Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy consumption.To address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is proposed.The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.

Random Amplified Polymorphic DNA (RAPD) Markers on Genomic DNA Polymorphism in 8 Main Grasshoppers in Inner Mongolia

[ Objective] The relationship between the genetic evolution and phylogenesis of the main grasshopper species in Inner Mongolia grasslands in molecular level was studied. [ Method] Random amplified polymorphic DNA （RAPD） technique was used to amplify the 80 individuals of 8 grasshoppers （4 families, 6 genera） in Acridoidea, the polymorphisms of their genomic DNA were compared. [ Result] 64 specific fragments were amplified by 7 primers with the molecular weight of 300 -2 000 bp. The genetic distance between 8 grasshoppers was 0.228 2 -0.589 6. Band pat- tern showed that polymorphism was commonly existed in different genus within the same family and different species within the same genus. The resuits were conducted UPGMA cluster analysis according to Neis＇ genetic distance, the results showed that the species within the same genus first clustered together, then the species in the same family clustered together. [ Condusloa] The study could provide molecular biological basis for system development and evolution research of main grasshoppers in Inner Mongolia grassland.

An Effective Image Retrieval Mechanism Using Family-based Spatial Consistency Filtration with Object Region

How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.