report distributed computing

8/3/2019 Report Distributed Computing

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DISTRIBUTED

COMPUTING

V/sGRID COMPUTING

Management of Software Projects

SUBMITTED BY:

MRIDUL SHARMA F-

AALOK AWASTHI F-

RAJEEV CHATURVEDI F-

AVINASH MISHRA F- 44


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ABSTRACT

There are two similar trends moving in tandem- distributed computing

and grid computing. Depending on how you look at the market, the two either overlap, or

distributed computing is a subset of grid computing. Grid Computing got its name

because it strives for an ideal scenario in which the CPU cycles and storage of millions

of systems across a worldwide network function as aflexible, readily accessible poolthat

could be harnessed by anyone who needs it.

Sun defines a computational grid as "a hardware and software infrastructure that

provides dependable, consistent, pervasive, and inexpensive access to computational

capabilities." Grid computing can encompass desktop PCs, but more often than not its

focus is on more powerful workstations, servers, and even mainframes and

supercomputers working on problems involving huge datasets that can run for days. And

grid computing leans more to dedicated systems, than systems primarily used for other

tasks.

Large-scale distributed computing of the variety we are covering usually refers to

a similar concept, but is more geared to poolingthe resources of hundreds or thousands

of networked end-user PCs, which individually are more limited in their memory and

processing power, and whose primary purpose is not distributed computing, but rather

serving their user.

When going for a new technology its a must for us to asses competing

technologies and compare it with the new trends. In this paper we discuss How

Distributed Computing differs from Grid Computing, the Application Characteristics

that make distributed computing different from grid computing. We also discuss the

areas where these computational technologies can be applied. Security and the standards

behind these technologies have also been discussed.


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DISTRIBUTED COMPUTING

Introduction

The numbers of real applications are still somewhat limited, and the challenges,

particularlystandardization, are still significant. But there's a new energy in the market,

as well as some actual paying customers. Increasing desktop CPU power and

communications bandwidth has also helped to make distributed computing a more

practical idea. Various vendors have developed numerous initiatives and architectures to

permit distributed processing of data and objects across a network of connected systems.

The area of distributed computing has received a lot of attention, and it will be a primary

focus of this paper--an environment where you can harness idle CPU cycles and storage

space of hundreds or thousands of networked systems to work together on a processing-

intensive problem. The growth of such processing models has been limited, however,

due to a lack of compelling applications and by bandwidth bottlenecks, combined with

significantsecurity, management, andstandardization challenges.

How It Works

A distributed computing architecture consists of very lightweight software agents

installed on a number of client systems, and one or more dedicated distributed

computing management servers. There may also be requesting clients with software that

allows them to submit jobs along with lists of their required resources.

An agent running on a processing client detects when the system is idle, notifies

the management server that the system is available for processing, and usually requests

an application package. The client then receives an application package from the server

and runs the software when it has spare CPU cycles, and sends the results back to theserver. If the user of the client system needs to run his own applications at any time,

control is immediately returned, and processing of the distributed application package

ends.


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A Typical Distributed System

Distributed Computing Management Server

The servers have several roles. They take distributed computing requests and

divide their large processing tasks into smaller tasks that can run on individual desktop

systems (though sometimes this is done by a requesting system). They send application

packages and some client management software to the idle client machines that request

them. They monitor the status of the jobs being run by the clients. After the client

machines run those packages, they assemble the results sent back by the client and

structure them for presentation, usually with the help of a database.

The server is also likely to manage any security, policy, or other management

functions as necessary, including handling dialup users whose connections and IP

addresses are inconsistent. Obviously the complexity of a distributed computing

architecture increases with the size and type of environment. A larger environment thatincludes multiple departments, partners, or participants across the Web requires complex

resource identification, policy management, authentication, encryption, and secure

sandboxing functionality.


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Administrators or others with rights can define which jobs and users get access to

which systems, and who gets priority in various situations based on rank, deadlines, and

the perceived importance of each project. Obviously, robust authentication, encryption,

and sandboxingare necessary to prevent unauthorized access to systems and data within

distributed systems that are meant to be inaccessible.

If you take the ideal of a distributed worldwide grid to the extreme, it requires standards

and protocols for dynamic discovery and interaction of resources in diverse network

environments and among different distributed computing architectures. Most distributed

computing solutions also include toolkits, libraries, and API's for porting third party

applications to work with their platform, or for creating distributed computingapplications from scratch.


