Calculating the minimum bounds of energy consumption for cloud networks

This paper is aiming at facilitating the energy-efficient operation of an integrated optical network and IT infrastructure. In this context we propose an energy-efficient routing algorithm for provisioning of IT services that originate from specific source sites and which need to be executed by suitable IT resources (e. g. data centers). The routing approach followed is anycast, since the requirement for the IT services is the delivery of results, while the exact location of the execution of the job can be freely chosen. In this scenario, energy efficiency is achieved by identifying the least energy consuming IT and network resources required to support the services, enabling the switching off of any unused network and IT resources. Our results show significant energy savings that can reach up to 55% compared to energy-unaware schemes, depending on the granularity with which a data center is able to switch on/off servers

Energy-efficient resource-provisioning algorithms for optical clouds

Rising energy costs and climate change have led to an increased concern for energy efficiency (EE). As information and communication technology is responsible for about 4% of total energy consumption worldwide, it is essential to devise policies aimed at reducing it. In this paper, we propose a routing and scheduling algorithm for a cloud architecture that targets minimal total energy consumption by enabling switching off unused network and/or information technology (IT) resources, exploiting the cloud-specific anycast principle. A detailed energy model for the entire cloud infrastructure comprising a wide-area optical network and IT resources is provided. This model is used to make a single-step decision on which IT end points to use for a given request, including the routing of the network connection toward these end points. Our simulations quantitatively assess the EE algorithm's potential energy savings but also assess the influence this may have on traditional quality-of-service parameters such as service blocking. Furthermore, we compare the one-step scheduling with traditional scheduling and routing schemes, which calculate the resource provisioning in a two-step approach (selecting first the destination IT end point and subsequently using unicast routing toward it). We show that depending on the offered infrastructure load, our proposed one-step calculation considerably lowers the total energy consumption (reduction up to 50%) compared to the traditional iterative scheduling and routing, especially in low-to medium-load scenarios, without any significant increase in the service blocking

Layer-by-Layer Assembly of Clay-Carbon Nanotube Hybrid Superstructures

Much of the research effort concerning layered materials is directed toward their use as building blocks for the development of hybrid nanostructures with well-defined dimensions and behavior. Here, we report the fabrication through layer-by-layer deposition and intercalation chemistry of a new type of clay-based hybrid film, where functionalized carbon nanotubes are sandwiched between nanometer-sized smectite clay platelets. Single-walled carbon nanotubes (SWCNTs) were covalently functionalized in a single step with phenol groups, via 1,3-dipolar cycloaddition, to allow for stable dispersion in polar solvents. For the production of hybrid thin films, a bottom-up approach combining self-assembly with Langmuir-Schaefer deposition was applied. Smectite clay nanoplatelets act as a structure-directing interface and reaction media for grafting functionalized carbon nanotubes in a bidimensional array, allowing for a controllable layer-by-layer growth at a nanoscale. Hybrid clay/SWCNT multilayer films deposited on various substrates were characterized by X-ray reflectivity, Raman, and X-ray photoelectron spectroscopies, as well as atomic force microscopy

Multi-branch convolutional neural network for identification of small non-coding RNA genomic loci

Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.peer-reviewe

Cdc6 expression represses E-cadherin transcription and activates adjacent replication origins

The Cdc6 replication licensing factor acts as a molecular switch at the E-cadherin locus, leading to E-cadherin transcriptional repression and local activation of replication