comparative study between utility computing and grid computing

8/2/2019 Comparative Study Between Utility Computing and Grid Computing

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Comparative Study

betweenUtility

Computingand

Grid

Computing

Utility computing:

is the packaging of computing resources, such as computation,

storage and services, as a metered service. This model has the

advantage of a low or no initial cost to acquire computer

resources; instead, computational resources are essentially rented.

This repackaging of computing services became the foundation of

the shift to "On Demand" computing, Software as a Service and

Cloud Computing models that further propagated the idea of

computing, application and network as a service.

[1]There was some initial skepticism about such a significant shift.

However, the new model of computing caught and eventually

became mainstream.

IBM, HP and Microsoft were early leaders in the new field of Utility

Computing with their business units and researchers working on

the architecture, payment and development challenges of the new


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computing model. Google, Amazon and others started to take the

lead in 2008, as they established their own utility services for

computing, storage and applications.

Utility Computing can support grid computing which has thecharacteristic of very large computations or a sudden peaks in

demand which are supported via a large number of computers.

"Utility computing" has usually envisioned some form of

virtualization so that the amount of storage or computing power

available is considerably larger than that of a single time-sharing

computer. Multiple servers are used on the "back end" to make this

possible. These might be a dedicated computer cluster specifically

built for the purpose of being rented out, or even an under-utilized

supercomputer. The technique of running a single calculation on

multiple computers is known as distributed computing.

The term "grid computing" is often used to describe a particular

form of distributed computing, where the supporting nodes are

geographically distributed or cross administrative domains. Toprovide utility computing services, a company can "bundle" the

resources of members of the public for sale, who might be paid

with a portion of the revenue from clients.

One model, common among volunteer computing applications, is

for a central server to dispense tasks to participating nodes, on the

behest of approved end-users (in the commercial case, the paying

customers). Another model, sometimes called the Virtual

Organization (VO), is more decentralized, with

organizations buying and selling computing resources as needed

or as they go idle.


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The definition of "utility computing" is sometimes extended to

specialized tasks, such as web services.

HISTORY:

IBM and other mainframe providers conducted this kind of

business in the following two decades, often referred to as time-

sharing, offering computing power and database storage to banks

and other large organizations from their world wide data centers.

To facilitate this business model, mainframe operating systems

evolved to include process control facilities, security, and user

metering. The advent of mini computers changed this business

model, by making computers affordable to almost all companies.

As Intel and AMD increased the power of PC architecture servers

with each new generation of processor, data centers became filled

with thousands of servers.

In the late 90's utility computing re-surfaced. InsynQ , Inc.

launched [on-demand] applications and desktop hosting services

in 1997 using HP equipment. In 1998, HP set up the Utility

Computing Division in Mountain View, CA, assigning former Bell

Labs computer scientists to begin work on a computing power

plant, incorporating multiple utilities to form a software stack.

Services such as "IP billing-on-tap" were marketed. HP introduced

the Utility Data Center in 2001. Sun announced the Sun Cloudservice to consumers in 2000. In December 2005, Alexa launched

Alexa Web Search Platform, a Web search building tool for which

the underlying power is utility computing. Alexa charges users for

storage, utilization, etc. There is space in the market for specific


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industries and applications as well as other niche applications

powered by utility computing. For example, PolyServe Inc. offers a

clustered file system based on commodity server and storage

hardware that creates highly available utility computing

environments for mission-critical applications including Oracle

and Microsoft SQL Server databases, as well as workload

optimized solutions specifically tuned for bulk storage, high-

performance computing, vertical industries such as financial

services, seismic processing, and content serving. The Database

Utility and File Serving Utility enable IT organizations to

independently add servers or storage as needed, retask workloads

to different hardware, and maintain the environment without

disruption.

In spring 2006 3tera announced its AppLogic service and later that

summer Amazon launched Amazon EC2 (Elastic Compute Cloud).

These services allow the operation of general purpose computing

applications. Both are based on Xen virtualization software and the

most commonly used operating system on the virtual computers

is Linux, though Windows and Solaris are supported. Common

uses include web application, SaaS, image rendering and

processing but also general-purpose business applications.

Utility computing merely means "Pay and Use", with regards to

computing power.

Utility Computing Today:

How does utility computing play out in todays storage and

networking marketplace? Depending on who you talk to, utility


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computing might be an IT management approach, a business

strategy or a software/hardware tool. HPs Mark Linesch, VP of

adaptive enterprise programs, put it as well as anybody: Its not

about a big new technology...Its about establishing a tighter, more

dynamic link between the business and its IT infrastructure.

