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					Distributed Data
   Processing
   See chapter 3, Stalling’s book


          Ho Sooi Hock
                    Outlines
   Types of Data Processing
   Advantages and Disadvantages of CDP
   Driving Factors and Reasons for DDP
   Advantages and Disadvantages of DDP
   Forms of DDP
   Distributed Applications and Databases
   Networking requirements for DDP
                Data Processing
   Centralized data processing
       Computer, processing, data, control, staff and
        development are centralized
   Distributed data processing (DDP)
       May include centralized center plus satellite
        facilities
       Involves distributed computers, data, and processing
       Greater flexibility in meeting individual needs
       More redundancy and more autonomy
            Advantages of CDP
   Economies of scale in the purchase and
    operation of equipment and software
   Shared professional resources
   Ease of control for data processing
    procurement, standards for programming,
    data file structure and design of security
    policies
An Example of CDP
Distributed Data Processing (DDP)
   “A data processing system in which processing
    is decentralized, with the computers and
    storage devices in dispersed locations.” [1]
   “Data processing in which some of the functions
    are performed in different places and
    connected by transmission facilities.” [2]



[1] www.iclml.mdx.ac.uk/DELBERT/glossary_terms.htm
[2] http://www.cogsci.princeton.edu/cgi-bin/webwn?stage=1&word=distributed+data+processing
Distributed Data Processing (DDP)
   One in which computers (usually smaller
    computers) are dispersed throughout an
    organisation
   Processing information in the most
    effectively way based on operational,
    economic and geographic considerations
   Redundancy
   Autonomy
An Example of DDP
Key Factors of Increasing DDP

   Dramatic and continuing decrease of hardware
    costs, accompanied by an increase in its
    capability
   More powerful desktop computers
   Improved user interfaces (GUI)
   Growing repertoire of applications software
   Technologies to share data across multiple
    servers
                Reasons for DDP
   Need for new applications
       On large centralized systems, development can take
        years
       On small distributed systems, development can be
        component-based and very fast
   Need for short response time
       Centralized systems result in contention among
        users and processes
       Distributed systems provide dedicated resources
               Benefits of DDP
   Responsiveness
   Availability
   Correspondence to organisation patterns
   Resource sharing
   Incremental growth
       Avoiding the “all or nothing” approach
   Increased user involvement and control
   Decentralised operation and centralised control
   End-User productivity
   Distance and location independence
   Privacy and security
   Vendor independence
   Flexibility
      Potential Drawbacks of DDP
   Difficult to test and diagnose failure
   Dependent on communication technology
   Incompatibility among equipments
   Incompatibility among data
   Network management and control
   Difficulty in control of corporate information
    resources
   Sub-optimisation
   Duplication of effort
              Forms of
    Distributed Data Processing

   Distributed Applications
   Distributed Devices
   Network Management and Control
   Distributed Data
         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 characterised by vertical or horizontal
    partitioning
            Vertical Partitioning

   Data processing is distributed in a hierarchical
    structure
   This distribution may reflect organisational
    structure, or may simply be the most
    appropriate for the application
Examples of Vertical Partitioning
   Insurance
       Data processing distribution is often a two-level
        hierarchy, branches prepare new contracts and
        process the claims. Summary information is sent to a
        head office.
   Process Control
       Each major operation is controlled by a workstation.
        The microprocessor are responsible for the
        automated control of sensors. All the workstations
        are linked to a higher-level computer concerned
        with operations planning, optimization, management
        of information and general corporate data
        processing.
         Horizontal Partitioning
   Data processing is distributed among a number
    of computers that have peer relationship
   There is no concept of master/slave
   Computers in a horizontal configuration
    normally operate autonomously
   One application is replicated on different
    systems, e.g. word processor and spreadsheet
   Different application on different systems
Examples of Horizontal Partitioning
   Office automation support system
       Secretarial staff and other personnel are equipped
        with personal computers linked in a network.
       Each user’s PC contains software packages useful to
        that user (word processing, spreadsheet).
       The systems exchange message, files and other
        information.
   Air traffic control
       Each regional center operates autonomously.
       Within the center, several computers are used to
        process radar and radio data to provide a visual
        status to their traffic controllers.
             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
Network Management and Control
   Control of access to the facilities in the
    distributed system
   Monitor the status of various components in the
    distributed system
   Manage communications facility to ensure
    availability and responsiveness
   Each distributed computers must include some
    management and control logic to interact with
    the central network management system.
      Client/Server Architecture
   Combine the best aspects of both distributed
    and centralised computing
   Users work on powerful workstations or PCs
    which support end user programming and use of
    off-the-shelf software
   Good response time inherent in distributed
    architecture
   Cost-effective, e.g. economies of scale by
    centralising support for specialised functions
   Flexible and scalable
                 Distributed Data
   Small organisations can function with a
    collection of files, e.g. report files,
    spreadsheets
   Large organisations need one or more databases
   Distributed organisations will often require
    distributed database
       “A distributed database is one in which portions of
        the data are dispersed among a number of computer
        systems.”
   Three ways to organise database: centralised,
    replicated and partitioned
            Centralised Database
   No duplication of data, little reorganisation
    required
   Used when security and integrity of the data
    are important
   Drawbacks
       Contention when accessing data simultaneously
       Response time
       Poor reliability
              Replicated Database
   All or parts of database is copied at two or more
    computers
   Less contention and improved response time
   Provides backup and recovery
   High reliability
   Drawbacks
       High storage and database reorganisation costs
   Replication strategy variants
       Real time (two phase commit)
       Near real time (batch backups, e.g. every 30 minuets)
       Deferred (bulk transfer, once or twice per day)
             Partitioned Database
   The database exists as distinct and
    non-overlapping segments
   Dispersed load hence good response time
   Eliminates single point of failure
   Drawbacks
       Difficult to produce ad hoc management reports
       Complex processing logics
       Not good for request involving data from multiple
        partitions
Networking Implications of DDP
   Connectivity
       The ability of components in the system to exchange
        data
   Availability
       The percentage of the time that a particular
        function or application is available for users
   Performance
       Response time
                  Summary
   Differences between CDP and DDP
   Advantages and disadvantages of CDP and DDP
   Different forms of DDP
   Distributed applications and databases
   Networking requirements
      Acknowlegements
This module was taught by Dr. Payam Mamaani
Barnaghi since 2005. Most slides have been
adopted from his lecture materials and original
works of William Stallings with some changes.

				
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