processes of distributed computer systems
TRANSCRIPT
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PROCESSESOF DISTRIBUTED COMPUTING
PRESENTED BY JAREK BOILO CABAUTAN
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OUTLINE OF THIS PRESENTATION
Distributed Processing
Distributed System
Architecture
Form of DP
Techniques
Challenges
Advantages and
Disadvantages
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WHAT IS DISTRIBUTED PROCESSING?
• 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.
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WHAT IS DISTRIBUTED PROCESSING?
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
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ARCHITECTURE
Software Layers
System Architectures
Interfaces and Objects
Design Requirements for Distributed
Architectures
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ARCHITECTURE: SOFTWARE LAYERS
• Applications, services, etc.
• Middleware
• Operating systems
• Computer and network hardware
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ARCHITECTURE: SYSTEM ARCHITECTURES
• Client-server model
• Services provided by multiple servers
• Proxy servers and caches
• Peer processes
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ARCHITECTURE: DESIGN REQUIREMENTS FOR
DISTRIBUTED ARCHITECTURES
• Performance Issues
• Responsiveness
• Balancing computer loads
• Quality of services
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ARCHITECTURE: DESIGN REQUIREMENTS FOR
DISTRIBUTED ARCHITECTURES
• Caching and Replication
• The performance issues often appear to be a
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.
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ARCHITECTURE: DESIGN REQUIREMENTS FOR
DISTRIBUTED ARCHITECTURES
• Dependability Issues
• Correctness
• Security
• Fault tolerance
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FORM OF DISTRIBUTED PROCESSING
Distributed Applications
Distributed Devices
Network Management and Control
Distributed Data
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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
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DISTRIBUTED DEVICES
• Support a distributed set of devices that can be
controlled by processors, such as ATMs or
laboratory interface equipment
• Distribution of processing technology to various
locations of the manufacturing process in factory
automation
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TECHNIQUES OF DISTRIBUTED PROCESSING
Centralized
Decentralized
Parallel
Open Distributed Processing
Clustering
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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.
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CENTRALIZED
Client/Server is the most
common example of
centralized processing,
where server is controlling
all the activities on the
network.
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DECENTRALIZED
• Computer systems in different
locations. Although data may be
transmitted between computers
periodically. Facebook is an
example of a decentralized
processing where each login
connects you to different servers.
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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
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OPEN DISTRIBUTED PROCESSING
• ODP is a reference model in computer science, which provides a coordinating framework for the standardization of ODP.
• It supports distribution, internetworking, platform & technology independence, and portability, together with an enterprise architecture framework for the specification of ODP systems.
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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.
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CLUSTERING
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.
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CLUSTERING
Example:
Internet is an example of clustering, where
different server are working but they look like a
single server.
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CHALLENGES
• Heterogeneity
• Security
• Scalability
• Failure handling
• Concurrency
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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.
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ADVANTAGES
• 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.
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ADVANTAGES
• Improved Data Integrity
• High degrees of accuracy and correctness may be achieved b giving users control over data entry and storage.
• Reduced Host Processor Costs
• The productive life of a costly mainframe can be extended by off-loading some of its processing tasks to other less expensive machines.
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ADVANTAGES
• 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.
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DISADVANTAGES
• Complexities
• A lot of extra programming is required to setup a
distributed system.
• Network Failure
• Since distributed system will be connected through
network and in case of network failure none of the
systems will work.
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DISADVANTAGES
• Security
• The information need to pass between the network, and
it can be tracked and can be used for illegal purposes.
• Costly Software
• Not all situations are suitable for Distributed
Computing.
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