A novel virtual machine deployment algorithm with energy efficiency in cloud computing

In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

Kinetic energy(KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density.

Energy-Performance Tradeoffs in laaS Cloud with Virtual Machine Scheduling

In the cloud data centers,how to map virtual machines(VMs) on physical machines(PMs) to reduce the energy consumption is becoming one of the major issues,and the existing VM scheduling schemes are mostly to reduce energy consumption by optimizing the utilization of physical servers or network elements.However,the aggressive consolidation of these resources may lead to network performance degradation.In view of this,this paper proposes a two-stage VM scheduling scheme:(1) We propose a static VM placement scheme to minimize the number of activating PMs and network elements to reduce the energy consumption;(2) In the premise of minimizing the migration costs,we propose a dynamic VM migration scheme to minimize the maximum link utilization to improve the network performance.This scheme makes a tradeoff between energy efficiency and network performance.We design a new twostage heuristic algorithm for a solution,and the simulations show that our solution achieves good results.

Energy-Efficient Framework for Virtual Machine Consolidation in Cloud Data Centers

With the advent of the era of cloud computing, the high energy consumption of cloud computing data centers has become a prominent problem, and how to reduce the energy consumption of cloud computing data center and improve the efficiency of data center has become the research focus of researchers all the world. In a cloud environment, virtual machine consolidation(VMC) is an effective strategy that can improve the energy efficiency. However, at the same time, in the process of virtual machine consolidation, we need to deal with the tradeoff between energy consumption and excellent service performance to meet service level agreement(SLA). In this paper, we propose a new virtual machine consolidation framework for achieving better energy efficiency-Improved Underloaded Decision(IUD) algorithm and Minimum Average Utilization Difference(MAUD) algorithm. Finally, based on real workload data on Planet Lab, experiments have been done with the cloud simulation platform Cloud Sim. The experimental result shows that the proposed algorithm can reduce the energy consumption and SLA violation of data centers compared with existing algorithms, improving the energy efficiency of data centers.

Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power stations.This study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead.The proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting models.Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all seasons.The proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction horizons.However,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results.This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.