The Advancement In Technology Distributed Network

By Usman Bhatti

Abstract This paper discus the distributed peer-to-peer network with an extensible plugs in architecture. It also describes the distributed Application and Computing. The unique advantages of distributed network architecture are flexibility, fault-tolerance, and lawyer resistance. The platform will support both broadcast and direct send/receive operations. Users will be separate from hosts, so that a user can log onto the network from any computer. Both authentication and data encryption will be built in from the ground up.

1. Introduction In the late 1980s and early 1990s, distributed systems consisted of large numbers of desktop computers. Today, the Internet and Web technologies have greatly expanded the concept of distributed systems. Distributed means that clients may interact with many different servers all over the network. . In distributed networks all the nodes in the network should have similar behavior and there is no network structure or organization is assumed.

Distributed computer networks consist of clients and servers and these are connected in such a way that any system can communicate with any other system. May be the platform for distributed systems are the enterprise network linking workgroups, departments, branches, and divisions of an organization. In fact the data is not located in one server, but in many servers. May be these servers are geographically different areas, connected by WAN links.

Networks that are built on web technologies are truly advanced distributed computing networks. Web technologies provide a new idea of distributed computing. Web server provides the universal access for any client with a web browser. Distributed computing utilizes the network of many other computers, to achieve a segment of an overall task and achieve a computational result that is much more quickly than a single computer. Distributed computing also allows many users to interact and connect openly.

2. Distributed Networking 2.1 A Definition To have a distributed network ( 1 ) , you need two things:

1. All the nodes in the network should have similar behavior.

2. No network structure or organization is assumed.

2.2 Anonymity By the definition of a distributed network itself, sending some data to a node is about the same as sending the same data to any other node. We can even say that the whole network is "represented" by any node in it. If you connect yourself to any node on the network, it is as if you connected yourself to all the nodes on the network. From that, we can say that no other node really needs to know a way to identify you precisely: if they want to send you some data, they just need to send it to any node on the network, it shouldn't make any difference.

As long as you can connect yourself to the network, you can send data to any node without you having to know where it is, or how to identify it. As a result, a distributed network can be anonymous, in the sense that you don't really need to know where some data came from or where its destination is.

Thus, it is possible, with a distributed network, to make an abstraction of the specificities of the underlying network protocol, or of the IP addresses in a TCP/IP network.

2.3 Peer-to-Peer If you think about it, if all the nodes are equivalent, then there is no need to restrict the ability to provide information only to some nodes in the network. Any node can equally be a server. So, from that and from the abstracted IP addresses, a distributed network can implement a peer-to-peer network.

3. Distributed Computer Networks

Distributed computer networks consist of clients and servers connected in such a way that any system can potentially communicate with any other system. The platform for distributed systems has been the enterprise network linking workgroups, departments, branches, and divisions of an organization. Data is not located in one server, but in many servers. These servers might be at geographically diverse areas, connected by WAN links. Illustrates the trend from expensive centralized systems to low-cost distributed systems that can be installed in large numbers.

The Web is a "massively distributed collection of systems," to paraphrase a 3Com paper mentioned in the next section. It consists of countless nodes ranging from servers, to portable computers, to wireless PDAs, not to mention embedded systems that largely talk to one another without human intervention.

The distributed environment needs the following components:

• The network platform that supports multivendor products and communication protocols. TCP/IP has become the de facto standard protocol.
• Application interfaces to exchange information between client and server, such as RPC (remote procedure call), message-passing systems, or Web protocols.
• A directory naming service that keeps track of resources and information, and where they are located.
• File systems and databases that support partitioning and replication to provide the distribution of data and ensure the availability, reliability, and protection of that data.
• Caching schemes to place information closer to users and minimize the amount of time it must be transmitted across long-distance links.
• Security features such as authentication and authorization, as well as trust relationships between systems at diverse locations.

3.1 Massively Distributed Systems

3Com has an interesting paper called "Massively Distributed Systems" by Dan Nessett. The paper talks about the trend from high-cost centralized systems to distributed low-cost, high-unit-volume products, to massively distributed systems that are everywhere and that often "operate outside the normal cognizance of the people they serve." This paper is highly recommended for those who want to understand trends in distributed computing.

