期刊名称：Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
印刷版ISSN：2093-5374
电子版ISSN：2093-5382
出版年度：2015
卷号：6
期号：3
页码：87-110
出版社：Innovative Information Science & Technology Research Group
摘要：Cloud computing offers on-demand, elastic resource provisioning that allows an enterprise to provide services to their customers at an acceptable quality while consuming only the requisite computing resources as a utility. Since cloud computing resources scale elastically, utilizing cloud computing re- duces the risk of over-provisioning, wasting resources during non-peak hours, and reduces the risk of under-provisioning, missing potential customers. By using an automated resource scaling algorithm, a system implemented using cloud services can fully exploit the benefits of on-demand elasticity. A simple reactive scaling algorithm, resource scaling is triggered after some monitored metric has crossed a threshold, suffers from the fact that cloud computing workloads can varying widely over time and a scalable application needs time to perform the triggered scaling action. Instead, resources can be proactively requested by forecasting future resource demand values based on demand history. Obtaining accurate prediction results is crucial to the efficient operation of an automated resource scaling algorithm. In this work, several forecasting models are evaluated for their applicability in forecasting cloud computing workloads. These forecasting methods were compared for their ability to forecast real cloud computing workloads including Google cluster data and Intel Netbatch logs. Two tests are performed to evaluate the accuracy of each forecasting model: out-of-sample forecast- ing and rolling forecast origin cross-validation
