalability & accessibility

• Emergence of data intensive applications & new types of workloads Large scale information processing, i.e. parallel computing using HadoopWeb 2.0 rich media interactionsLight weight run anywhere web apps

Industry Trends Leading to Industry Trends Leading to Cloud Computing Cloud Computing

Grid Computing

Solving large problems with parallel computing

Made mainstream by Globus Alliance

Software as a Service

• Network-based subscriptions to applications

• Gained momentum in 2001

Cloud Computing

• Next-Generation Internet computing

• Next-Generation Data Centers

19901998

20002008

Utility Computing

Offering computing resources as a metered service

Introduced in late 1990s

Some Characteristics of Cloud Some Characteristics of Cloud ComputingComputing

Virtual – Physical location and underlying infrastructure details are transparent to users

Scalable – Able to break complex workloads into pieces to be served across an incrementally expandable infrastructure

Efficient – Services Oriented Architecture for dynamic provisioning of shared compute resources

Flexible – Can serve a variety of workload types – both consumer and commercial

Cloud Computing Management Services

Cloud Computing in the New Enterprise Data CenterCloud Computing in the New Enterprise Data Center

WorkloadManagement

Provisioning Monitoring

Virtualized PhysicalServers

(Ensembles)

iDataPlex, BladeCenter, System x, System p, System z

Software Development

Deploys development

tools for immediate use

Technology Incubation

Reduces time to launch new

offerings

Innovation Enablement

Expands sources of innovation, increases

competitiveness

Large Scale Information Processing

Optimizes emerging

Internet scale workloads

Self-serviceAdmin Portal

Workload PatternTemplates

SLA andCapacity Planning

Administration Workflows

Workload Solution Patterns

Why Cloud Computing? Why Cloud Computing?

Pay per use Instant Scalability Security Reliability APIs

[An Introduction to SaaS and Cloud Computing presentation By Ross Cooney]

Case Study of a Cloud DeploymentCase Study of a Cloud Deployment

Current IT

Spend

StrategicChange Capacity

Hardware, labor & power savings reduced annual cost of operation by 83.8%Hardware Costs

( - 88.7%)

Labor Costs ( - 80.7%)

100%

Deployment (1-time)

Note: 3-Year Depreciation Period with 10% Discount Rate

Hardware Costs

(annualized)

New Development

Liberated funding for new development,

transformation investment or direct saving

Labor Costs (Operations and

Maintenance)

Power Costs(88.8%)

Power Costs

Software Costs

Software Costs

““Cloud Computing” Defined “as a Cloud Computing” Defined “as a Service” typesService” types


Everything as a service (EaaS or XaaS)

Communication as a service (CaaS) Infrastructure as a service (IaaS) Monitoring as a service (MaaS) Software as a service (SaaS – includes

Application Service Provider (ASP) services)

Platform as a service (PaaS)

IaaSInfrastructure as a Service

PaaSPlatform as a Service

SaaSSoftware as a Service




Software delivery model

Increasingly popular with SMEs

No hardware or software to manage

Service delivered through a browser

SaaS


Advantages


SaaS


Examples CRM Financial Planning Human Resources Word processing

Commercial Services: Salesforce.com emailcloud

SaaS




Platform delivery model

Platforms are built upon Infrastructure, which is expensive

Estimating demand is not a science!

Platform management is not fun!

PaaS


Popular services

Storage Database Scalability

PaaS


Advantages


PaaS


Examples

Google App Engine Mosso AWS: S3

PaaS




Computer infrastructure delivery model

Access to infrastructure stack: Full OS access Firewalls Routers Load balancing

IaaS


Advantages


IaaS


Examples

Flexiscale AWS: EC2

IaaS






Common Factors


IaaS

PaaS

SaaS


Advantages

Lower cost of ownership Reduce infrastructure

management responsibility Allow for unexpected

resource loads Faster application rolloutIaaS

PaaS

SaaS


Cloud Economics

Multi-tenented Virtualisation lowers costs

by increasing utilisation Economies of scale afforded

by technology Automated update policyIaaS

PaaS

SaaS


Supercomputing & Cloud Supercomputing & Cloud ComputingComputing

Two macro strategies dominate large-scale (intentional) computing infrastructures

Supercomputing type Structures Large-scale integrated coherent systems Managed for high utilization and efficiency

Emerging cloud type Structures Large-scale loosely coupled, lightly integrated Managed for availability, throughput, reliability

How should we think about How should we think about the cloud opportunities?the cloud opportunities? Virtual zoo of systems? Replacements for Clusters? Extensions to existing systems

and infrastructure? Surge capacity? Edge datasystems?

Opportunity to go “hardwareless” when designing new systems and services?

