Cloud-based Resources Research Articles

An AI Model for Digital Forensic Readiness in the Cloud using Secure Access Service Edge

Computing infrastructure has evolved and has brought about changes to the ways that work and business are carried out. Cloud computing has redefined these workspaces by providing connectivity and tools to enable productivity, collaboration, and flexibility. As work moves outside the centralised office and goes remote, users are accessing the cloud directly, leaving the protection of the corporate network and leaving the users’ computing platforms open to threats. In the pre-cloud era, the data centre for organisations was the single location where digital assets would be housed and non-complicated security parameters implemented. The firewall would be the main security perimeter implemented to secure the network in this pre-cloud era. The advent of cloud computing has brought potential areas and gaps in securing the organisational data, information and communication connectivity to the cloud-based resources. As such, there is need to rethink and redesign the models which can be implemented to secure the cloud computing services. The cloud should be in a state of digital forensics readiness in order to facilitate digital forensics investigation. The study focuses on the development of an artificial intelligence model for digital forensic readiness for the cloud using secure access service edge. This integrated approach might assist in the provisioning of cloud security.

Enhancing BYOD mobile device security in a hybrid environment

Research on enhancing Bring Your Own Device (BYOD) security in hybrid environments is critically relevant and important to protect data and ensure customer and organizational trust through the development of strong security policies. The study aims to develop and implement effective strategies and interventions that will help ensure that corporate data and resources are protected when employees use personal mobile devices in an environment where both on-premises and cloud-based resources are present. The methods used include analytical, deduction, modeling, statistical, and synthesis methods. This research identified specific vulnerabilities of potential threats to BYOD mobile devices in a hybrid environment and developed security strategies that include recommendations for creating a security policy and using a mobile device management system. The research led to the development of specific technical solutions, such as recommendations for mobile device configuration. This included educating employees and managers about security rules and the importance of adhering to security policies, and updated lists of current BYOD security threats and challenges given the changing threat environment. The practical value of the work lies in providing practical relevance to organizations by providing robust protection of sensitive data and resources, increasing employee comfort and confidence in the security of personal devices, easing IT departments’ tasks of device management and threat detection, and increasing customer and partner confidence in a security-layer organization.

Monitoring, Management, and Analysis of Security Aspects of IaaS Environments

Many companies or institutions either already have placed their resources in or plan to move them to the cloud. They do so for security reasons and are weary of the fact that by relying on cloud-based resources, they do not have to bear such extensive infrastructure-related costs. However, continuous technology advancement results not only in benefits, but also in disadvantages. The latter include the growing risk associated with IT security, forcing the individual actors to implement monitoring measures and to respond to numerous threats.This work focuses on creating a small infrastructure setup using the publicly available Google Cloud Platform which, thanks to the monitoring systems implemented thereon, allows to rapidly respond to hardware and software faults, including those caused by external factors, such as attacks on specific components. This project may also be customized to satisfy individual needs, depending on the cloud service provider selected. The work uses public cloud provider tools as well as open-source systems available for everyone, both in the cloud and in the on-prem environment. The paper deals also with the concept of a proprietary intrusion detection system.

Importance and Various Types of Task Scheduling in Cloud Computing System

Cloud Computing has arisen as quite possibly the main new computing procedures in the undertaking. A blend of advances and cycles has prompted an insurgency in the manner that computing is created and conveyed to end client. Cloud Computing traces all the way back to 1950 for example the enormous scope centralized servers were made accessible to huge undertakings. Basically, the cloud architecture is built with the combined perspective of cloud provider, user, and the broker. Scheduling is the most important task in any working framework, and it is controlled by the CPU. This is due to the fact that resources are not stored in a particularly designated manner, but rather in a strictly handy manner in order to ensure maximum skills. Cloud computing is a relatively new technology that allows users to access computer infrastructure and other services on a demand basis. Various types of task schedulers have been developed to make use of the vast amount of cloud-based resources users can access while also improving the management of data centers. We can say that cloud computing has transformed IT by allowing users and consumers to access services via the Internet. These services range from hardware to software, saving money on both the expense of setting up physical resources and the cost of obtaining the appropriate software licenses. The task scheduling problem is one of the most important and prominent challenges facing the cloud computing system.

Awareness and Predictors of the Use of Bioinformatics in Genome Research in Saudi Arabia.