That is because utility computing lives or dies on the integration of

its parts. Utility networks exist today, but true utility computing

requires close coordination between hardware components, the

applications that run on them, and the data management tools

that handle provisioning, storage pooling, and a myriad of tasks

that require wide-scale automation across a utility network. Theutility infrastructure must be able to automatically provision and

deliver resources on demand, while tracking usage for later

chargeback.

Such a level of flexibility and tracking requires management tools

that are currently in their infancy, which explains why not every

company is jumping on the utility bandwagon (basing your

companys IT life on a bunch of relatively untried tools is only for

the very brave or the foolhardy). But the real holdup for utility

computing is that application providers have yet to move en

masse toward UC-ready licensing models. The software licensing

models in particular are currently the barrier to utility pricing

models, says Corey Ferengul, senior vice president at Meta Group.

Ideally, utility computing pricing models would allow customers to

pay by the sip, much as we do with electricity and water. But

software vendors are still predominantly selling their products on a

per-seat or per-CPU basis, regardless of how much or how little an

individual seat or CPU is utilized.


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Like ILM, utility computing is more a strategic approach than a

specific application or suite of applications. The idea behind utility

computing is to provide unlimited computing power and storage

capacity that can be used and reallocated for any application

and billed on a pay-per-use basis.

Ideally, utility computing brings some important benefits with it.

These include:

Simplified administration. Reduces time-consuming and complex

administration overhead. This will happen faster when going to a

reliable SP model, but internal deployment will yield the same

benefits. Utility computing also needs scalable, standardized, and

heterogeneous computing resources, and should not depend on

highly proprietary hardware or software to work.

Capacity to meet business needs. Enables administrators to

manage fast growth and peaks-and-valleys capacity and

processing demands. Avoids network downtime and lag by

immediately provisioning for changing needs.

Cost-effective. Leverages infrastructure costs to meet changing

business requirements, serves business growth. Automated

provisioning based on need yields excellent ROI on internal

resources.


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Basic requirements for successful utility computing

Automating costing procedures for computing resources. Billing orchargeback information should be driven by the capacity required

to support business processes. As a result of properly aligning

infrastructure with business processes, the business wants IT to

help minimize the costs of providing business services. Note that

this sounds good on paper but can lead to heavy political

infighting: Many business units hate chargeback because it adds

costs to their bottom line. But in the face of spiraling IT costs - all

of which are coming out of their budget - CIOs are increasingly

unsympathetic.

Automated provisioning to meet the business units scaled-up or

scaled-down needs. Without automated provisioning, IT

departments have to resort to painful manual techniques to deal

with impossibly complex server farms, a plethora of operating

systems, multiplying storage systems and expensive management

software. The better automated provisioning technology gets, the

easier this critical piece of utility computing will become.


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Virtualization. Virtualization is an underlying technology that

makes it possible to quickly ready storage for incoming

applications. Virtualization actually ranges from a visual screen

where administrators can make changes to their storage

assignments, up to automatic provisioning where the software

does it for you.

Other types of automation . Discovery

.

Automatically identify

storage networking devices hosts, storage, etc. Be able to apply

them to specific business processes. Provisioning

.

This is the big

one. Automation should work to allocate computing power and

storage room to shifting workloads. It should also know how toapply various settings like user authentication and security

policies to various types of data and originating applications.

Configuration

.

Automatically implements network settings across

environments like system configurations, security settings and

storage definitions. Self-

healing.

Automate problem detection and

subsequent correction or recovery.

Flexible systems. Virtualization and automatic provisioning will

have to work across operating systems and switches, and in

multi-vendor environments. And yes, this is a tall order.

Security. If you thought security was tough in a regular network

environment, try a utility computing network that is serving

hundreds or thousands of customers. A case in point is the recent

denial-of-service attack that Sun suffered on the very first day that

the company allowed users to buy Internet access to its much-

hyped, and much delayed, public utility grid.


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Grid computing and SOA. Grid computing is a form of distributed

computing where resources are often spread across different

physical locations and domains. Grid computing is a foundation

technology for models like utility computing, where computing

resources are pay-per-use commodities. SOA (Service-Oriented

Architecture) is a computing architecture that undergirds the act of

delivering IT as a service. SOA can be used for designing, building,

and managing distributed computing environments, works well

best with standards-based computing resources, and efficiently

enables utility computing infrastructure development.

