The research of QoS monitoring-based cloud service selection

Abstract

With the rapid development of cloud computing, more and more consumers adopt third-party cloud services to implement their critical business. Cloud services are provided by different service providers, and usually have different cost performance and quality of service. Service consumers have to make trade-offs on multiple factors in order to choose the appropriate cloud services. Therefore, an efficient cloud service selection mechanism makes sense for both cloud service providers and consumers. According to existing works, this research field still requires appropriate models, effective design paradigm, in-depth experimentations and practical implementations. Because agent can identify the paradigm of the clouds by learning algorithm, they can be trained to observe the differences and behave flexibly for cloud service selection. To rank different clouds, we propose and assign QoS factor for each cloud service and ranks it as whole. According to simulation experiment, we validate the approach, which emphasizes the need to rank cloud services of widely spreading and complex domains.