Mobile computing applications are required to operate in environments in which the availability for resources and services may change significantly during system operation. Thus, mobile computing applications need to be capable of adapting to these changes to offer the best possible level of service to their users. However, traditional middleware is limited in its capability of adapting to the environment changes and different users requirements. Aspect Oriented Programming has been used in the design and implementation of adaptive middleware architectures. In this paper, we propose an adaptive middleware architecture which can be used to develop adaptive mobile applications. Such architecture is compared to a component-based architecture from a quantitative perspective. The results suggest that middleware based on this model can be used to build mobile adaptive applications that require only a very small overhead in terms of running time as well as memory footprint while producing code that is more ease to comprehend and modify.
This paper presents a distributed software architecture that allows the cooperation among research institutions in the field of Combinatorial Optimization-DEVOpT: Distributed Evolutionary Optimization Centers. It has as main aims to share existing algorithms for optimization problems, to allow the easy testing of these algorithms with existing instances, to provide fast and better ways to design new algorithms, and to share computational power among the cooperating institutions. This is achieved respecting the autonomy and heterogeneity of the cooperating institutions. The distributed architecture is discussed here and also a case study of a Paxailel Memetic Algorithm to solve the Asymmetric Traveling Salesman Problem (ATSP) running on this environment is analyzed.
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