AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

With the rising popularity of web-based applications, the primary and consistent resource in the infrastructure of World Wide Web are cluster-based web servers. Overtly in dynamic contents and database driven applications, especially at heavy load circumstances, the performance handling of clusters is a solemn task. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will grieve from substantial performance deprivation under database-driven and dynamic contents. The most serviceable load balancing approaches are Web Server Queuing (WSQ), Server Content based Queue (QSC) and Remaining Capacity (RC) under specific conditions to provide better results. By Considering this, we have proposed an approximated web server Queuing mechanism for web server clusters and also proposed an analytical model for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The approximated load of each web server is used for load balancing. The investigational results illustrate the effectiveness of the proposed mechanism by improving the mean response time, throughput and drop rate of the server cluster

A Novel Cloud based Mobile Social TV

ABSTRACT: Cloud computing is now considered a major commanding hosting platform in numerous areas including mobile computing. Several mobile television systems have sprung up in current years, motivated by hardware as well as software progression in mobile devices. By delegating towards cloud of communications as a provision, mobile television becomes familiarized to streams intended in aid of a variety of applications. We intend CloudMoV to effortlessly make use of agile resource support as well as prosperous functionalities obtainable by Infrastructure-as-aService cloud and Platform-as a-Service cloud. Novel system of cloud-based social television makes easy for consumption of two most important functionalities in the direction of contributing portable users such as, a user provoke frequent friends to stare at comparable video in co-viewing by social interactions, and replacement text communication while examination. The system is capable to attain an important power saving, by opportunistically switching device among high-power as well as low-power transmission modes throughout streaming

Blockchain-based multi-layered federated extreme learning networks in connected vehicles

Intelligent and networked vehicles help build an efficient vehicular network’s infrastructure. The widespread use of electronic software exposes these networks to cyber-attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real-time performance. Current learning-based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain-based Multi-Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF-IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi-Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road-Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real-time data sets, the suggested algorithm’s performance under different attack scenarios were extensively tested. The suggested method obtained 98 % accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it’s incredibly secure and costs very little to transmit

Blockchain-based multi-layered federated extreme learning networks in connected vehicles

Intelligent and networked vehicles help build an efficient vehicular network’s infrastructure. The widespread use of electronic software exposes these networks to cyber-attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real-time performance. Current learning-based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain-based Multi-Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF-IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi-Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road-Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real-time data sets, the suggested algorithm’s performance under different attack scenarios were extensively tested. The suggested method obtained 98 % accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it’s incredibly secure and costs very little to transmit

BENS−B5G: Blockchain-Enabled Network Slicing in 5G and Beyond-5G (B5G) Networks

Fifth-generation (5G) technology is anticipated to allow a slew of novel applications across a variety of industries. The wireless communication of the 5G and Beyond-5G (B5G) networks will accommodate a wide variety of services and user expectations, including intense end-user connectivity, sub-1 ms delay, and a transmission rate of 100 Gbps. Network slicing is envisioned as an appropriate technique that can meet these disparate requirements. The intrinsic qualities of a blockchain, which has lately acquired prominence, mean that it is critical for the 5G network and B5G networks. In particular, the incorporation of blockchain technology into B5G enables the network to effectively monitor and control resource utilization and sharing. Using blockchain technology, a network-slicing architecture referred to as the Blockchain Consensus Framework is introduced that allows resource providers to dynamically contract resources, especially the radio access network (RAN) schedule, to guarantee that their end-to-end services are effortlessly executed. The core of our methodology is comprehensive service procurement, which offers the fine-grained adaptive allocation of resources through a blockchain-based consensus mechanism. Our objective is to have Primary User—Secondary User (PU—SU) interactions with a variety of services, while minimizing the operation and maintenance costs of the 5G service providers. A Blockchain-Enabled Network Slicing Model (BENS), which is a learning-based algorithm, is incorporated to handle the spectrum resource allocation in a sophisticate manner. The performance and inferences of the proposed work are analyzed in detail

Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images

Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance

Bioavailability Enhancement of Ritonavir by Solid Dispersion Technique

Ritonavir is an antiretroviral agent used in the treatment of HIV-infection. It is a BCS class IV drug having poor aqueous solubility leading to poor bioavailability. Bioavailability is the amount of drug that enters the systemic circulation. The bioavailability is affected by various factors like solubility, dissolution and stability. In order to improve bioavailability, many techniques like solid dispersions, nanoparticles, liposomes, encapsulation methods were present. The main aim of this study is to improve the bioavailability of ritonavir with the help of Polyvinyl Pyrrolidone (PVP) K-30 by using solid dispersion technique. Different formulations were made with varied concentrations of polymer. Characterization of solid dispersion was done by phase solubility, drug content, Fourier transformed infrared spectroscopy (FT-IR), Differential Scanning Calorimetry (DSC) and in-vitro dissolution studies.  From phase solubility studies that apparent solubility constant was found to be 42.227M-1. The drug content of the binary system of ritonavir and PVP was found to be ranging from 99.17% to 103.06%. %. FT-IR studies revealed that there was no drastic change in the wave number indicating polymer compatibility with drug. In-vitro dissolution studies proved that there was an increase in drug release of ritonavir with incremental ratios of polymer and F5 formulation has shown almost 95% of drug release. Keywords: Bioavailability, Solid dispersion, Polyvinyl pyrrolidine, Solvent evaporation, Dissolution