Energy Consumption Acquisition And Prediction Method For Cloud Computing Services

Cloud computing as the latest computing paradigm has shown its promising future in business systems facing massive concurrent user requests and complicated computing tasks. With the fast growth of cloud data centers, energy management especially energy monitoring and saving in cloud based systems has been attracting increasingly more attentions.Cloud services run on the virtual machines hosted in the physical machines of cloud datacenters. Under different resource management strategies, the energy consumed by cloud services and virtual machines have a large difference. However, existing energy management strategies mainly monitor the virtual machines instead of the cloud services running on them, and hence it is difficult to directly monitor and optimize the energy consumption of cloud services. To address such an issue, in this paper, we propose an effective energy testing framework for cloud services.This framework can help to accurately test and analyze the baseline energy of physical and virtual machines in the cloud environment, and then obtain the energy consumption data of cloud services. Based on these data, we can further produce the energy consumption model and design energy prediction strategies. Our experiments are conducted in an OpenStack based cloud computing environment. The effectiveness of our framework has been successfully verified through a detailed case study and a set of energy modelling and prediction experiments based on representative time-series models.