Utility Computing and SMB :

Amazingly enough, utility computing might not be purely a matter

for the enterprise. IT can be as complex for SMB to manage as for

the enterprise. SMB commonly lacks internal IT skills to optimize

their network infrastructure, and can benefit from a solidly hosted,

reliable and high-performance model. (Internally deploying a utility

computing infrastructure runs into exactly the same challenges

driving SMB to utility computing in the first place. At this point,

most SMBs adopting utility computing will outsource to an SP.)

There are differences between SMB and the enterprise utility

computing models, particularly the lack of a chargeback model in

SMB. According to strategic consultancy THINK strategies, SMBs

utility computing SPs depend primarily on network and

performance management tools, software distribution tools, and

software diagnostic tools to serve their SMB clients.


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Network Management tools to proactively monitor hardware

states

Performance Management tools to effectively measure network,

system, and software performance

Software distribution tools to automatically update operating

systems and applications from a central console

Software diagnostic tools to perform system and software

analyses, and self-healing techniques

Predictions

I expect utility computing to dovetail with developments in grid

computing, SOA, automated provisioning and discovery, security,

and other foundational technologies. Over time, storage,

databases and applications will increasingly be made available for

customers to access on demand over networks that appear as one

large virtual computing system. Utility computing provides the

enterprise with a charge-back function to support this business

model. SMB will increasingly turn to its own brand of utility

computing, where they turn over network management to an SP.

Utility computing is ultimately about how companies can make

better use of all their computing resources. By delivering fast and

intelligent access to network resources, utility computing

leverages computing infrastructure costs and reduces

management overhead.

A Quick

DefinitionUtility computing is a model in which each IT resource is treated

as a unit of capacity that is delivered when and where it is needed.


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Resources are shared across applications. Servers and their

associated resources are aggregated into pools and allocated to

applications as needed. When an application needs more

resources -perhaps because of a component failure or a spike in

demand -allocation occurs immediately. When demand subsides,

resources are returned to the shared pools.

Technologies that Come into Play

Server virtualization and systems-based automation are two key

technologies for enabling utility computing. Server virtualization is

the running of multiple server images -called virtual servers (ormachines) -on a single physical host server. To the environment,

virtual servers look just like separate physical servers. Physical

servers can run different operating systems and applications

concurrently in isolated virtual machines and can host virtualized

high-speed network connections between virtual servers. This

technology reduces the number of physical servers and

associated hardware components you need because virtualservers share network and storage devices.

Data center optimization solutions help you plan, build, operate

and manage your utility computing infrastructure. The solutions

simplify management, increase efficiency and reduce costs by

providing:

Automatic discovery,

Modeling of IT infrastructure,

Availability and performance monitoring and management,

Real-time capacity management,


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Automatic provisioning of resources, and

A single source of resource reference across all IT disciplines.

Implementing Utility Computing

Implementing a utility computing environment involves four steps:

Discovery,

Analysis and planning (modeling),

Implementation, and

Optimization.

The first step is to discover all assets in your IT infrastructure and

populate a configuration management database (CMDB) with

information about those assets. Information includes IT resources,

their configurations and their users. This information helps you

determine the relationships among the resources and the services

they support. Systems-based solutions come into play here by

populating the CMDB and maintaining its accuracy. Because the

infrastructure changes constantly as you add, update and replace

components, automated discovery must run on a regular basis to

keep the CMDB up to date.

In step 2, analysis and planning, you model the target environment

to create a workload or application perspective of resource

utilization. Modeling takes the guesswork out of physical serversizing and helps you achieve maximum resource utilization. You

interact with the model and vary parameters until you understand:


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How many physical systems you can move onto a single physical

host server as virtual machines,

How big the physical host server must be,

The right mix of virtual machines on each physical host server,

How future growth will affect capacity requirements, and

The current resource demand cycles relative to the business

services.

The output of this step is a comprehensive hardware resource plan

-knowledge of the specific hardware configurations needed tosustain the virtual environment.

Step 3 involves moving physical server workloads to virtual

servers. You'll be making a number of potentially complex changes

to the overall IT environment. To minimize risk, you need to

encapsulate all changes within a broader change and

configuration management process that ensures that only planned

changes are authorized, only authorized changes are initiated, and

that changes are implemented as planned and authorized. It's a

good idea to make the move incrementally, starting with

workloads that have the greatest potential for improvement. It's

also a good idea to work in a test environment first.

