The strength of grid computing, namely being able to aggregate distributed computing and data storage capacities for solving larger, more complex problems, can currently only be partially exploited because of difficulties in accessing grid infrastructures, lack of usability, security or legal concerns, and missing performance guarantees or billing mechanisms. In this chapter, we present application scenarios where some of those weaknesses are overcome by presenting the user with transparent, intuitive, location independent access to grid resources using techniques from mobile and pervasive computing. Two approaches are suggested. In the implicit approach, user needs and context information are to be captured by means of smart devices and objects trying to perceive the jobs and tasks users want or need to delegate for computation in the grid. In the user-controlled approach, users explicitly provide meta-information on the type of tasks to be solved and the underlying supporting infrastructure provides the most suitable type of applications as well as mechanisms for returning the results to the user. Both approaches are briefly described in the chapter by means of a specific sample scenario in the field of image analysis. Other application areas for mobile and ubiquitous grids based on our experiences gained in the Austrian grid project are also presented in the paper.
The scenarios described in the foregoing section aim to combine strengths of three main disciplines: grid computing, pervasive computing, and mobile computing.
The term “grid” was coined in the mid-1990s to refer to a proposed distributed computing infrastructure for advanced science and engineering (Foster & Kesselman, 2004). A grid is an infrastructure of geographically distributed resources, comprising hardware components such as processors, memory media, or scientific instrumentation and software components such as services, applications, licenses, and so forth. Its infrastructure consists of hard- and software elements to aggregate and to coordinate resources.
The first grid that has been developed, for the European Organization for Nuclear Research (CERN) to support the research of the particle physics laboratory (Colasanti, 2004), uses a large scale distributed system by taking the advantage of the rich infrastructure provided by the Internet. By using a grid of computers, it is possible to aggregate computational power to generate a huge virtual multi-computer ready for processing, storage, and communication. Since a grid can be made up of a set of geographically separate networks, enormous computational power can be made available for solving complex or data intensive problems.
Grid computing is still at its early stages of evolution. Anyhow it is no longer the exclusive realm of researchers aiming to solve sophisticated scientific tasks (Gentsch, 2004). Alike the evolution of the Internet, main grid initiatives aim to successively establish a global grid, providing users with infinite resources, just by plugging the computer.
Key Terms in this Chapter
Pervasive Computing: Pervasive computing aims to develop interaction paradigms, where information processing has been thoroughly integrated into everyday objects and activities, allowing computers to vanish into the background.
MPEG 7: Is formally called “Multimedia Content Description Interface.” It is a multimedia content description standard developed to describe content itself and thus allow fast and efficient searching for material that is of interest to the user.
Context: Context comprises relevant information about a service’s situation of use, for example information about the user’s interest, device’s display capabilities, or geographic location of service invocation.
User Profile: Or simply “profile” is a collection of personal settings enabling the personalization of a system.
Usability: Is an equivalent to “user friendliness” and denotes the ease with which people can employ a tool or an object in order to achieve a particular goal.
Grid Computing: A grid is an infrastructure of geographically distributed resources, comprising hardware components to aggregate and to coordinate resources. By using a grid of computers, it is possible to aggregate computational power to generate a huge virtual multi-computer ready for processing, storage, and communication.
Smart Objects: Intelligent artefacts, embedded in a pervasive computing environment, detecting user needs and initializing all necessary processes.
Complete Chapter List
Elhadi Shakshuki, Xinyu Xing, Haroon Malik
Reinhard Kronsteiner, Bettina Thurnher
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Mamun I. Abu-Tair
Abdulhussain E. Mahdi
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Eduardo Antonio Viruete Navarro
Paolo Barsocchi, Alan A. Bertossi, M. Cristina Pinotti, Francesco Potortì
Do van Thanh, Ivar Jørstad
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Daniel C. Doolan, Sabin Tabirca, Laurence T. Yang
Daniel C. Doolan, Sabin Tabirca, Laurence T. Yang
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Christos K. Georgiadis
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Baud Haryo Prananto
Diego Moreira Alves
Dietmar G. Wiedemann
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Patrícia Dockhorn Costa, Luís Ferreira Pires, Marten van Sinderen
Frédéric Lassabe, Philippe Canalda, Damien Charlet, Pascal Chatonnay, François Spies
Anastasis A. Sofokleous, Marios C. Angelides, Christos N. Schizas
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Ioannis Priggouris, Evangelos Zervas, Stathes Hadjiefthymiades
Ghita Kouadri Mostéfaoui
Do Van Thanh, Ivar Jørstad, Schahram Dustdar
Mohamed Ali Feki
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Roland Wagner, Franz Gruber, Werner Hartmann