Currently, cloud computing has received great attention in commerce and scientific research due to its flexibility and strong data processing capability. However, in view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this article, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of proposed method consists of three core components: tasks clustering, the amount of tasks prediction in cluster, dynamic resource provisioning and scheduling. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster to meet their performance requirements. We implement the experiments in Google cloud traces dataset and the results show that our method achieves 92.3, 91.2 percent, and 3679.2 kW$ \cdot$·h respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

The cloud computing technology offers consistent access to large-scale computing capabilities, thereby bringing convenience to life. However, the virtualized cloud systems are still too vulnerable to maintain performance scalability and service agility once a task burst surges in without any warning. A mounting account of research has been conducted on proper strategies for accurate workload prediction as well as effective resource reservation and arrangement, but commonly cloud providers seek help to strategies that deploy excessive resources, adding overhead cost and sacrificing the cloud's advantage of scalability, or otherwise fail to reconfigure timely and properly, causing dissatisfaction and even financial loss, which are not expected by both cloud providers and clients.