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Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5

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Please purchase a personal license.


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Definition of a Distributed System (1)

A distributed system is:

A collection of independent

computers that appears to itsusers as a single coherentsystem.


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Definition of a Distributed System (2)

Figure 1-1. A distributed system organized as middleware. Themiddleware layer extends over multiple machines, and offers

each application the same interface.


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Transparency in a Distributed System

Figure 1-2. Different forms of transparency in adistributed system (ISO, 1995).


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Scalability Problems

Figure 1-3. Examples of scalability limitations.


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Scalability can be measure along 3 dimensions:

Size scalable: easily add more users &recourses to the system

Geographically scalable: users & resources may

lie far apart Administratively scalable: it can still be easy to

manage even if it spans independent

administrative organizations


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Characteristics of decentralized algorithms:

No machine has complete information about thesystem state.

Machines make decisions based only on local

information. Failure of one machine does not ruin the

algorithm.

There is no implicit assumption that a globalclock exists.


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Scaling Techniques (1)

Figure 1-4. The difference between letting (a) a serveror (b) a client check forms as they are being filled.


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Scaling Techniques (2)

Figure 1-5. An example of dividing the DNS

name space into zones.


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Pitfalls when Developing

Distributed Systems

False assumptions made by first time developer:

The network is reliable.

The network is secure.

The network is homogeneous.

The topology does not change.

Latency is zero.

Bandwidth is infinite. Transport cost is zero.

There is one administrator.


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Types of Distributed Systems:1. Distributed Computing Systems

Cluster Computing Systems

Grid Computing Systems

2. Distributed Information Systems

Transaction Processing Systems

Enterprise Application Integration (Exchange info via RPC or RMI)

3. Distributed Pervasive Systems (usually small, battery-powered systems, Mobile & wireless)

Home Systems (e.g. Smart phones, PDAs)

Electronic Health care systems (Heart monitors, BAN: Body AreaNetworks)

Sensor Networks (distributed Databases connected wirelessly)


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Hooking up a collection of simple computers via

high-speed networks to build a supercomputingenvironment

Mostly homogenous

Example: server clusters at Banks, Brokerages, etc.


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Figure 1-6. An example of a cluster computing system.


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In contrast to cluster computing, grid computing

systems have a high degree of heterogeneity, noassumption are made concerning hardware, OS,Networks, Security,

Users and recourses from different organizations arebrought together to allow collaboration (i.e. a V.O.= Virtual Organization)

Members belonging to the same V.O. have access

rights to a common set of recourses (e.g. Police,FBI, and some local agencies may form acomputing grid)


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Figure 1-7. A layered architecture for grid computing systems.

Grid

Middleware

Applications operate

within a virtual

organization and make

use of grid computing

environment


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Grid Computing Layers:

1. Collective layer: access to multiple resources and

typically consists of services for resource discovery,allocation and scheduling.

2. Connectivity layer: transfer data between resources

or access a resource from a remote location3. Resource layer: managing a single recourse such as

creating a process or reading data

4. Fabric layer: provides interface to local resources ata specific site within a V.O.


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Transaction Processing Systems (1)

Figure 1-8. Example primitives for transactions.


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Characteristic properties of transactions:

Atomic: To the outside world, the transactionhappens indivisibly.

Consistent: The transaction does not violate

system invariants. Isolated: Concurrent transactions do not

interfere with each other.

Durable: Once a transaction commits, thechanges are permanent.


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Figure 1-9. A nested transaction.


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Figure 1-10. The role of a TP monitor in distributed systems.


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Enterprise Application Integration

Figure 1-11. Middleware as a communication facilitator inenterprise application integration.


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Enterprise Application Integration

Various middleware packages and

communication protocols are used insupport of Enterprise applications such as:

CORBA (Common Object Request Broker Architecture)

DCOM (Distributed Component Object Management)

RPC (Remote Procedure Call) RMI (Remote Method Invocation)


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Distributed Pervasive Systems

Requirements for pervasive systems:

Embrace contextual changes (i.e. I was aphone now I am a web access device. Adevice must continuously be aware of thefact that its environment may change)

Encourage ad hoc composition (used

differently by different users, e.g. PDA) Recognize sharing as the default (easilyread, store, manage, and share info)


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Electronic Health Care Systems (1)

Questions to be addressed for health care systems:

Where and how should monitored data be

stored? How can we prevent loss of crucial data?

What infrastructure is needed to generate and

propagate alerts? How can physicians provide online feedback?

How can extreme robustness of the monitoring

system be realized? What are the security issues and how can the

proper policies be enforced?


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Electronic Health Care Systems (2)

Figure 1-12. Monitoring a person in a pervasive electronic healthcare system, using (a) a local hub or

(b) a continuous wireless connection.


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Sensor Networks (1)

Questions concerning sensor networks:

How do we (dynamically) set up anefficient tree in a sensor network?

How does aggregation of results takeplace? Can it be controlled?

What happens when network links fail?


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Sensor Networks (2)

Figure 1-13. Organizing a sensor network database, while storingand processing data (a) only at the operators site or


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Sensor Networks (3)

Figure 1-13. Organizing a sensor network database, while storingand processing data or (b) only at the sensors.

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