  This work proposes an energy-efficient resource provisioning and allocation
framework to meet the dynamic demands of future applications. The frequent
variations in a cloud user's resource demand lead 'to the problem of excess
power consumption, resource wastage, performance, and Quality-of-Service
degradation. The proposed framework addresses these challenges by matching the
application's predicted resource requirement with the resource capacity of VMs
precisely and thereby consolidating the entire load on the minimum number of
energy-efficient physical machines. The three consecutive contributions of the
proposed work are: Online Multi-Resource Feed-forward Neural Network to
forecast the multiple resource demands concurrently for future applications;
autoscaling of VMs based on the clustering of the predicted resource
requirements; allocation of the scaled VMs on the energy-efficient PMs. The
integrated approach successively optimizes resource utilization, saves energy
and automatically adapts to the changes in future application resource demand.
The proposed framework is evaluated by using real workload traces of the
benchmark Google Cluster Dataset and compared against different scenarios
including energy-efficient VM placement with resource prediction only, VMP
without resource prediction and autoscaling, and optimal VMP with autoscaling
based on actual resource utilization. The observed results demonstrate that the
proposed integrated approach achieves near-optimal performance against optimal
VMP and outperforms rest of the VMPs in terms of power saving and resource
utilization up to 88.5% and 21.12% respectively. In addition, the OM-FNN
predictor shows better accuracy, lesser time and space complexity over a
traditional single-input and single-output feed-forward neural network
predictor.