Nessett discusses two approaches to distributed processing. One method is to move data to the edge processors, as is done with the Web and Web-based file systems. The other approach is to move processing to the data, as is done with active networking and Java applets (e.g., objects move within the distributed system and carry both code and data). If the object consists primarily of data, it will closely approximate moving data to the processing. If it consists primarily of code, it will closely approximate moving processing to the data. Yet another approach is the thin-client approach, in which users work at graphical terminals connected to servers that perform all processing and store the user's data.

The World Wide Web is a massively distributed system full of objects. There are Web sites containing documents that contain both objects and referrals to other objects. Nessett talks about how the presentation of massively distributed objects to technically naïve users will require new interfaces. One example is to represent objects in virtual spaces that users navigate through as if walking through a 3D world.

4. Distributed applications

Distributed applications allow users to interact with other systems on a network. A distributed application is traditionally divided into two parts-the front-end client and the back-end server. This is the client/server model a model that balances processing loads between client and server.

Application/groupware suites like Microsoft Exchange, Novell GroupWise, Lotus Notes/Domino, and Netscape SuiteSpot are designed for distributed networks. Management applications that use SNMP can collect information from remote distributed systems and report it back to management systems.

The Internet and the Web are "massively distributed networks." Web browsers provide a universal client for accessing applications and resources on local and remote systems, either within the organization or outside. The object/component approach breaks up complex programs into smaller components that make it easier to distribute and update applications, especially on the Internet.

The three-tier model consists of clients on one end and servers on the other, and a middle tier that provides services, business rules, transaction management, and other business logic. The server side may consist of database servers, data marts, and data warehouses. The middleware component is now commonly referred to as an application server. Application servers interface with databases and information systems on the back end and clients, usually Web server clients, on the front end. The servers may perform relatively simple functions, such as building Web pages on-the-fly with data obtained from back-end servers. Application servers provide a variety of functions. They serve as a central hub for application services, including message routing, object exchange, transaction processing, data transformation, and so on.

4.1 Distributed Application Models

There are several models for creating distributed applications and providing a way for client, server, and components to communicate:

• RPCs (remote procedure calls) A session-oriented communication protocol between computers connected across networks. RPCs are generally used for real-time, connection- oriented activities.
• Messaging services Messaging services (usually called MOM, or message-oriented middleware) provide a way to exchange information between applications and components using queues and store-and-forward messaging. It is not appropriate for real-time communications. Newer techniques use XML. Microsoft's SOAP (Simple Object Access Protocol) is a message-passing protocol that uses HTTP to carry XML-formatted messages.
• ORB (object-request brokering) The best way to describe how ORBs fit into this picture is to describe their use in the Web client/server model. A user running a Web browser contacts a Web site and uses an ORB to locate a necessary component. Once he or she has the component, the client communicates through it to back-end services, and the original Web server is out of the picture.

5. Distributed Computing

Distributed computing utilizes a network of many computers, each accomplishing a portion of an overall task, to achieve a computational result much more quickly than with a single computer. In addition to a higher level of computing power, distributed computing also allows many users to interact and connect openly. Different forms of distributed computing allow for different levels of openness, with most people accepting that a higher degree of openness in a distributed computing system is beneficial. The segment of the Internet most people are most familiar with, the World Wide Web, is also the most recognizable use of distributed computing in the public arena. Many different computers make everything one does while browsing the Internet possible, with each computer assigned a special role within the system. A home computer is used, for example, to run the browser and to break down the information being sent, making it accessible to the end user. A server at your Internet service provider acts as a gateway between your home computer and the greater Internet.

Another type of distributed computing is known as grid computing. Grid computing consists of many computers operating together remotely and often simply using the idle processor power of normal computers. The highest visibility example of this form of distributed computing is the At Home project of the Search for Extra-Terrestrial Intelligence (SETI).

6. Network Stability Because we assume that there is no network structure or organization, or that the network is "anarchic", we can assume that there is no single point of failure. What this means is that for the network to "go down", all the nodes must "go down". Also, it can be very difficult to attack the network, since you will need to attack it from many points at the same time to have any kind of effect.

So, we can say that the network is dynamic, as opposed to static.

In a static network, the number of nodes affected when a node disconnects itself, n , is:

n = im + k ,

where k and i are some arbitrary constants other than 0, and m is the total number of nodes in the network. As the network grows, the number of nodes affected by the disconnection becomes higher. For example, a pure Client/Server network has i = 1 and

k = 0 for the server, and i = 0 and k = 1 for the clients. In a dynamic network, the number of affected nodes becomes:

n = k , where k is an arbitrary constant.