With the advances in genomics research, many countries still need more bioinformatics skills. This study aimed to assess the levels of awareness of bioinformatics and predictors of its use in genomics research among scientists in Saudi Arabia. In a cross-sectional survey, 309 scientists of different biological and biomedical specialties were subjected to a previously validated e-questionnaire to collect data on (1) Knowledge about bioinformatics programming languages and tools, (2) Attitude toward acceptance of bioinformatics resources in genome-related research, and (3) The pattern of information-seeking to online bioinformatics resources. Logistic regression analysis was applied to identify the predictors of using bioinformatics in research. Significance was set at p<0.05. More than one-half (248, 56.4%) of all scientists reported a lack of bioinformatics knowledge. Most participants had a neutral attitude toward bioinformatics (295, 95.4%). The barriers facing acceptance of bioinformatics tools reported were; lack of training (210, 67.9%), insufficient support (180, 58.2%), and complexity of software (138, 44.6%). The limited experience was reported in; having one or more bioinformatics tools (98, 31.7%), using a supercomputer in their research inside (44, 14.2%) and outside Saudi Arabia (55, 17.8%), the need for developing a program to solve a biological problem (129, 41.7%), working in one or more fields of bioinformatics (93, 30.1%), using web applications (112, 36.2%), and using programming languages (102, 33.0%). Significant predictors of conducting genomics research were; younger scientists (p=0.039), Ph.D. education (p=0.003), more than five years of experience (p<0.05), previous training (p<0.001), and higher bioinformatics knowledge scores (p<0.001). The study revealed a short knowledge, a neutral attitude, a lack of resources, and limited use of bioinformatics resources in genomics research. Education and training during each education level and during the job is recommended. Cloud-based resources may help scientists do research using publicly available Omics data. Further studies are necessary to evaluate collaboration among bioinformatics software developers and biologists.

Pathogen genomics in public health laboratories: successes, challenges, and lessons learned from California's SARS-CoV-2 Whole-Genome Sequencing Initiative, California COVIDNet.

The capacity for pathogen genomics in public health expanded rapidly during the coronavirus disease 2019 (COVID-19) pandemic, but many public health laboratories did not have the infrastructure in place to handle the vast amount of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequence data generated. The California Department of Public Health, in partnership with Theiagen Genomics, was an early adopter of cloud-based resources for bioinformatics and genomic epidemiology, resulting in the creation of a SARS-CoV-2 genomic surveillance system that combined the efforts of more than 40 sequencing laboratories across government, academia and industry to form California COVIDNet, California's SARS-CoV-2 Whole-Genome Sequencing Initiative. Open-source bioinformatics workflows, ongoing training sessions for the public health workforce, and automated data transfer to visualization tools all contributed to the success of California COVIDNet. While challenges remain for public health genomic surveillance worldwide, California COVIDNet serves as a framework for a scaled and successful bioinformatics infrastructure that has expanded beyond SARS-CoV-2 to other pathogens of public health importance.

A Hybrid Accuracy- and Energy-Aware Human Activity Recognition Model in IoT Environment

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR (Accuracy and Energy Aware-HAR)</i> model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR</i> introduces a “light-weight” <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine learning</i> on-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.

Current Development, Challenges, and Future Trends in Cloud Computing: A Survey

Cloud computing is a new paradigm in information and communication technologies (ICTs) that provides the ability to access shared pools of different computing resources that are related to many cloud users within a pay-per-use or on-demand approach. It has transformed the delivery model of ICT from a product to a service. This provides several different advantages for institutions, companies and users based on savings and reduced capital expenditure through lower operating expenses. This paper provides a comprehensive survey of cloud computing. It first develops an understanding of cloud computing in general and discusses its advantages, current development, challenges and future trends. Subsequently, a detailed discussion on the cloud computing architectures, services models, fault tolerance mechanisms, services selection methods, adoption by industry, and scheduling of cloud-based resources is also presented. Nonetheless, cloud computing has many obstacles which expose it to a number of limitations. Some of these challenges include security of data, fault tolerance, and load balancing. A number of techniques in literature are proposed to cope with these challenges which are discussed and analyzed. Experimental data and usage drift validates the popularity of cloud computing and its adoption in recent years. Future trends in cloud computing support the use of intelligent machine learning (ML) techniques and new technologies to cope with some of the challenges and making cloud computing more efficient, secure and commercially viable to be widely accepted.

