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Naghibzadeh, M. (2007). A New Approach to Resource Discovery and Dissemination for Pervasive Computing Environments Based on Mobile Agents. Scientia Iranica, 14(6), -.
M. Naghibzadeh. "A New Approach to Resource Discovery and Dissemination for Pervasive Computing Environments Based on Mobile Agents". Scientia Iranica, 14, 6, 2007, -.
Naghibzadeh, M. (2007). 'A New Approach to Resource Discovery and Dissemination for Pervasive Computing Environments Based on Mobile Agents', Scientia Iranica, 14(6), pp. -.
Naghibzadeh, M. A New Approach to Resource Discovery and Dissemination for Pervasive Computing Environments Based on Mobile Agents. Scientia Iranica, 2007; 14(6): -.

A New Approach to Resource Discovery and Dissemination for Pervasive Computing Environments Based on Mobile Agents

Article 11, Volume 14, Issue 6, December 2007  XML PDF (619 K)
Author
M. Naghibzadeh
Department of Computer Engineering,Ferdowsi University of Mashhad
Abstract
Pervasive computing, as a new branch in the field of distributed computing, has received wide contribution from different researchers. In this novel computing model, a vast range of computational and communication resources, along with other types of service, are gathered under a single system image based on certain predefined criteria. To create a transparent environment and provide end-users with the illusion of the local availability of multiple resources, some kind of manager is needed to coordinate the tasks and their required resources. The resource management system is mainly responsible for a balanced distribution of available resources among different tasks. Devising efficient resource discovery and dissemination algorithms is, hence, an important step towards preparing the bases for a resource centric management package. In this article, the aim is to provide two algorithms for this problem, using mobile agents. The proposed resource discovery algorithms use two different hierarchical and flat approaches. The simulations show a good performance for both of the proposed models; however, the hierarchical algorithm shows better results, based on some of the introduced factors.
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