If we look at the ratio of the nodes that will be affected by a disconnection as the size of the network grows to infinity, the ratio is i for a static network, and 0 for a dynamic network. This means that for a dynamic network, the more the network grows, the more stable it becomes.

7. The Ability to solve network flow problems We ran our toolkit on four different networks of increasing complexity, starting with the absurdly simple case, and adding traps and tricks in later networks.

7.1 Trivial Network

This network was designed basically as a sanity check of the evolver. Given any level of functionality in the evolver short of pure random programs, the model should be solved. For every run we performed, the maximum flow of 20 had been achieved within 10 generations. In most cases, it was solved within 5.

7.2 Simple Network

The simple network adds a tricky loop between the middle nodes. Any program which sends flow from the bottom to the top is doomed to a less than optimal solution.

Simple Network Performance

This graph shows that half of the final programs learned nothing about the tricky loop and pushed the full five units of flow through, thus reducing their total flow to 20.

Of the four programs which didn’t fail completely on the loop, only one got a perfect score of 25.

The average fitness was 21.5, or 86 percent of the maximum flow of 25.

7.3 Complex Network

This network adds flows out of the sink, and into the source, as well as increasing the number of nodes and pipes in the network.

Programs must learn not to send flow out of the sink, or into the source. Also, the program must not send flow to the middle node from the top, because the middle node does not have sufficient output capacity to handle the extra flow.

Complex Network Performance

The three lowest fitness scores of 20 indicate that the program never learned not to send flow out of the sink node. The three scores of 25 indicate programs which learned not to send flow out of the sink, but were fooled by both the flow from the top node back into the source, and the flow from the top node into the middle node, where it cannot be effectively used.

7.4 Tricky Network

This sadistic network requires that flow be pushed only on the two pipes shown in bold on the graph. An optimal solution either requires the program (1) across multiple generations, or learns about the topography during runtime through message passing.

Tricky Network Performance

This graph shows that a solution is possible using evolutionary techniques even for the most challenging of networks. All programs managed to avoid the many pitfalls we designed into the network and send some flow from the source to the sink. The majority of programs managed to send 70% or more of the maximum capacity of 10. Two programs learned to send the maximum possible flow of 10.

(1) Pitfalls

This program tree shows an actual program generated by a test run on the tricky network that has been slightly simplified for human comprehension. This program takes the first approach of learning topography implicitly through evaluation. This has the disadvantage of being specific to the particular network used to evaluate it.

A potential way to generate programs, which make use of the second, more general method, is to evaluate with several networks simultaneously, summing the fitness for all of them. The toolkit has such capability, but time constraints did not allow us to explore this avenue vigorously.

The average fitness value was 24.125, or 80 percent of an optimal flow of 30.

8. Characteristics of distributed environment

i. It takes advantage of client/server computing and multi network architectures.

ii. It distributes processing in low-cost systems and relieves servers of many tasks.

iii. Data may be accessed from a many sites over wired or wireless networks.

iv. Data may be replicated to other systems.

v. Distributing data provides protection from local disasters.

9. Conclusion

Advance technology gives the idea of distributed network. It is very important to solve our problems. So we can say that Distributed networks consist of clients and servers connected in such a way that any system can potentially communicate with any other system and problem solving is down by distributed Application and Computing. It reduces the cost of Centralize client/server architecture. We can access data from many servers. We say that different servers are used for different applications.

10. References

1. Encyclopedia of Networking and telecommunication 3 rd edition

by McGraw Hill “ distributed computer networks" 2001

http://www.linktionary.com/d/dist_computing.html

2. Brendan McGuigan “ what is distributed computing" 2005 http://www.wisegeek.com/what-is-distributed-computing.htm

3. “ distributed network model's ability to solve network flow problems"

http://www.limey.net/~fiji/mqp/presentation/analysis.html

4. Encyclopedia of Networking and telecommunication 3 rd edition

by McGraw Hill “ distributed computer networks" 2001

http://www.linktionary.com/d/dist_apps.html

5. Benad “ Distributed Networking: A Definition"2001

http://anet.sourceforge.net/docs/devel/intro/distributed.html