@article {110,
	title = {Multi-objective mapping for NoC architectures},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2007},
	pages = {378 - 384},
	abstract = {In order to improve the performance of current embedded systems, Network -on-Chip (NoC) offers many advantages, especially in terms of flexibility and low cost. Applications require more and more intensive computations, especially multimedia applications such as video encoding. Developing product and application using such an architecture offers many challenges and opportunities. Many tools will be required to develop a NoC architecture for a specific application. A toot which can map and schedule an application or a set of applications to a given NoC architecture will be essential and must be able to satisfy many relative trade-offs (real-time, performance, low power consumption, time to market, re-usability, cost, area, etc). In this paper, we survey most approaches used to solve this problem with different goals. We then describe our approach based on a genetic algorithm and its implementation and its design objective.},
	keywords = {Genetic algorithms, Mapping objective, MPSoC, Network-on-chip, Optimization},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-49149093560\&partnerID=40\&md5=0f2dbcdf8982dc893dbcaccefdebbc45},
	author = {Benyamina, A. El H. and Boulet, P.}
}
@article {67,
	title = {Sensitivity analysis of server placement on enterprise network topology through soft computing},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {1 - 7},
	abstract = {We analyze the consequences when both the server placement problem and network topology problem are solved concurrently through soft computing. Both problems are formulated as a combined optimization problem, subject to a set of design and performance constraints while minimizing the enterprise network cost. We have coded the combined optimization problem within a soft computing methodology, which is based on a probabilistic genetic program for automatically searching the design space for good network topologies. The experimental results for synthesizing and optimizing 3-level enterprise network (65 user nodes) for an edge-server placement has demonstrated the effectiveness of our methodology in finding good solutions with a static workload in less than five minutes.},
	keywords = {Analysis, Clustering, Edge-server placement, Network topology, Optimization, Soft computing, Web structure analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33947320448\&partnerID=40\&md5=62ced3281abb66c04e4ac8f00c808c7b},
	author = {Safar, M. and Habib, S.}
}
