A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

Abstract

With the striking rise in penetration of Cloud Computing, energy consumption is considered as one of the key cost factors that need to be managed within cloud providers' infrastructures. Subsequently, recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources, where the energy consumption and the operational costs are minimized. However, to make better cost decisions in these strategies, the performance and energy awareness should be supported at both Physical Machine (PM) and Virtual Machine (VM) levels. Therefore, in this paper, a novel hybrid approach is proposed, which jointly considered the prediction of performance variation, energy consumption and cost of heterogeneous VMs. This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance, in which the power consumption and resource usage are utilized for estimating the VMs' total cost. Specifically, the service performance variation is handled by detecting the underloaded and overloaded PMs; thereby, the decision(s) is made in a cost-effective manner. Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation, with a high prediction accuracy on the basis of historical workload patterns.

A Systematic Review of Live VM Migration Techniques for Energy Management

“…The paper also presented the comparison analysis of all algorithms. Aldossary, M. in (50) proposed an approach to predict performance variation, energy consumption and cost of VMs.…”

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

“…Kanwal, S. [26] presented "Genetic Algorithm (GA) based leader election (GLEA)" approach to analyze the nodes in cloud data centers and found the approach to be memory efficient with minimum execution time. A hybrid approach "Integration of VMs Auto-Scaling with Live Migration" was proposed in [27] to combine auto-scaling and live-migration to calculate the total cost of dynamic VMs. This approach is also able to predict the VM workload for better performance.…”