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This work proposes an energy-efficient resource provisioning and allocationframework to meet the dynamic demands of future applications. The frequentvariations in a cloud user's resource demand lead 'to the problem of excesspower consumption, resource wastage, performance, and Quality-of-Servicedegradation. The proposed framework addresses these challenges by matching theapplication's predicted resource requirement with the resource capacity of VMsprecisely and thereby consolidating the entire load on the minimum number ofenergy-efficient physical machines. The three consecutive contributions of theproposed work are: Online Multi-Resource Feed-forward Neural Network toforecast the multiple resource demands concurrently for future applications;autoscaling of VMs based on the clustering of the predicted resourcerequirements; allocation of the scaled VMs on the energy-efficient PMs. Theintegrated approach successively optimizes resource utilization, saves energyand automatically adapts to the changes in future application resource demand.The proposed framework is evaluated by using real workload traces of thebenchmark Google Cluster Dataset and compared against different scenariosincluding energy-efficient VM placement with resource prediction only, VMPwithout resource prediction and autoscaling, and optimal VMP with autoscalingbased on actual resource utilization. The observed results demonstrate that theproposed integrated approach achieves near-optimal performance against optimalVMP and outperforms rest of the VMPs in terms of power saving and resourceutilization up to 88.5% and 21.12% respectively. In addition, the OM-FNNpredictor shows better accuracy, lesser time and space complexity over atraditional single-input and single-output feed-forward neural networkpredictor.

A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center