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Comparisons and Other Trends

Distributed vs. Other Trends


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Application CharacteristicsObviously not all applications are suitable for distributed computing. The closer

an application gets to running in real time, the less appropriate it is. Even processing

tasks that normally take an hour are two may not derive much benefit if the

communications among distributed systems and the constantly changing availability of

processing clients becomes a bottleneck. Instead you should think in terms of tasks that

take hours, days, weeks, and months. Generally the most appropriate applications consist

of "loosely coupled, non-sequential tasks in batch processes with a high compute-to-data

ratio." The high compute to data ratio goes hand-in-hand with a high compute-to-

communications ratio, as you don't want to bog down the network by sending large

amounts of data to each client, though in some cases you can do so during off hours.

Programs with large databases that can be easily parsed for distribution are very

appropriate.

Clearly, any application with individual tasks that need access to huge data sets

will be more appropriate for larger systems than individual PCs. If terabytes of data are

involved, a supercomputer makes sense as communications can take place across the

system's very high speed backplane without bogging down the network. Server and other

dedicated system clusters will be more appropriate for other slightly less data intensive

applications. For a distributed application using numerous PCs, the required data should

fit very comfortably in the PC's memory, with lots of room to spare.

Taking this further, it is recommended that the application should have the

capability to fully exploit "coarse-grained parallelism," meaning it should be possible to

partition the application into independent tasks or processes that can be computed

concurrently. For most solutions there should not be any need for communication

between the tasks except at task boundaries, though Data Synapse allows some

interprocess communications. The tasks and small blocks of data should be such that

they can be processed effectively on a modern PC and report results that, when combined


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with other PC's results, produce coherent output. And the individual tasks should be

small enough to produce a result on these systems within a few hours to a few days

Distributed Computing Applications

The following scenarios are examples of types of application tasks that can be set up

to take advantage of distributed computing.

A query search against a huge database that can be split across lots of desktops,

with the submitted query running concurrently against each fragment on each

desktop.

Complex modelingand simulation techniques that increase the accuracy of results

by increasing the number of random trials would also be appropriate, as trials

could be run concurrently on many desktops, and combined to achieve greater

statistical significance (this is a common method used in various types of

financial risk analysis).

Exhaustive search techniques that require searching through a huge number of

results to find solutions to a problem also make sense. Drug screening is a prime

example.

Complex financial modeling, weather forecasting, and geophysical exploration

are on the radar screens of the vendors, as well as car crash and other complex

simulations.

Many of today's vendors are aiming squarely at the life sciences market, which

has a sudden need for massive computing power. Pharmaceutical firms have

repositories of millions of different molecules and compounds, some of which

may have characteristics that make them appropriate for inhibiting newly found

proteins. The process of matching all these ligands to their appropriate targets is

an ideal task for distributed computing, and the quicker it's done, the quicker and

greater the benefits will be.


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Security and Standards Challenges

The major challenges come with increasing scale. As soon as you move outside

of a corporate firewall, security and standardization challenges become quite significant.

Most of today's vendors currently specialize in applications that stop at the corporate

firewall, though Avaki, in particular, is staking out the global grid territory. Beyond

spanning firewalls with a single platform, lies the challenge of spanning multiple

firewalls and platforms, which means standards.


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Most of the current platforms offer high level encryption such as Triple DES. The

application packages that are sent to PCs are digitally signed, to make sure a rogue

application does not infiltrate a system. Avaki comes with its own PKI (public key

infrastructure). Identical application packages are typically sent to multiple PCs and the

results of each are compared. Any set of results that differs from the rest becomes

security suspect. Even with encryption, data can still be snooped when the process is

running in the client's memory, so most platforms create application data chunks that are

so small, that it is unlikelysnoopingthem will provide useful information. Avaki claims

that it integrates easily with different existing security infrastructures and can facilitate

the communications among them, but this is obviously a challenge for global distributed

computing.

Working out standards for communications among platforms is part of the

typical chaos that occurs early in any relatively new technology. In the generalized peer-

to-peer realm lies the Peer-to-Peer Working Group, started by Intel, which is looking to

devise standards for communications among many different types of peer-to-peer

platforms, including those that are used for edge services and collaboration.

The Global Grid Forum is a collection of about 200 companies looking to devise

grid computing standards. Then you have vendor-specific efforts such as Sun's Open

Source JXTA platform, which provides a collection of protocols and services that allows

peers to advertise themselves to and communicate with each other securely. JXTA has a

lot in common with JINI, but is not Java specific (thought the first version is Java based).

