Improving QoS in cloud resources scheduling using Chameleon Dynamic Clustering Algorithm

QoS (Quality of Service) controls network resources by setting priorities for a specific type of configuration. There are many clustering algorithms for cloud workload clustering, most of which are static. In fact, according to a dynamic algorithm, in the face of huge databases that are seen in real-time and according to the existing conditions of clustering. Speed ​​and accuracy are some of the most important factors that the proposed method should include. To solve this challenge, one of the most important components of service quality is the use of resources. In this article, it is proposed to use the Chameleon algorithm, which performs clustering based on pairs of neighborhood similarity components and dynamic clustering capability. The Administration Command of the Islamic Republic of Iran (Farja) is in contact with a large amount of information in various social, security, economic and cultural fields in order to operate in the field of order and security. The results of accurate and dynamic clustering patterns from the huge databases of this organization can greatly improve the quality of dealing with this information. The science of data mining and the use of its clustering techniques is essential for information analysis. Correct analysis, correct positioning, and correct receiving of information at any moment will lead to the correct result. Well, dynamism in clustering and knowing the correct situation at any moment can also lead to security. By comparing the Chameleon algorithm with the Gaussian algorithm, 36% improvement in time can be observed. Also, the resource consumption of Chameleon algorithm has decreased by 50% compared to Gaussian algorithm. Therefore, it can be concluded that the proposed method has the dynamic cluster in the shortest time due to reducing the use of resources