The Synergy between Business Process and Big Data

The synergy of Big Data and cloud computing has ushered in a new era of data-driven innovation, transforming the way organizations store, process, and derive insights from vast and complex datasets. This comprehensive overview delves into the intersection of Big Data and cloud computing, exploring key technologies, scalability, elasticity, data warehousing, NoSQL databases, distributed data processing, and the benefits they offer. By harnessing the power of cloud-based resources, organizations can efficiently manage their data resources, optimize performance, control costs, and gain a competitive edge in the data-centric landscape.

Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

Background : To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement. Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment). This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement. Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing. Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models. Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively. Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model. Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment. On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model. Keywords: Cloud Computing; EdgeCloud; DAPEEO; Energy Efficiency; Throughput; Cost

IoTwins: Toward Implementation of Distributed Digital Twins in Industry 4.0 Settings

While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose.

RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor

BackgroundHeterogeneous omics data, increasingly collected through high-throughput technologies, can contain hidden answers to very important and still unsolved biomedical questions. Their integration and processing are crucial mostly for tertiary analysis of Next Generation Sequencing data, although suitable big data strategies still address mainly primary and secondary analysis. Hence, there is a pressing need for algorithms specifically designed to explore big omics datasets, capable of ensuring scalability and interoperability, possibly relying on high-performance computing infrastructures.ResultsWe propose RGMQL, a R/Bioconductor package conceived to provide a set of specialized functions to extract, combine, process and compare omics datasets and their metadata from different and differently localized sources. RGMQL is built over the GenoMetric Query Language (GMQL) data management and computational engine, and can leverage its open curated repository as well as its cloud-based resources, with the possibility of outsourcing computational tasks to GMQL remote services. Furthermore, it overcomes the limits of the GMQL declarative syntax, by guaranteeing a procedural approach in dealing with omics data within the R/Bioconductor environment. But mostly, it provides full interoperability with other packages of the R/Bioconductor framework and extensibility over the most used genomic data structures and processing functions.ConclusionsRGMQL is able to combine the query expressiveness and computational efficiency of GMQL with a complete processing flow in the R environment, being a fully integrated extension of the R/Bioconductor framework. Here we provide three fully reproducible example use cases of biological relevance that are particularly explanatory of its flexibility of use and interoperability with other R/Bioconductor packages. They show how RGMQL can easily scale up from local to parallel and cloud computing while it combines and analyzes heterogeneous omics data from local or remote datasets, both public and private, in a completely transparent way to the user.

Greening the Cloud: A Load Balancing Mechanism to Optimize Cloud Computing Networks

ABSTRACT Approximately 55 percent of the world’s 7.3 billion people access the Internet, creating a significant demand in information technology (IT) services for both organizations and consumers. The number of data centers continue to increase to meet this demand, consuming 2 percent of the energy produced worldwide. Many organizations moving towards cloud computing due to its ability to meet users’ needs on demand. Greening cloud computing technology has become an important aspect of an organization’s design and use of cloud computing. One aspect of greening the cloud is through efficiently using cloud-based resources. In this study, we focus on the resource allocation applications of cloud computing technologies to green an organizations cloud. We design a pricing and allocation mechanism for a private cloud computing service that allows the firm to effectively load balance their cloud computing resources. Our optimal pricing mechanism is a dynamic pricing model, which maximizes the net value of users in a private cloud computing service. Additionally, we create a job allocation algorithm based on our dynamic pricing model to load balance the cloud. We show that balancing the job allocation such that the number of jobs at individual resource servers as close to equal as possible is optimal. Furthermore, simulations were run on the allocation mechanism to examine the effects on the cloud resources while gaining insight to effectively distribution resources to public clouds. Our model can help organizations to efficiently distribute their cloud-based resources, which allows for a greener cloud computing system.

Decentralised Agent-Based Medical Image Reconstruction

In this paper we propose a solution to a challenging task of magnetic resonance image reconstruction in a scalable and flexible way. The proposal elaborates task slicing and its distribution using market-based approach, where participating nodes perform task processing using their own spare computing capacities. The distributed task execution is orchestrated within multi-agent environment where agents offer the execution services by bidding for the offered tasks. Proposed negotiation protocol includes local and cloud-based resources where the offered services are exchanged for an award based on the negotiated award value function. The proposed solution considers simplicity and expressiveness of SDLMAS language and the advantages of corresponding platform for rapid design and development of multi-agent systems.

