Towards Virtual Machine Energy-Aware Cost Prediction in Clouds (original) (raw)

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

Computers, Materials & Continua, 2021

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.

Energy Prediction for Cloud Workload Patterns

Economics of Grids, Clouds, Systems, and Services

The excessive use of energy consumption in Cloud infrastructures has become one of the major cost factors for Cloud providers to maintain. In order to enhance the energy efficiency of Cloud resources, proactive and reactive management tools are used. However, these tools need to be supported with energyawareness not only at the physical machine (PM) level but also at virtual machine (VM) level in order to enhance decision-making. This paper introduces an energy-aware profiling model to identify energy consumption for heterogeneous and homogeneous VMs running on the same PM and presents an energy-aware prediction framework to forecast future VMs energy consumption. This framework first predicts the VMs' workload based on historical workload patterns using Autoregressive Integrated Moving Average (ARIMA) model. The predicted VM workload is then correlated to the physical resources within this framework in order to get the predicted VM energy consumption. Compared with actual results obtained in a real Cloud testbed, the predicted results show that this energyaware prediction framework can get up to 2.58 Mean Percentage Error (MPE) for the VM workload prediction, and up to-4.47 MPE for the VM energy prediction based on periodic workload pattern.

Prediction Model for Virtual Machine Power Consumption in Cloud Environments

Procedia Computer Science, 2016

Power consumption has become a crucial issue in cloud computing environments because of environmental and financial concerns. It is necessary to estimate individual virtual machine power consumption to enforce efficient power aware policies in cloud. Existing solutions are built on linear power models to infer power consumption through VM resource utilization. However, linear models do not capture dependencies among multiple parameters and hence they do not ensure prediction accuracy across multiple workloads. In this paper, a non-linear support vector regression based power model using performance monitor counters is proposed to predict individual virtual machine power consumption. Experimental results with various standard benchmark workloads demonstrate that the prediction accuracy of proposed approach is better than the existing linear regression based power model.

A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption

Journal of Grid Computing, 2018

Cloud Computing has revolutionized the software, platform and infrastructure provisioning. Infrastructure-as-a-Service (IaaS) providers offer ondemand and configurable Virtual Machine (VMs) to tenants of cloud computing services. A key consolidation force that widespread IaaS deployment is the use of pay-as-you-go and pay-as-you-use cost models. In these models, a service price can be composed of two dimensions: the individual consumption, and a proportional value charged for service maintenance. A common practice for public providers is to dilute both capital and operational costs on predefined pricing sheets. In this context, we propose PSVE (Proportional-Shared Virtual Energy), a cost model for IaaS providers based on CPU energy consumption.

Performance and Energy-Based Cost Prediction of Virtual Machines Auto-Scaling in Clouds

2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2018

Virtual Machines (VMs) auto-scaling is an important technique to provision additional resource capacity in a Cloud environment. It allows the VMs to dynamically increase or decrease the amount of resources as needed in order to meet Quality of Service (QoS) requirements. However, the auto-scaling mechanism can be time-consuming to initiate (e.g. in the order of a minute), which is unacceptable for VMs that need to scale up/out during the computation, besides additional costs due to the increase of the energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs auto-scaling by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the auto-scaling workload, power consumption and total cost for heterogeneous VMs, with a cost-saving of up to 25% for the predicted total cost of VM self-configuration as compared to the current approaches in literature.

Energy Consumption-based Pricing Model for Cloud Computing

2016

Pricing mechanisms employed by di erent service providers significantly influence the role of cloud computing within the IT industry. The purpose of this paper is to investigate how di erent pricing models influence the energy consumption, performance and cost of cloud services. Therefore, we propose a novel Energy-Aware Pricing Model that considers energy consumption as a key parameter with respect to performance and cost. Experimental results show that the implementation of the Energy- Aware Pricing Model achieves up to 63.3% reduction of the total cost as compared to current pricing models like those advertised by Rackspace.

Modeling virtualized services for optimization of energy consumption in cloud computing

Communications on Advanced Computational Science with Applications, 2017

Cloud computing gives the system administrators the ability to replace the operations which use physical hardware with virtualized services. Service cost calculation or accounting method in cloud computing must be dynamic enough to calculate the billing information for each user based on the computational resources used during each session. In this paper, a model for estimation of consumed energy in virtualized services and calculation of billing information for users in the cloud computing environment is presented. The proposed method estimates the consumed energy by each virtual machine based on physical characteristics of the hardware. The experimental results show that the proposed model is able to estimate the consumed energy with errors lower than 5%.

Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds

Proceedings of the 8th International Conference on Cloud Computing and Services Science, 2018

Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energybased Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.