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DOI: 10.14569/IJACSA.2013.040211
PDF

Neural Network Solution For Service Level Agreement

Author 1: Sarmad Al-Aloussi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 2, 2013.

  • Abstract and Keywords
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Abstract: Service Oriented Computing is playing an important role in sharing the industry and the way business is conducted and services are delivered and managed. This paradigm is expected to have major impact on service economy; the service sector includes health services, financial services, government services, etc. This involves significant interaction between clients and service providers[1]. This paper is pointed in addressing the problem of enabling Service Level Agreement (SLA) oriented resources allocation in data centers to satisfy competing applications demand for computing services. A QoS report designed to compare performance variables to QoS parameters and indicate when a threshold has been crossed. This paper was suggested a methodology which helps in SLA evaluation and comparison. The methodology was found on the adoption of policies both for service behavior and SLA description and on the definition of a metric function for evaluation and comparison of policies. In addition, this paper contributes a new philosophy to evaluate the agreements between user and service provider by monitoring the measurable and immeasurable qualities to extract the decision by using artificial neural networks (ANN).

Keywords: Service Oriented Architecture; Service Level Agreements; QoS; and Neural Network.

Sarmad Al-Aloussi, “Neural Network Solution For Service Level Agreement” International Journal of Advanced Computer Science and Applications(IJACSA), 4(2), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040211

@article{Al-Aloussi2013,
title = {Neural Network Solution For Service Level Agreement},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040211},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040211},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {2},
author = {Sarmad Al-Aloussi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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