RAFALE: Rethinking the provisioning of virtuAl network services using a Fast and scAlable machine LEarning approach

Network Function Virtualization (NFV) has been identified to revamp the provisioning of next-generation network services. This new paradigm allows cloud and network/service providers to compose their network services, also known as service function chains (SFCs), in an agile way since the software of the network function is decoupled from the legacy hardware. To reap the benefits of this new technology, there is a need for novel mechanisms that help cloud and network/service providers deploy the increasingly complex virtual network services seamlessly, efficiently, and in a time-efficient way. Existing state-of-the-art techniques often rely on the Integer Linear Programming framework, heuristics/metaheuristics, and greedy methods to deploy the services function chains. However, these techniques although reasonable and acceptable, still suffer from several key limitations: convergence time and scalability. To this end, we propose RAFALE, a suite of solution techniques, to tame this complexity by leveraging the concept of similarity from machine learning and skip-gram modeling framework. To the best of our knowledge, we are the first to tackle these key limitations and propose a suite of solutions to them. RAFALE, a novel approach proposed to find the similarity between the new incoming virtual network service request and all the already-deployed services to learn from the previous experience of deploying techniques and use the same or close similar provisioning techniques. RAFALE is the first and the only method that develops the idea of detecting the similarity between virtual network services. Experimental results show that RAFALE reduces greatly the convergence time needed for provisioning virtual network services and can scale to 100 virtual network functions per virtual network service compared to the state-of-the-art. The Experimental results prove that RAFALE accomplished the NFV promises; decreasing the time and complexity of managing and deploying the virtual services, and providing a solution that is agile, faster, and scalable to deploy the new service requests by skipping one or more service provisioning steps (i.e., detecting and resolving the conflicts among policies, placement, and chaining) while satisfying the validated NFV policies.

     

Use of Eddy Current Conductivity and Hardness Testing to Evaluate Heat Damage in Aluminum Alloys

Aluminum alloys are heat treated to provide optimal material properties for use in a variety of applications. However, when exposed to abnormally high temperatures, an evaluation must be performed to determine if the aluminum component has been compromised. Nondestructive evaluation of aluminum alloys, by means of electrical conductivity and hardness tests, can assist in determining the condition of the part. These techniques require experience and engineering judgment to properly interpret the data produced in order to determine whether a part needs to be replaced. This article will elaborate on issues with these nondestructive techniques to help diagnose the condition of aluminum alloys exposed to high temperatures.     

Silicene Nanoribbons on an Insulating Thin Film

Silicene, a new 2D material has attracted intense research because of the ubiquitous use of silicon in modern technology. However, producing free-standing silicene has proved to be a huge challenge. Until now, silicene could be synthesized only on metal surfaces where it naturally forms strong interactions with the metal substrate that modify its electronic properties. Here, the authors report the first experimental evidence of silicene nanoribbons on an insulating NaCl thin film. This work represents a major breakthrough, for the study of the intrinsic properties of silicene, and by extension to other 2D materials that have so far only been grown on metal surfaces.     

Atmospheric and anthropogenic deterioration of the İvriz rock monument: Ereğli-Konya,Central Anatolia,Turkey

Bulletin of Engineering Geology and the Environment - The aim of this study is to determine the extent of deterioration of the limestone on which the İvriz rock monument is engraved. This...     

Clarification and pasteurisation effects on monomeric anthocyanins and percent polymeric colour of black carrot (Daucus carota L.) juice

Black carrots (BCs) are a rich source of stable anthocyanins (ACNs). The purpose of this study was to evaluate the effects of clarification and pasteurisation on ACNs of black carrot juice (BCJ). Monomeric ACNs, ACN profile and percent polymeric colour were determined during processing of BCJ. While depectinisation and bentonite treatments resulted in 7% and 20% increases in monomeric ACN content of BCJ, respectively, gelatine–kieselsol treatment and pasteurisation resulted in 10% and 3–16% reduction. Percent polymeric colour decreased after clarification, but substantially increased in samples subjected to heat. ACNs of BCJ samples were identified by HPLC–MS. Unclarified BCJ contained cyanidin-3-galactoside-xyloside-glucoside-ferulic acid as the major ACN, followed by cyanidin-3-galactoside-xyloside-glucoside-coumaric acid, and cyanidin-3-galactoside-xyloside-glucoside. After depectinisation, two more ACNs (cyanidin-3-galactoside-xyloside and cyanidin-3-galactoside-xyloside-glucoside-sinapic acid) were also identified. These results indicated that depectinisation and bentonite treatment had positive effect on the colour of BCJ, while gelatin–kieselsol treatment and pasteurisation had negative effect.     

Implementation of Hydraulic Modelling for Water-Loss Reduction Through Pressure Management

This study was conducted in the Konyaalti Water Distribution Network in Antalya, Turkey. The study area was divided into 18 district metered areas (DMAs) for better management of water losses. Water levels in reservoirs, flow rates, and water pressures were monitored on-line by the SCADA data system. A hydraulic model was calibrated and verified for each DMA using data provided by SCADA. The model results revealed that a number of DMAs exhibited high pressures, greater than 3.5 bars, and high minimum night flow (MNF) throughout the year. Also, the Infrastructure Leakage Index (ILI) for the study area was greater than 20, indicating high water losses. As a result of these findings, a pressure reducing valve (PRV) was installed at DMA No. 2 as an example and set at 3.0 bars resulting in considerable reduction in water losses. The optimum pressure level for setting the PRV was chosen using the hydraulic model. The same model was used to predict water savings due to pressure reduction. The predicted water savings were verified using long periods of flow rates and water pressure profiles. The predicted and measured water savings showed good agreement. The study concluded that hydraulic modelling is essential for applying appropriate pressure management strategies.