Transcript Figure 15.1 A distributed multimedia system

Distributed Systems
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Overview
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Definitions
Advantages of distributed systems
Disadvantages of distributed systems
Characteristics of distributed systems
Examples of distributed systems
Challenges arising from the construction of Distributed Systems
Fault tolerant distributed systems
Paradigm shift
toward deeply distributed applications
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Distributed computing and Distributed system
 Early computing was performed on a single processor (centralized computing).
 Two advances in technology began to change the situation
 The development of powerful microprocessors
 The invention of high-speed computer networks
 A distributed system is
 One in which components located at networked computers communicate
and coordinate their actions only by message passing
 A software system running on top of a network
 Distributed computing
 Is computing performed in a distributed system.
 Refers to the use of distributed systems to solve computational problems.
 In distributed computing, a problem is divided into many tasks, each of
which is solved by one or more component, which communicate with each
other by message passing.
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Advantages of Distributed Systems
 The motivation for constructing and using distributed systems stems from a
desire to share resources.
 Economics: distributed systems allow the pooling of resources, including
CPU cycles, data storage, input/output devices, and services.
– Distributed systems have a better price/performance ratio
 Concurrency: we can solve the problem more quickly using several processors
concurrently.
 Some applications are inherently distributed
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Disadvantages of Distributed Computing
 Multiple Points of Failures: the failure of one or more participating
computers, or one or more network links, can spell trouble.
 Security Concerns: In a distributed system, there are more
opportunities for unauthorized attack.
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Characteristics of Distributed Systems
 Concurrency of components (or Parallel activities)
 Autonomous components executing concurrent tasks
 Lack of a global clock (only limited precision for processes to
synchronize)
 No global state: No single process can have knowledge of the
current global state of the system
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Examples of distributed systems
 The following examples are based on familiar and widely used computer
networks: the Internet, intranets and the emerging technology of networks based on
mobile devices.
 World Wide Web,
 e-mail
 file transfer.
 Programs running on the computers connected to the Internet interact by
passing messages.
 Some times Web is incorrectly used to mean the Internet
 By default the terms ‘client’ and ‘server’ refer to processes rather than the
computers that they execute upon, although in everyday parlance those terms
also refer to the computers themselves.
 An executing web browser is an example of a client.
Distributed Computing Introduction
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Challenges (Cont.)
1. Heterogeneity
 Heterogeneous components must be able to interoperate
2. Openness
 Interfaces should be publicly available to ease adding new
components
3. Security
 The system should only be used in the way intended
4. Scalability
 System should work efficiently with an increasing number of
users
 System performance should increase with inclusion of
additional resources.
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Challenges (Cont.)
5. Fault tolerance and handling
 Failure of a component (partial failure) should not result in
failure of the whole system
6. Concurrency
 Shared access to resources must be possible
7. Distribution transparency
 Distribution should be hidden from the user as much as
possible
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Heterogeneity—Cont.
 Middleware: a software layer that provides a programming
abstraction as well as masking the heterogeneity of the underlying
networks, hardware, operating systems and programming
languages.
 The Common Object Request Broker (CORBA, Ch4, 5 and 20)
is an example.
 Java Remote Method Invocation (RMI, Ch5) is an example of a
middleware that support a single programming language.
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Security
 The resources are accessible to authorized users and used in the
way they are intended.
 Security for information resources has three components:
 Confidentiality
– Protection against disclosure to unauthorized individual.
 Integrity
– Protection against alternation or corruption.
– e.g. changing the account number or amount value in a money
order
 Availability
– Protection against interference with the means to access the
resources.
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Security—Cont.
 Security mechanisms
 Encryption and Authentication
 The following two security challenges have not yet been fully met
 Denial of service attack.
– Achieved by bombarding the service with such a large number of
pointless requests that the serious users are not able to use it.
 Security of mobile code
– Receiving an executable program as an electronic mail
attachment.
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Fault Tolerant Distributed Systems
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Distributed Predicate Detection
 Writing correct distributed programs is hard. In spite of extensive
testing and debugging faults persist.
 Many distributed systems, especially those employed in safety
critical environments should be able to operate properly even in
the presence of software faults.
 Monitoring the execution of distributed systems and detecting
failures is an important way to tolerate such faults. This gives raise
to the predicate detection problem.
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Distributed Predicate Detection—cont.
 Predicate detection is a fundamental problem in distributed
computing. This problem arises in many contexts such as testing
and debugging of distributed systems.
 Example:- the detection of global predicate arises in implementing
the most basic command of a debugging system:
" Stop the program when the predicate P is true”
 A non-trivial task if P requires access to the global state.
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Checkpointing and Logging
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Checkpointing and Logging
 Message logging Every process must save a copy of every message
it sends
 Checkpointing:
Periodically save the state of distributed
computation into stable storage, if any process fails, all processes
are rolled back to the last checkpoint and the computation is
restarted from there.
