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.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2015.061002
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 10, 2015.
Abstract: Several Big data services have been developed on the cloud to meet increasingly complex needs of users. Most times a single Big data service may not be capable in satisfying user requests. As a result, it has become necessary to aggregate services from different Big data providers together in order to execute the user's request. This in turn has posed a great challenge; how to optimally compose services from a given set of Big data providers without affecting if not optimizing Quality of Service (QoS). With the advent of cloud-based Big data applications composed of services spread across different network environments, QoS of the network has become important in determining the true performance of composite services. However current studies fail to consider the impact of QoS of network on composite service selection. Therefore a novel network-aware genetic algorithm is proposed to perform composition of Big data services in the cloud. The algorithm adopts an extended QoS model which separates QoS of network from service QoS. It also uses a novel network coordinate system in finding composite services that have low network latency without compromising service QoS. Results of evaluation indicate that the proposed approach finds low latency and QoS-optimal compositions when compared with current approaches.
Umar SHEHU, Ghazanfar SAFDAR and Gregory EPIPHANIOU, “Towards Network-Aware Composition of Big Data Services in the Cloud” International Journal of Advanced Computer Science and Applications(IJACSA), 6(10), 2015. http://dx.doi.org/10.14569/IJACSA.2015.061002