Distributed processing is a technique where computing tasks and data are distributed across multiple computers or devices that are connected through communication facilities. A distributed system is one where components located at networked computers communicate and coordinate their actions by passing messages. Some key forms of distributed processing include centralized, decentralized, parallel, open distributed processing, and clustering. Distributed processing provides advantages like quicker response times, lower costs, improved data integrity, and resource sharing, but also poses challenges like heterogeneity, security, scalability, and failure handling.

1. BY: IMTIAZ HUSSAIN 048 M. EHSAN ULLAH 012 HAFIZ ABUBAKAR 096(06) MUHAMMAD IDREES 027 DISTRIBUTING PROCESSING

2. OUTLINE Distributed Processing Distributed System Architecture Form of D.P Techniques Challenges Advantage/Disadvantage

3. What is Distributed Processing Distributed Processing is a technique of distributing the information over a number of devices. The devices may be computers or data terminals with some level of intelligence. The devices are interconnected with communication facilities.

4. What is Distributed System A distributed system is one in which components located at networked computers communicate and coordinate their actions only by passing messages . Examples The internet An intranet which is a portion of the internet managed by an organization

5. Architecture Software layers System architectures Interfaces and objects Design requirements for distributed architectures

6. Software layers Applications, services Middleware Operating system Computer and network hardware

7. System architectures Client-server model Services provided by multiple servers Proxy srvers and caches Peer processes

8. Design requirements for distributed architectures Performance issues Use of caching and replication Dependability issues

9. Design requirements for distributed architectures Performance issues Responsiveness Balancing computer loads Quality of services Caching and replication The performance issues often appear to be major obstacles to the successful deployment of DS, but much progress has been made in the design of systems that overcome them by the use of data replication and caching. Dependability issues Correctness Security Fault tolerance

10. Form of Distributed Processing Distributed Applications Distributed Devices Network Management and Control Distributed Data

11. Distributed Applications One application splits up into components that are dispersed among a number of machines One application replicated on a number of machines A number of different applications distributed among a number of machines Can be characterized by vertical or horizontal partitioning

12. Distributed Devices Support a distributed set of devices that can be controlled by processors, e.g. ATMs or laboratory interface equipments Distribution of processing technology to various locations of the manufacturing process in factory automation

13. Techniques of Distributed Processing Centralized Decentralized Parallel Open Distributed Processing Clustering

14. Centralized Centralized Processing is done at a central location, using terminals that are attached to a central computer The central computer performs the computing functions and controls the remote terminals. This type of system relies totally on the central computer.

15. Centralized Example : Client/server is the most common example of centralized processing, where server is controlling all the activities on the network.

16. Decentralized Computer systems in different locations. Although data may be transmitted between the computers periodically Example: Yahoo server is the example of the decartelized processing. On each login it connects you to a different server .

17. Parallel Processing Parallel processing is the simultaneous processing of the same task on two or more microprocessors in order to obtain faster results Multiple processor Multiple computer Shared memory resources

18. Open Distributed Processing ODP is a reference model in computer science, which provides a coordinating framework for the standardization of open distributed processing (ODP). It supports distribution, interworking, platform and technology independence, and portability, together with an enterprise architecture framework for the specification of ODP systems.

19. Clustering A cluster is a group of individual computer systems that can be made to appear as one computer system. Clustering is just one form of parallel computing. key points that distinguishes clustering from other is the ability to view the cluster as either a single entity or a collection of stand-alone systems For example : a cluster of web servers can appear as one large web server, but at the same time, individual systems within the cluster can be accessed as individual systems,

20. Clustering Example: Internet is an example of clustering, where different server are working but they look like a single server.

21. Challenges Heterogeneity Security Scalability Failure handling Concurrency

22. Advantages Quicker response time By locating processing power close to user, response time is typically improved. This means that the system responds rapidly to commands entered by users. Lower costs Long-distance communication costs are declining at a slower rate than the cost of computer power Distributed processing can reduce the volume of data that must be transmitted over long-distances and thereby reduce long-distance costs. Improved data integrity High degrees of accuracy and correctness may be achieved by giving users control over data entry and storage.

23. Advantages Reduced host processor costs The productive life of a costly mainframe can be extended by off-loading some its processing tasks to other, less expensive machines Resource sharing One of the main advantages of developing microcomputer networks is because they make it possible to share expensive resources such as high-speed, color laser printers, fast data storage devices, and high-priced software packages.

24. Disadvantage Complexities A lot of extra programming is required to set up a distributed system Network failure Since distributed system will be connected through network and in case of network failure non of the systems will work Security The information need to be passed between the network. And it can be tracked and can be used for illegal purpose. Costly software Not all situations are suitable for distributed computing