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 thoseservices. To provide expected quality of service to the users cloud providers need tomaintain sufficient resources. To manage these resources in an efficient manner and inorder to maintain the QoS, a capable scheduling algorithm is required. To address thisissue, a self-adaptive based allocation of various resources can be used, which has theability to change the resource allocation dynamically according to the differentconditions in the cloud environment. There are many such methods and approachesavailable in literature in which a set of rules are defined with respect to each service totake better decisions for efficient resource allocation. Self-adaptive based allocation ofresources can be achieved by using machine learning approach and control theory. Inthis paper we propose a hybrid self-adaptive PSO and QoS based machine learningmodel for cloud service data. The proposed model has three modules. The first modulecontains an improved quality of service-based cloud service ranking and recommendationmodel to find the essential top ranked services in the cloud environment. The secondmodule contains a hybrid PSO optimization model integrated with recommendation andranking model for efficient task scheduling in cloud computing environment. In the thirdmodule we proposed a hybrid self-adaptive PSO and QoS based machine learning modelfor cloud service data. We have compared the performance of the existing algorithms withthe proposed algorithm based on average accuracy of each task, average error rate ofeach task, task level runtime for response time prediction and job level runtime forresponse time prediction. During every comparison the proposed algorithm is givingbetter results.