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Time Series Forecasting of Cloud Data Center
Workloads for Dynamic Resource Provisioning The University of Texas at San Antonio One UTSA Circle, San Antonio, TX 78249, USA Abstract 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 reduces 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 forecasting
and rolling forecast origin cross-validation. Keywords: Cloud Computing, Workload Forecasting,
Forecasting Models +: Corresponding author: Ram Krishnan Journal of
Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
(JoWUA), Vol. 6, No. 3, pp. 87-110, September 2015 [pdf] |