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Paradigm shift
toward deeply distributed applications
 Thirty years ago
 central computing unit accessed from terminals with a
minimum of resources.
 With the introduction of the personal computers,
 a tremendous increase in the resources available at the client
side
– services were tied to the client hardware
 During the last ten years the connectivity has been increased
 Thus, applications do not need to be standalone and can benefit
from the available connectivity, for additional interaction or just to
benefit from extra computational power deployed in data centers
 Client machines only act as powerful terminals
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Paradigm shift
 We may quickly find scenarios that resemble the typical
deployment forty years ago but now on a global Internet scale:
available anytime, anywhere
 elimination of initial investment in hardware needed to deploy a
service,
 the service can scale and support thousands or millions of users.
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Paradigm shift
 The infrastructure that supports these services is now an
extremely complex distributed system.
 we need professionals that are able to design and implement the
mechanisms and the software that provide it: this is where PhD
and master students will find their role.
 The main problem is scalability
 It should do so while maintaining the image of a increasingly
powerful single computing machine
 Cloud Computing and related computing paradigms and concepts
like Grid Computing, Utility Computing or Voluntary Computing
have been discussed in academia and industry
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Could Computing
 A cloud is an elastic execution environment of resources
(computational and storage) involving multiple stakeholders and
providing a metered service (pay only for what you use) at
multiple granularities for a specified level of quality (of service).
 Elasticity : the ability to scale resource usage up and down rapidly
according to instantaneous demand.
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Volunteer Computing
 Volunteer computing is a type of distributed computing in which
computer owners donate their computing resources (such as processing
power and storage) to one or more "projects".
 The Berkeley Open Infrastructure for Network Computing (BOINC) is
an open source middleware system for volunteer and grid computing.
 It became useful as a platform for other distributed applications in areas
such as mathematics, medicine, biology, climatology, and astrophysics.
The intent of BOINC is to make it possible for researchers to tap into the
enormous processing power of personal computers around the world.
 BOINC is software that can use the unused CPU and GPU cycles on a
computer to do scientific computing—what one individual does not use
of his/her computer, BOINC uses.
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Grid Computing
 It is the federation of computer resources from multiple locations
to reach a common goal. What distinguishes grid computing from
conventional high performance computing systems such as cluster
computing is that grids tend to be more loosely coupled,
heterogeneous, and geographically distributed.
 It was originally driven by scientific applications which are usually
computation-intensive.
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Utility Computing
 Utility computing is the packaging of computing resources, such as
computation, storage and services, as a metered service. This
model has the advantage of a low or no initial cost to acquire
computer resources; instead, computational resources are
essentially rented.
 This repackaging of computing services became the foundation of
the shift to "on demand" computing, software as a service and
cloud computing models that further propagated the idea of
computing, application and network as a service.
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Cloud Computing
 a model for enabling convenient, on-demand network access to a
shared pool of configurable computing resources.
 Cloud Computing is not a fundamentally new paradigm. It draws
on existing technologies and approaches, such as Utility
Computing, Software-as-a-Service, distributed computing, and
centralized data centers.
 What is new is that Cloud Computing combines and integrates
these approaches.
 While Grid Computing is partly defined by its decentralized
control, Cloud Computing seems to be a step back towards
centralizing IT in data centers again to economize on scale and
scope.
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Cloud Computing—Cont.
 The most frequently used pricing model of the presented service
providers is Pay-per-use, in which the user pays a static price for a
used unit, often per hour, GB, CPU-hour etc.
 In order to allow the easy use of Cloud systems, there will be the
need for a standardized Cloud API.
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Cloud Computing—Cont.
 Clouds also require new business models, especially with respect to
the licensing of software. This has already become an issue in
Grids.
 Current licenses are bound to a single user or physical machine.
 Usage-based licenses, penalties and pricing.
 More sophisticated technologies for service composition and adhoc creation of situational applications are needed.
 The hype about cloud computing accompanies the Software as a
Service (SaaS) wave. Services built on top of Cloud infrastructures
enable software providers to offer products at lower cost and
simultaneously with a higher degree of customization.
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Cloud Computing—Cont.
 Another crucial point for the eventual acceptance of Cloud
technology in business industries will be the safety of critical data,
both in transfer as in storage.
 Reasons for that are foreign laws, which would possibly allow
foreign governments to access this data, or domestic insurance
contracts, demanding the data to be stored only in certain regions.
 How the large Cloud vendors will tackle this concern?
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Mobile and ubiquitous computing
 The integration of small and portable computing devices into
distributed systems. These devices include: Laptop computers,
Handheld devices (PDAs, mobile phones, pagers, video cameras
and digital cameras), smart watches, devices embedded in
appliances such as cars, washing machines.
 Ubiquitous is intended to mean that small computing devices
will eventually become so pervasive in everyday objects that
they are scarcely noticed.
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Related Topics
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Performance analysis of distributed systems
Load balancing in clouds
Distributed AI
Distributed information retrieval
Distributed evolutionary algorithms
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