Intel recently released its own peer-to-peer middleware, the Intel Peer-to-Peer

Accelerator Kit for Microsoft .Net, also designed for discovery, and based on the

Microsoft.Net platform.
http://www.peer-to-peerwg.org/http://www.gridforum.org/http://www.gridforum.org/http://www.peer-to-peerwg.org/


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GRID COMPUTING

Introduction

Grid computing, emerging as a new paradigm for next-generation computing, enables the

sharing, selection, and aggregation of geographically distributed heterogeneous resources

for solving large-scale problems in science, engineering, and commerce. The resources

in the Grid are heterogeneous and geographically distributed. Availability, usage and

cost policies vary depending on the particular user, time, priorities and goals. It enables

the regulation of supply and demand for resources.

It provides an incentive for resource owners to participate in the Grid; and

motivates the users to trade-off between deadline, budget, and the required level of

quality of service. The thesis demonstrates the capability of economic-based systems for

wide-area parallel and distributed computing by developing users quality-of-service

requirements-based scheduling strategies, algorithms, and systems. It demonstrates their

effectiveness by performing scheduling experiments on the World-Wide Grid for solving

parameter sweeptask and data parallelapplications.

The Grid unites servers and storage into a single system that acts as a single

computer - all your applications tap into all your computing power. Hardware resources

are fully utilized and spikes in demand are met with ease. This Web site sponsored by

Oracle brings you the resources you need to evaluate your organization's adoption of grid

technologies. The Grid is ready when you are.

The Grid

The Grid is the computing and data management infrastructure that will provide

the electronic underpinning for a global society in business, government, research,

science and entertainment, integrate networking, communication, computation and

information to provide a virtual platform for computation and data management in the

same way that the Internet integrates resources to form a virtual platform for


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information. The Grid is the computing and data management infrastructure that will

provide the electronic. Grid infrastructure will provide us with the ability to dynamically

link together resources as an ensemble to support the execution of large-scale, resource-

intensive, and distributed applications.

Grid is a type of parallel and distributed system that enables the sharing,

selection, and aggregation of geographically distributed "autonomous" resources

dynamically at runtime depending on their availability, capability, performance, cost,

and users' quality-of-service requirements.

Beginning of the Grid

Parallel computing in the 1980s focused researchers efforts on the development

of algorithms, programs and architectures that supported simultaneity. During the 1980s

and 1990s, software for parallel computers focused on providing powerful mechanisms

for managing communication between processors, and development and execution

environments for parallel machines. Successful application paradigms were developed to

leverage the immense potential of shared and distributed memory architectures. Initially

it was thought that the Grid would be most useful in extending parallel computing

paradigms from tightly coupled clusters to geographically distributed systems. However,

in practice, the Grid has been utilized more as a platform for the integration of loosely

coupled applications some components of which might be running in parallel on a low-

latency parallel machine and for linking disparate resources (storage, computation,

visualization, instruments). Coordination and distribution two fundamental concepts in

Grid Computing.

The first modern Grid is generally considered to be the information wide-area

year (IWAY). Developing infrastructure and applications for the I-WAY provided a

seminar and powerful experience for the first generation of modern Grid researchers and

projects. This was important, as the development of Grid research requires a very

different focus than distributed computing research. Grid research focuses on addressing


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the problems of integration and management of software. I-WAY opened the door for

considerable activity in the development of Grid software.

Characteristics

An enterprise-computing grid is characterized by three primary features -

Diversity;

Decentralization

Dynamism

Diversity:

A typical computing grid consists of many hundreds of managed resources of

various kinds including servers, storage, Database Servers, Application Servers,

Enterprise Applications, and system services like Directory Services, Security and

Identity Management Services, and others. Managing these resources and their life cycle

is a complex challenge.

Decentralization:

Traditional distributed systems have typically been managed from a central

administration point. A computing grid further compounds these challenges since the

resources can be even more decentralized and may be geographically distributed across

many different data centers within an enterprise.

Dynamism:

Components of a traditional application typically run in a static environment

without the needing to address rapidly changing demands. In a computing grid, however,

the systems and applications need to be able to flexibly adapt to changing demand. For

instance, with the late binding nature and cross-platform properties of web services, an

application deployed on the grid may consist of a constantly changing set of components.

At different points in time, these components can be hosted on different nodes in the


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network. Managing an application in such a dynamic environment can be a challenging

undertaking.

A Community of Grid Model

Over the last decade, the Grid community has begun to converge on a layered

model that allows development of the complex system of services and software required

to integrate Grid resources. The Community Grid Model (a layered abstraction of thegrid)being developed in a loosely coordinated manner throughout academia and the

commercial sector.

