Improving QoS in cloud resources scheduling using dynamic clustering algorithm and SM-CDC scheduling model

Abstract

Quality of Service (QoS) regulates and controls network resources by setting priorities for specific data types. Many clustering algorithms are used to cluster cloud workloads, most of which are static. However, the lack of dynamic algorithms is seen in the face of huge databases that are real-time and according to the existing clustering conditions. Additionally, fair allocation of tasks on servers and efficient resource utilization pose challenges. In this research, two solutions are proposed to improve the quality of service: the first solution uses the chameleon dynamic algorithm, a method to improve service quality. The chameleon algorithm has been able to show significant performance due to its high accuracy in detecting the smallest distance between clusters. This dynamic algorithm outperforms static algorithms with classification accuracy and response speed, which are the most important parameters of service quality. The second part of the proposed solution is to use the Scheduling Model using Cloud Data Centers (SM-CDC) system to select the best service provider based on the clustering done in the previous step. A SM-CDC technique is developed to handle cloud storage center tasks that are stored in electronic devices. According to the comparison with existing scheduling policies, SM-CDC offered 36% decrease on response time, 50% reduction on cost of resources, and 40% improvement on QoS Satisfaction.