Energy-Efficient Algorithm for Load Balancing and VMs Reassignment in Data Centers

International audienceNowadays, cloud computing is an effective solution for providing computing services to consumers. However, data centers that host computing resources are still faced with a misuse of resources and a workload imbalance of servers, where some servers become overloaded while others are underloaded or even idle. This results in performance degradation and resource wastage. The load balancing is a key aspect and has an important role in the management of cloud data centers. It allows an optimal use of the resources and improves the desired Quality of Service (QoS) using optimal methods for allocating resources and distributing workload. In this paper, we propose a load-balancing algorithm that is based on a new parameter called the balance factor of the data center, introduced here, to determine if a data center is imbalanced or not, in order to redistribute the workload equally over all the hosts. To minimize the energy consumption of the data center, our strategy relies on the live migration of virtual machines (VMs) while using a mathematical model. The simulation results, using the CloudSim toolkit, have shown that the energy efficiency can be managed by reassigning VMs to the data-center hosts

Glasshouse behaviour of eight trangenic potato clones with a modified nitrate reductase espression under two fertilization regimes

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A New Scalable and Energy Efficient Algorithm for VMs Reassignment in Cloud Data Centers.

International audienc

Toward an efficient gene transfer in grain legumes for fundamental studies

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Toward an efficient gene transfer in grain legumes for fundamental studies

National audienc

CPU-based prediction with Self Organizing Map in Dynamic Cloud Data Centers

International audienceThe major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to the dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee an effective resource provisioning. To tackle these challenges, the future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on the CPU utilization since it is considered as a leading cause of the Quality of Service (QoS) dropping. The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps (SOM) that tries to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that SOM known for its strength in classification is also effective for prediction

Flow cytometric analysis and molecular characterization of Agrobacterium tumefaciens-mediated transformants of Medicago truncatula

Sur la publication, l'auteur S. Djennane est mal orthographié (Djenanne).International audienceLeaf explants from leaflets collected from either in vivo grown or in vitro grown seedlings of Medicago truncatula genotype R108-1 were co-cultivated with bacterial cells of Agrobacterium tumefaciens strains EHA105 or C58pMP90. Each of these strains was carrying the pCambia 1390 plasmid harbouring a hygromycin resistance gene cassette. Explants were then incubated on a medium containing 10 mg/l hygromycin and 800 mg/l augmentin to suppress Agrobacterium growth, and subcultured 4-5 times every 2 weeks for the proliferation of calli. After 8-10 weeks, callusing explants were transferred to hormone-free medium with 10 mg/l hygromycin and 400 mg/l augmentin for shoot regeneration. After rooting, a total of about 300 putative transformants were grown into plantlets, transferred to soil, acclimatized, and then moved to the greenhouse. Of these, a total of 43 independent PCR positive primary transformants and their T1 and T2 progeny were subjected to flow cytometric analysis, to assessing their trueness-to-type, as well as to southern blot analysis

Assessing the impact of transgenic grapevine rootstocks on the environment using NGS

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Production of transgenic pear plants expressing ferritin gene with the aim to reduce fire blight susceptibility

International audienceFire blight caused by the bacteria Erwinia amylovora is one of the most important diseases of pear. This causal agent produces a siderophore (desferrioxamine), which was identified as one of its virulence factors. This protein enables the bacteria to overcome conditions of iron limitation encountered in host tissues, and may also protect the bacteria against active oxygen species. Previous experiments indicate that the use of an iron chelator protein, encoded by the bovine lactoferrin gene, reduces fire blight symptoms in some transgenic pear clones. The aim of the present work is to test the ability of a more efficient iron chelator, plant ferritin, to reduce fire blight susceptibility in pear. In the literature, ferritin genes have been overexpressed under the control of a constitutive promoter in different transgenic plant species for various purposes. In several cases, the constitutive expression of ferritin produced negative effects such as reduced growth and chlorophyll content. Therefore, we decided to place the exogenous ferritin gene from pea under the control of a pathogen inducible promoter (sgd24) in comparison with a constitutive promoter (CaMV 35S). Two pear varieties, 'Conference' (CF) and 'Passe-Crassane' (PC) were transformed using both constructs. Transformation rates depended on variety and construct. They were respectively of 12 and 4.3% for CF and PC using the sgd24-ferritin construct. Only PC was transformed with the 35S-ferritin construct, with a transformation rate of 2%. First analyses of the transgenic clones by RT-PCR showed the expression of pea ferritin in both constructs and in all clones. The transgenic clones were acclimatized in greenhouse and exhibited normal growth. Quantification of ferritin gene expression, ferritin protein accumulation, and evaluation of fire blight resistance are underway

Introduction and expression of a deregulated tobacco nitrate reductase gene in potato lead to highly reduced nitrate levels in transgenic tubers

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