VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10–20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.     

Mechanisms for SLA provisioning in cloud-based service providers

A challenge in cloud resource management is to design self-adaptable solutions capable to react to unpredictable workload fluctuations and changing utility principles. This paper analyzes the problem from the perspective of an Application Service Provider (ASP) that uses a cloud infrastructure to achieve scalable provisioning of its services in the respect of QoS constraints.First we draw a taxonomy of IaaS provider and use the identified features to drive the design of four autonomic service management architectures differing on the degree of control an ASP have on the system. We implemented two of this solutions and related mechanism to test five different resource provisioning policies. The implemented testbed has been evaluated under a realistic workload based on Wikipedia access traces on Amazon EC2 platform.The experimental evaluation performed confirms that: the proposed policies are capable to properly dimension the system resources making the whole system self-adaptable respect to the workload fluctuation. Moreover, having full control over the resource management plan allow to save up to the 32% of resource allocation cost always in the respect of SLA constraints.     

P distribution in different sediment fraction of a constructed wetland

The aim of this work was to study the accumulation and fractionation of P in the inlet and outlet sediment of a constructed wetland for the wastewater treatment of a metallurgic plant in Argentina. It was important to predict whether P could be released into the water again by changing environmental conditions or retained over time. P-fractionation was performed using a sequential extraction method. Sediment cores were sliced at depths of: 0-3; 3-7 and 7-10 cm. Sediment showed high pH values and anoxic conditions. In the inlet area, P was principally bound to the carbonate fraction, whereas in the outlet area, it was mainly bound in the residual fraction. This behavior was justified by the effluent composition, which is rich in Ca2+ and Fe3+ and presents high values of pH and conductivity. These conditions favor CaCO3 and Fe(OOH)n precipitation and the subsequent sorption of P to their surface. The sediment active layer involved in the exchange reactions was the superficial one (0-3 cm). The wetland is highly efficient in P retention. P was retained by sediment in fractions that will not release it to the water while chemical and environmental conditions of the system are maintained.     

The Influence of the Internet on European Journalism

This study investigates how European journalists evaluate the changes that have occurred in their profession since the Internet has been integrated in newsrooms. How do journalists perceive the features and innovations associated with the Internet? What are the principal changes in the profession? Do practitioners believe that the quality of journalism has been raised or lowered? To answer to these research questions, we carried out a survey across 11 European countries–Cyprus, Estonia, Finland, Greece, Ireland, Italy, Lithuania, United Kingdom, Slovenia, Spain, Sweden–of 239 journalists, working for 40 of the most‐read print/online news outlets in these countries. The survey shows that the opportunity to use the Internet to reinforce the social functions of journalism has not been fully recognised.     

A static view of localities

This paper proposes alternative, effective characterizations for nets of automata of the location equivalence and preorder presented by Boudol et al. in the companion paper [BCHK]. Contrary to the technical development in the above given reference, where locations are dynamically associated to the subparts of a process in the operational semantics, the equivalence and preorder we propose are based on a static association of locations to the parallel components of a net. Following this static approach, it is possible to give these distributed nets a standard operational semantics which associates with each net a finite labelled transition system. Using this operational semantics for distributed nets, we introduce effective notions of equivalence and preorder which are shown to coincide with those proposed in [BCHK].     

FreesoundVR: soundscape composition in virtual reality using online sound repositories

Virtual Reality - The intersection between sound and music computing and Virtual Reality (VR) has grown significantly over the past decades, amounting to an established area of research today....     

Photonic-aware neural networks

Neural Computing and Applications - Photonics-based neural networks promise to outperform electronic counterparts, accelerating neural network computations while reducing power consumption and...     

A Novel Particle/Photon Detector Based on a Superconducting Proximity Array of Nanodots

The current frontiers in the investigation of high-energy particles demand for new detection methods. Higher sensitivity to low-energy deposition, high-energy resolution to identify events and improve the background rejection, and large detector masses have to be developed to detect even an individual particle that weakly interacts with ordinary matter. Here, we will describe the concept and the layout of a novel superconducting proximity array which show dynamic vortex Mott insulator to metal transitions, as an ultra-sensitive compact radiation-particle detector.     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.