The bottom horizontal layer of the Community Grid Model consists of the

hardware resources that underlie the Grid. Such resources include computers, networks,

data archives, instruments, visualization devices and so on. Moreover, the resource pool

represented by this layer is highly dynamic, both as a result of new resources being

added to the mix and old resources being retired, and as a result of varying observable

performance of the resources in the shared, multi-user environment of the Grid.

Figure 1: Layered architecture of the Community Grid Model.

The next horizontal layer (common infrastructure) consists of the software

services and systems, which virtualized the Grid. The key concept at the common

infrastructure layer is community agreement on software, which will represent the Grid


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as a unified virtual platform and provide the target for more focused software and

applications.

The next horizontal layer (user and application-focused Grid middleware, tools

and services) contains software packages built atop the common infrastructure. This

software serves to enable applications to more productively use Grid resources by

masking some of the complexity involved in system activities such as authentication, file

transfer.

Types Of Grid

Grid computing can be used in a variety of ways to address various kinds of application

requirements. Often, grids

are categorized by the type of solutions that they best address. The three primary types of

grids are

Computational grid

A computational grid is focused on setting aside resources specifically for computing

power. In this type of grid, most of the mchines are high-performance servers.

Scavenging grid

A scavenging grid is most commonly used with large numbers of desktop machines.

Machines are scavenged for available CPU cycles and other resources. Owners of the

desktop machines are usually given control over when their resources are available to

participate in the grid.

Data grid

A data grid is responsible for housing and providing access to data across multiple

organizations. Users are not concerned with where this data is located as long as they

have access to the data. For example, you may have two universities doing life science


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research, each with unique data. A data grid would allow them to share their data,

manage the data, and manage security issues such as who has access to what data.

Another common distributed computing model that is often associated with or

confused with Grid computing is peer-to-peer computing. In fact, some consider this is

another form of Grid computing.

Grid Tools

Infrastructure components include file systems, schedulers and resource

managers, messaging systems, security applications, certificate authorities, and filetransfer mechanisms like Grid FTP.

Directory services. Systems on a grid must be capable of discovering what

services are available to them. In short, Grid systems must be able to define (and

monitor) a grids topology in order to share and collaborate. Many Grid directory

services implementations are based on past successful models, such as LDAP,

DNS, network management protocols, and indexing services.

Schedulers and load balancers. One of the main benefits of a grid is maximizing

efficiency. Schedulers and load balancers provide this function and more.

Schedulers ensure that jobs are completed in some order (priority, deadline,

urgency, for instance) and load balancers distribute tasks and data management

across systems to decrease the chance of bottlenecks.

Developer tools. Every arena of computing endeavor requires tools that allow

developers to succeed. Tools for grid developers focus on different niches (file

transfer, communications, environment control), and range from utilities to full-

blown APIs.

Security. Security in a grid environment can mean authentication and

authorization -- in other words, controlling who/what can access a grids

resources -- but it can mean a lot more. For instance, message integrity and

message confidentiality are crucial to financial and healthcare environments


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Grid Components

Depending on the grid design and its expected use, some of these components may or

may not be required, and in some cases they may be combined to form a hybrid

component.

Portal/user interface

Just as a consumer sees the power grid as a receptacle in the wall, a grid user

should not see all of the complexities of the computing grid.

Although the user interface can come in many forms and be application-specific. A grid

portal provides the interface for a user to launch applications that will use the resources

and services provided by the grid. From this perspective, the user sees the grid as a

virtual computing resource just as the consumer of power sees the receptacle as an.

interface to a virtual generator.


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Figure 2: Possible user view of a grid

Security

A major requirement for Grid computing is security. At the base of any grid

environment, there must be mechanisms to provide security, including authentication,

authorization, data encryption, and so on. The Grid Security Infrastructure (GSI)

component of the Globus Toolkit provides robust security mechanisms. The GSI

includes an OpenSSL implementation. It also provides a single sign-on mechanism, so

that once a user is authen

authenticated, a proxy certificate is created and used when performing actions within the

grid. When designing your grid environment, you may use the GSI sign-in to grant

access to the portal, or you may have your own security for the portal. The portal will

then be responsible for signing in to the grid, either using the user's credentials or using a

generic set of credentials for all authorized users of the portal.