Automation during this phase helps bring accuracy, repeatability

and scalability of the entire project. To ensure accuracy andcompliance of the resulting total configuration, you need to build a

Definitive Software Library (DSL) that defines specific software

configurations, such as versions and patch levels required for all

the hardware configurations specified in the resource plan.


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In the fourth step - optimization - you implement the utility

computing environment, which involves automating the sharing

and provisioning of physical and virtual resources and

applications. This orchestration is based on resource allocation

policies that you have established. To ensure service levels, it's

important to provide sufficient time to act when implementing

real-time resource allocation. Traditional change management

relies on manual steps and change approval boards, so it is critical

to establish the automation policies and subject them to

comprehensive, closed-loop change and configuration

management processes prior to implementing real-time resource

allocation strategies. This approach ensures that only planned and

authorized allocation decisions are made in real time.

Virtualization technologies help you put an end to overprovisioning

while still delivering high availability and fast performance.

Moreover, they allow you to take full advantage of utility

computing - so you can optimally match resource capacity with

business requirements through real-time capacity management.

Virtualization and utility computing position you to meet the

demands of business users for a continual stream of new and

more advanced business services. Bottom line: You can adapt to

changing business requirements while continuing to deliver high-

quality business services at the lowest possible cost. BMC

Software offers solutions that can proactively and effectively

manage virtual server environments while minimizing the risks in

deploying virtual servers


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Utility Computing Advantages and

Disadvantages:For most clients, the biggest advantage of utility computing is

convenience. The client doesn't have to buy all the hardware,

software and licenses needed to do business. Instead, the client

relies on another party to provide these services. The burden of

maintaining and administering the system falls to the utility

computing company, allowing the client to concentrate on other

tasks.

Closely related to convenience is compatibility. In a large company

with many departments, problems can arise with computingsoftware. Each department might depend on different software

suites. The files used by employees in one part of a company

might be incompatible with the software used by employees in

another part. Utility computing gives companies the option to

subscribe to a single service and use the same suite of software

throughout the entire client organization.

Cost can be either an advantage or disadvantage, depending on

how the provider structures fees. Using a utility computing

company for services can be less expensive than running

computer operations in-house. As long as the utility computing

company offers the client the services it needs to do business,

there's no need for the client to look elsewhere. Most of the cost

for maintenance becomes the responsibility of the provider, not

the client. The client can choose to rely on simplified hardware,

which is less expensive and can be easier to maintain.

However, in some cases what the client needs and what the

provider offers aren't in alignment. If the client is a small business


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and the provider offers access to expensive supercomputers at a

hefty fee, there's a good chance the client will choose to handle its

own computing needs. Why pay a high service charge for

something you don't need?

Another potential disadvantage is reliability. If a utility computing

company is in financial trouble or has frequent equipment

problems, clients could get cut off from the services for which

they're paying. This spells trouble for both the provider and the

client. If a utility computing company goes out of business, its

clients could fall victim to the same fate. Clients might hesitate to

hand over duties to a smaller company if it could mean losing dataand other capabilities should the business suffer.

Utility computing systems can also be attractive targets for

hackers. A hacker might want to access services without paying

for them or snoop around and investigate client files. Much of the

responsibility of keeping the system safe falls to the provider, but

some of it also relies on the client's practices. If a company

doesn't educate its workforce on proper access procedures, it's

not hard for an intruder to find ways to invade a utility computing

company's system.

One challenge facing utility computing services is educating

consumers about the service. Awareness of utility computing isn't

very widespread. It's hard to sell a service to a client if the client

has never heard of it. Now that you've read this article, you're

ahead of the game.

As utility computing companies offer more comprehensive and

sophisticated services, we may see more corporations choosing to


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use their services. Eventually, it's possible that computers in data

centers miles from your home or office will handle all your

computational needs for you.

A Utility software is actually a kind of computer software which is

designed to help in management and tuning of computer

hardware, operating system and application software. It performs

a single task or a number of small tasks. The examples of Utility

software are as follows:

- Disk defragmenters

- System Profilers

- Virus scanners

- Application launchers

- network managers

- Encryption utilities.


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Grid Computing :

is a form of distributed computing that involves coordinating

and controlled sharing of diverse computing, applications, data,

storage, or network resources across dynamic and

geographically dispersed multi-institutional virtualorganizations.

A user of Grid computing does not need to have the data and

the software on the same computer, and neither must be on the

users home (login) computer.

Background of Grid Computing :

The idea of Grid computing resulted from the confluence of

three developments:

The proliferation of largely unused computing resources

(especially desktop computers)

Their greatly increased cpu speed in recent years

The widespread availability of fast, universal network

connections (the Internet).