The Virtual ZooThe Virtual Zoo

Access to a diverse image library provides an inexpensive mechanism to test applications and services on a variety of OS configurations without having to build all of them. Leverages virtualization and

community images Leverages “cloud” when scale is

important Using cloud for scalability testing

could be interesting when you have servers you want to stress and test, but limited time and resources Creating hundreds of running instances

is relatively easy and could be done by a few people in less than a day

Automation of the scalability testing could be easily accomplished

As Replacements for As Replacements for Clusters?Clusters? There have been several experiments creating virtual

clusters in EC2 and probably in other environments as well [Peter Skomoroch, et al].

These “soft” clusters are interesting, constructed on demand and then torn down with the application run is complete.

It might be possible to integrate virtual clusters into existing Linux cluster queues such that jobs that are queued for a physical cluster could be dispatched to a local cluster or a cloud based virtual cluster for execution. In fact for throughput jobs this might be even more

effective. Local facilities that start supporting image based

scheduling services would lead in this transition (i.e. you submit your job as one or more images rather than scripts or executables)

Cloud hosting for clusters provides one easy way to implement cycle banking since each application determines their own operation environment and overheads are relatively low This would ideally be implemented as a distributed

resource if physical ownership was important Virtual ownership would make it much easier and

robust to implement

Seamless extensionsSeamless extensions

Like in the previous example seamlessly extending an existing queue could be a one way to integrate clouds with existing services and systems.

But we can imagine others. How about using the cloud as

a giant impedance matcher for geographically distributed systems of large-scale sensors and tightly coupled data analysis environments?

The idea is simple.


Surge CapacitySurge Capacity

Power companies have peakers. Typically natural gas powered turbines

used during times of peak demand for power.

Clouds can be used for surge capacity for groups that have variable demands for access to compute cycles or server/service cycles

Sensor + Cloud + Sensor + Cloud + Supercomputer = Next Supercomputer = Next Generation Simulations Generation Simulations Imagine thousands (or millions)

of distributed sensors deployed over the globe each generating data in some asynchronous fashion.

Each sensor updates data structures in the cloud via local internet connections. The cloud is ubiquitous, secure enough, reliable etc. and scales to the size of the sensor network and acts as an impedance matcher.

Periodically harvesting processes (in the cloud say) wake up and organize the datasets into a fashion that they can be downloaded coherently to a supercomputer for data assimilation to a large-scale parallel simulation.

Going HardwarelessGoing Hardwareless

Need: 24x7 access to flexibly configured hardware, scalable data infrastructure, and customized operating environment

1000 cores x .10 hour x 8760 hours/year x 3 years = $2.6M

1000 cores x $390/core + 3 x $43,800 power + 3 x 200K + 3 x 100K = $1.4M

In my example if cluster utilization is < 53% then it is cheaper to go “hardwareless” at current retail prices


Clouds ExamplesClouds Examples

Amazon.comAmazon.com Amazon Simple Storage Service (Amazon S3) . Amazon Elastic Compute Cloud (Amazon EC2)

Hadoop (Map/Reduce) Large scale information processing, i.e.

parallel computing

ConclusionsConclusions

The emerging concept of the cloud is pretty cool. The existing available “retail” models are hugely

empowering, since they require only a credit card to get going.

Ease of use is being tackled, a market is developing for images and value added services.

Clouds feel like the next thing that will have traction and will enable hardwareless ventures.

Scientific applications will not drive clouds, but will benefit from their widespread adoption.

It is a disruptive technology in many ways and the university/agency shift will take some time, hence private sector will likely get significantly ahead.

Many groups should be experimenting and it really is pretty cheap to gain the critical experience to figure out interesting things to try.

ReferencesReferences http://en.wikipedia.org/wiki/Cloud_computing

Includes references to Amazon, Apple, Dell, Enomalism, Globus, Google, IBM, KnowledgeTreeLive, Nature, New York Times, Zimdesk

Others like Microsoft Windows Live Skydrive important An Introduction to SaaS and Cloud Computing presentation By Ross Cooney

http://en.wikipedia.org/wiki/Amazon_Elastic_Compute_Cloud http://uc.princeton.edu/main/index.php?option=com_content&task=view&

id=2589&Itemid=1 Policy Issues

http://www.cra.org/ccc/home.article.bigdata.html Hadoop (MapReduce) and “Data Intensive Computing” See Data intensive computing minitrack at HICSS-42 January 2009

http://ianfoster.typepad.com/blog/2008/01/theres-grid-in.html OGF Thought Leadership blog

OGF22 talks by Charlie Catlett and Irving Wladawsky-Berger

Presentation Question:Presentation Question:

What are the two macro strategies dominate large-What are the two macro strategies dominate large-scale (intentional) computing infrastructures? scale (intentional) computing infrastructures? Explain.Explain.

Supercomputing type StructuresLarge-scale integrated coherent systemsManaged for high utilization and efficiency

Emerging cloud type StructuresLarge-scale loosely coupled, lightly integratedManaged for availability, throughput, reliability

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Cloud Computing Dr. Elise de Doncker CS6260 Yazeed K. Almarshoud.

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