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Once the resources have been identified, the next logical step is to schedule the

individual jobs to run on them. If sets of stand-alone jobs are to be executed with no

interdependencies, then a specialized scheduler may not be required. However, if you

want to reserve a specific resource or ensure that different jobs within the application run

concurrently (for instance, if they require inter-process communication), then a job

scheduler should be used to coordinate the execution of the jobs. The Globus Toolkit

does not in include such a scheduler, but there are several schedulers available that have

been tested with and can be used in a Globus grid environment. It should also be noted

that there could be different levels of schedulers within a grid environment. For instance,

a cluster could be represented as a single resource. The cluster may have its own

scheduler to help manage the nodes it contains. A higher-level scheduler (sometimes

called a meta scheduler) might be used to schedule work to be done on a cluster, while

the cluster's scheduler would handle the actual scheduling of work on the cluster's

individual nodes.

Figure 5: Scheduler

Data management

If any data -- including application modules -- must be moved or made accessible to

the nodes where an application's jobs will execute, then there needs to be a secure and

reliable method for moving files and data to various nodes within the grid. The Globus


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Toolkit contains a data management component that provides such services. This

component, know as Grid Access to Secondary Storage (GASS), includes facilities such

as GridFTP. GridFTP is built on top of the standard FTP protocol, but adds additional

functions and utilizes the GSI for user authentication and authorization. Therefore, once

a user has an authenticated proxy certificate, he can use the GridFTP facility to move

files without having to go through a login process to every node involved. This facility

provides third-party file transfer so that one node can initiate a file transfer between two

other nodes.

Figure 6: Data management

Job and resource management

With all the other facilities we have just discussed in place, we now get to the

core set of services that help perform actual work in a grid environment. The Grid

Resource Allocation Manager (GRAM) provides the services to actually launch a job on

a particular resource, check its status, and retrieve its results when it is complete.


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Figure 7: Gram

Job flow in a grid environment

Enabling an application for a grid environment, it is important to keep in mind

these components and how they relate and interact with one another. Depending on your

grid implementation and application requirements, there are many ways in which these

pieces can be put together to create a solution.

Advantages

Grid computing is about getting computers to work together. Almost every organization

is sitting on top of enormous, unused computing capacity, widely distributed.

Mainframes are idle 40% of the time With Grid computing, businesses can optimize

computing and data resources, pool them for large capacity workloads, share them acrossnetworks, and enable collaboration. Many consider Grid computing the next logical step

in the evolution of the Internet, and maturing standards and a drop in the cost of

bandwidth are fueling the momentum we're experiencing today.

Challenges of the Grid


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A word of caution should be given to the overly enthusiastic. The grid is not a silver

bullet that can take any application and run it a 1000 times faster without the need for

buying any more machines or software. Not every application is suitable or enabled for

running on a grid. Some kinds of applications simply cannot be parallelized. For others,

it can take a large amount of work to modify them to achieve faster throughput. The

configuration of a grid can greatly affect the performance, reliability, and security of an

organization's computing infrastructure. For all of these reasons, it is important for us to

understand how far the grid has evolved today and which features are coming tomorrow

or in the distant future

Conclusion

Grid computing introduces a new concept to IT infrastructures because it

supports distributed computing over a network of heterogeneous resources and is enabled

by open standards. The advantages of distributed type of architecture for the right kind of

applications are impressive. The most obvious is the ability to provide access to

supercomputer level processing power or better for a fraction of the cost of a typical

supercomputer.

Grid computing works to optimize underutilized resources, decrease capital

expenditures, and reduce the total cost of ownership, however, the specific promise of

distributed computing lies mostly in harnessing the system resources that lies within the

firewall. It will take years before the systems on the Net will be sharing computeresources as effortlessly as they can share information.

Scalability is also a great advantage of distributed computing. Though they provide

massive processing power, super computers are typically not very scalable once they're

installed. A distributed computing installation is infinitely scalable--simply add more

systems to the environment. In a corporate distributed computing setting, systems might


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be added within or beyond the corporate firewall. On the other hand, Grid computing is

about getting computers to work together. Almost every organization is sitting on top of

enormous, unused computing capacity, widely distributed. Mainframes are idle 40% of

the time With Grid computing, businesses can optimize computing and data resources,

pool them for large capacity workloads, share them across networks, and enable

collaboration. Many consider Grid computing the next logical step in the evolution of the

Internet, and maturing standards and a drop in the cost of bandwidth are fueling the

momentum we're experiencing today.

Bibliography

www.google.com

http://www.extremetech.com/

Distributed Systems Architecture and Implementation B.W. Lampsen,

M.Paul

Distributed Systems, Methods and tools for Specification An Advanced

Course, Springer-Verlag.

Operating Systems - Tennanbaman
http://www.google.com/http://www.extremetech.com/http://www.google.com/http://www.extremetech.com/

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