@article{Sirisha Potluri , Katta Subba Rao_2020, title={A Hybrid Self-Adaptive PSO and QoS Based Machine Learning Model for Cloud Service Data}, volume={13}, url={http://sersc.org/journals/index.php/IJCA/article/view/11539}, abstractNote={<p>All the users present in the cloud expect services with high quality since they pay for those<br>services. To provide expected quality of service to the users cloud providers need to<br>maintain sufficient resources. To manage these resources in an efficient manner and in<br>order to maintain the QoS, a capable scheduling algorithm is required. To address this<br>issue, a self-adaptive based allocation of various resources can be used, which has the<br>ability to change the resource allocation dynamically according to the different<br>conditions in the cloud environment. There are many such methods and approaches<br>available in literature in which a set of rules are defined with respect to each service to<br>take better decisions for efficient resource allocation. Self-adaptive based allocation of<br>resources can be achieved by using machine learning approach and control theory. In<br>this paper we propose a hybrid self-adaptive PSO and QoS based machine learning<br>model for cloud service data. The proposed model has three modules. The first module<br>contains an improved quality of service-based cloud service ranking and recommendation<br>model to find the essential top ranked services in the cloud environment. The second<br>module contains a hybrid PSO optimization model integrated with recommendation and<br>ranking model for efficient task scheduling in cloud computing environment. In the third<br>module we proposed a hybrid self-adaptive PSO and QoS based machine learning model<br>for cloud service data. We have compared the performance of the existing algorithms with<br>the proposed algorithm based on average accuracy of each task, average error rate of<br>each task, task level runtime for response time prediction and job level runtime for<br>response time prediction. During every comparison the proposed algorithm is giving<br>better results.</p&gt;}, number={2s}, journal={International Journal of Control and Automation}, author={Sirisha Potluri , Katta Subba Rao}, year={2020}, month={Apr.}, pages={36 - 50} }