Need for Grid Computing:1. The proliferation of largely unused computing resources(especially desktop computers, of which 152 million were

sold in 2003).

2. Their greatly increased CPU speed in recent years (now >3

GHz).

3. The widespread availability of fast, universal network

connections (the Internet).

4. High performance computers (formerly calledsupercomputers) are very expensive to buy and maintain.

5. Much of the enhancement of computing power recently

has come through the application of multiple CPUs to a


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problem (e.g., NCSC had a 720 processor IBM parallel

computer).

6. Many computing tasks relegated to these (especially

massively parallel) computers could be performed by a

divide and conquer strategy using many more, althoughslower, processors as are available on a Grid.

Evolution of Grid :

1. Custom solutions (early 90s)

Metacomputing explorative work

Applications built directly on Internet protocols (TCP/IP)

Limited functionality, security, scalability, and robustness.

2. Open Grid Services Architecture (OGSA) (2002)

Community standard with multiple implementations

Globus GT3 implementation

Service-oriented architecture based on XML Web services.


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The Globus Toolkit 4 :

The Globus Toolkit of the Globus-Alliance is a Middleware for Grid

Systems. Although it is still in development, almost all major grid

projects project are based on this toolkit, so even Instant-Grid

does. It is therefore shortly described as a referenceimplementation of the OGSA specification. The Globus project

arose from a collaboration between the University of Chicago and

the University of Southern California, with the participation of IBM

and NASA. It is based among others on the experience of other

projects related to Grid-Technology such as Condor, Codine / Sun

Grid Engine, Legion, Nimrod and Unicore. The GT4 offers all

necessary components for the implementation of Grid systems.

This includes areas of security and data-, resources-, andadministrative tasks. In addition, it provides interfaces and libraries

for popular programming environments.


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Advantages

:

1. No need to buy large six figure SMP servers for applications

that can be split up and farmed out to smaller commoditytype servers. Results can then be concatenated and analyzed

upon job(s) completion.

2. Much more efficient use of idle resources. Jobs can be

farmed out to idle servers or even idle desktops. Many of

these resources sit idle especially during off business hours.

Policies can be in place that allow jobs to only go to servers

that are lightly loaded or have the appropriate amount ofmemory/cpu characteristics for the particular application.

3. Grid environments are much more modular and don't have

single points of failure. If one of the servers/desktops within

the grid fail there are plenty of other resources able to pick

the load. Jobs can automatically restart if a failure occurs.

4. Policies can be managed by the grid software. The software

is really the brains behind the grid. A client will reside on eachserver which send information back to the master telling it

what type of availability or resources it has to complete

incoming jobs.

5. This model scales very well. Need more compute resources?

Just plug them in by installing grid client on additional

desktops or servers. They can be removed just as easily on

the fly. This modular environment really scales well.

6. Upgrading can be done on the fly without scheduling

downtime. Since there are so many resources some can be

taken offline while leaving enough for work to continue. This


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way upgrades can be cascaded as to not effect ongoing

projects.

7. Jobs can be executed in parallel speeding performance. Grid

environments are extremely well suited to run jobs that can

be split into smaller chunks and run concurrently on many

nodes. Using things like MPI will allow message passing to

occur among compute resources.

Disadvantages

:

1. For memory hungry applications that can't take advantage of

MPI you may be forced to run on a large SMP.

2. You may need to have a fast interconnect between compute

resources (gigabit ethernet at a minimum). Infiband for MPI

intense applications

3. Some applications may need to be tweaked to take full

advantage of the new model.

4. Licensing across many servers may make it prohibitive for

some apps. Vendors are starting to be more flexible with

environment like this.

5. Grid environments include many smaller servers across

various administrative domains. Good tools for managing

change and keeping configurations in sync with each other

can be challenging in large environments. Tools exist to

manage such challenges include systemimager, cfengine,

Opsware, Bladelogic, pdsh, cssh, among others.


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6. Political challenges associated with sharing resources

(especially across different admin domains). Many groups

are reluctant with sharing resources even if it benefits

everyone involved. The benefits for all groups need to be

clearly articulated and policies developed that keeps everyonehappy. (easier said than done...)

Areas that already are taking good advantage of grid computing

include bioinformatics, cheminformatics, oil & drilling, and financial

applications.

With the advantages listed above you'll start to see much larger

adoption of Grids which should benefit everyone involved. I believe

the biggest barrier right now is education.

comparative study between utility computing and grid computing

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