Resources performance forecasting constitutes one of particularly significant research problems in distributed computing. To ensure an adequate use of the computing resources in a metacomputing environment, there is a need for effective and flexible forecasting method to determine the available performance on each resource. In this paper, we present a modeling approach to estimating the future value of CPU load. This modeling prediction approach uses the combination of Adaptive Network-based Fuzzy Inference Systems (ANFIS) and the clustering process applied on the CPU Load time series. Experiments show the feasibility and effectiveness of this approach that achieves significant improvement and outperforms the existing CPU load prediction models reported in literature.

This paper describes an embedded minutia-based matching algorithm using the reference point neighborhoods minutiae. The proposed matching algorithm is implemented in restricted environments such as smart card devices requiring careful monitoring of both memory and processing time usage. The proposed algorithm uses a circular tessellation to encode fingerprint features in neighborhood minutia localization binary codes. The objective of the present study is the development of a new matching approach which reduces both computing time and required space memory for fingerprint matching on Java Card. The main advantage of our approach is avoiding the implicit alignment of fingerprint images during the matching process while improving the fingerprint verification accuracy. Tests carried out on the public fingerprint databases DB1-a and DB2-a of FVC2002 have shown the effectiveness of our approach.

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National audienceFingerprint matching is one of the most important problems in an Automatic Fingerprint Identification System. The Fingerprint matching is a high computational task, repetitive and arduous. Moreover, fingerprint databases are by nature large scale. Indeed, there is a need for flexible and independent solution for automatic distribution of fingerprint matching. So, this paper introduces an agent-based distributed matching system. The matching task distributed on a available local Network resources with an optimal way in order to ensure a fast matching process in large scale fingerprint databases. The distribution workload is ensured by our developed middleware allowing the monitoring and prediction of unexploited network computing resources, and designed for the implementation of intensive calculation services