Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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    Autonomic cloud computing: Self management in cloud computing
    (ICIC Express Letters Office icicel@ijicic.org, 2014) Anithakumari, S.; Chandrasekaran, K.
    Cloud computing presents an innovative computing paradigm in which computational power is provided as a service utility similar to electricity. The enhancing dynamism, heterogeneity and interactivity in software services, applications and networks leads to complex and unmanageable systems in cloud environment. This difficulty can be addressed by utilizing self managing computing model such as autonomic computing for cloud service provisioning. The collaboration of cloud and autonomic computing gives rise to anew form of computing service called autonomic cloud service. Without autonomic techniques, efficient monitoring and management of current cloud systems become impossible because the scale of such systems is increasing day by day. This paper gives a brief review of technologies which lead to Autonomic Cloud Computing and also discusses some services, applications and case studies in Autonomic Clouds. © 2014 ICIC International.
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    Ensemble deep neural network based quality of service prediction for cloud service recommendation
    (Elsevier B.V., 2021) Sahu, P.; Raghavan, S.; Chandrasekaran, K.
    Applications of Cloud Services are increasing day by day, and so is the difficulty of choosing the best-suited service for a customer. Quality of Service (QoS) parameters can be used for quality assurance and evaluation; further, a service can be recommended based on these QoS parameters’ values. Recommendation systems are getting much attention lately. It has a crucial role in almost all the major commercial platforms and many improvements are being made to make the recommendations more precise and closer to the user's requirements. Conventional Machine Learning algorithms and statistical analysis methods, presently are not that efficient in learning the complex correlation between data elements. Lately, Deep Learning models have proven to be practical and precise in areas like natural language processing, image processing, data mining, & data interpretation. However, there are not many examples of complete Deep Learning applications for cloud service recommendation systems, though some works partially use Deep Learning. We propose the Ensemble of Deep Neural Networks (EDNN) method, which is of the hybrid type, i.e., the fusion of neighborhood-based and neural network model-based methods. The output obtained from both the models are combined using another different neural network model. Our approach for predicting QoS values is simple and different from previous works, and the results show that it outperforms other classical methods marginally. © 2021 Elsevier B.V.