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

Abstract This work proposes an energy-efficient resource provisioning and allocation framework to meet dynamic demands of the future applications. The frequent variations in a cloud user’s resource demand leads to the problem of an excess power consumption, resource wastage, performance and Quality-of-Service (QoS) degradation. The proposed framework addresses these challenges by matching the application’s predicted resource requirement with resource capacity of VMs precisely and thereby consolidating entire load on the minimum number of energy-efficient physical machines (PMs). The three consecutive contributions of the proposed work are: (1) Online Multi-Resource Feed-forward Neural Network (OM-FNN) to forecast the multiple resource demands concurrently for the future applications, (2) autoscaling of VMs based on the clustering of the predicted resource requirements, (3) 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 (VMP) 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, OM-FNN predictor shows better accuracy, lesser time and space complexity over a traditional single-input and single-output feed-forward neural network (SISO-FNN) predictor.