A Hybrid Self-Adaptive PSO and QoS Based Machine Learning Model for Cloud Service Data - Sirisha Potluri , Katta Subba Rao (International Journal of Control and Automation, 2020)
All the users present in the cloud expect services with high quality since they pay for those services. To provide expected quality of service to the users cloud providers need to maintain sufficient resources. To manage these resources in an efficient manner and in order to maintain the QoS, a capable scheduling algorithm is required. To address this issue, a self-adaptive based allocation of various resources can be used, which has the ability to change the resource allocation dynamically according to the different conditions in the cloud environment. There are many such methods and approaches available in literature in which a set of rules are defined with respect to each service to take better decisions for efficient resource allocation. Self-adaptive based allocation of resources can be achieved by using machine learning approach and control theory. In this paper we propose a hybrid self-adaptive PSO and QoS based machine learning model for cloud service data. The proposed model has three modules. The first module contains an improved quality of service-based cloud service ranking and recommendation model to find the essential top ranked services in the cloud environment. The second module contains a hybrid PSO optimization model integrated with recommendation and ranking model for efficient task scheduling in cloud computing environment. In the third module we proposed a hybrid self-adaptive PSO and QoS based machine learning model for cloud service data. We have compared the performance of the existing algorithms with the proposed algorithm based on average accuracy of each task, average error rate of each task, task level runtime for response time prediction and job level runtime for response time prediction. During every comparison the proposed algorithm is giving better results.