Using Cloud-Based Resources for Neuroimaging Research: A Practical Approach.

Introduction The scale and scope of human and animal neuroscience research has been increasing exponentially over the past decade. This growth has manifested both as increases in the number of participants in many studies, as well as an increase in the volume and types of data collected from each individual [1,2,3]. Many of these […]

Abstract B13: Leveraging cloud-based computational resources for gene fusion discovery with potential clinical implications for pediatric solid tumor patients

Abstract Introduction: Gene fusions are important oncogenic drivers with significant clinical impact in some cancer types. This is particularly true in pediatric cancers that often have low mutational burden and lack other diagnostic markers and therapeutic targets. Many gene fusions are rare or private to the individual patient and can be difficult to detect with methods optimized for common fusions. Unbiased sequencing methods and expansive computational resources are needed for expanding our ability to characterize fusions. Building a comprehensive catalog of oncogenic gene fusions will improve our understanding of their diversity and fully harness their potential for clinical impact. Methods: Patients are eligible for the GAIN/iCat2 study if they have been diagnosed with high-risk or recurrent/refractory extracranial solid tumor at age 30 or less and have a sample available for sequencing. Enrolled patients with an unclear diagnosis after standard clinical testing are nominated for transcriptome sequencing by the study investigators. We developed a computational pipeline in Google Cloud for gene fusion discovery utilizing paired end Illumina RNA-Seq data, multiple fusion callers, and a custom algorithm for integrative data analysis. The multicaller fusion detection approach enables us to address the high false-positive rate typical for gene fusion calling in transcriptomic data while improving the sensitivity to detect the more challenging fusions. After filtering, the fusions are annotated using the databases of known fusions and cancer genes. The predicted fusion transcripts are inspected visually, and the fusions are selected based on relevance to diagnostic classification or therapy to be validated by an orthogonal method. Results: 41 tumor samples were sequenced and analyzed for gene fusions. A total of 203 candidate fusions were detected by two or more fusion callers. Based on functional annotations and potential impact on diagnosis or therapeutic approaches, 12 fusion transcripts of interest were identified, 10 of which were validated by either pre-enrollment testing or an orthogonal method. Of 16 mesenchymal cases, 6 validated fusions had diagnostic relevance and 3 validated fusions had therapeutic implications (ERC1-BRAF, RBPMS-NTRK2, and VCAN-IL23R). Two patients responded to matched targeted therapy. In one case, diagnostic classification was revised. Conclusions: Whole-transcriptome sequencing in this selected patient population identified some fusion transcripts with clinical relevance. Determining the biologic significance of previously unreported fusions will require orthogonal sequencing such as whole genome, functional studies, and analysis of larger patient populations. Improved accuracy and scalability of methods for large-scale gene fusion analysis in the growing public datasets are likely to expand the landscape of gene fusions in cancer. Citation Format: Alma Imamovic, Alanna J. Church, Laura B. Corson, Deirdre Reidy, Navin Pinto, Luke Maese, Theodore W. Laetsch, AeRang Kim, Susan I. Vear, Margaret E. Macy, Mark A. Applebaum, Rochelle Bagatell, Amit J. Sabnis, Daniel A. Weiser, Julia L. Glade-Bender, Gianna R. Strand, Lobin A. Lee, R. Seth Pinches, Catherine M. Clinton, Brian D. Crompton, Neal I. Lindeman, Steven G. DuBois, Katherine A. Janeway, Eliezer M. Van Allen. Leveraging cloud-based computational resources for gene fusion discovery with potential clinical implications for pediatric solid tumor patients [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B13.

PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform

For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.

Strengthening Cloud Performance by Applying Load Balancing Methodology and Security Automation

Cloud enrolling gives association of sharing information collecting most extreme and PC authority over the Internet and Intranet. Cloud advancement has another procedure for wide scale dissipated figuring with five star introduction. It is a total of wide get-together of topographically and continuously appropriated heterogeneous assets for illuminating broad scale information and strategy concentrated issues. The heterogeneous technique for mastermind selecting asset makes the advantage association an essentially problematic occupation. Asset association conditions a significant part of the time join asset presentation, asset watching, asset inventories, asset provisioning, charge detainment, plan of autonomic limits and association level association works out. The exibility, security and assurance of cloud organizations can be improved by grasping customer driven access control and character the officials plans. Late advances in cryptography and web progresses empower us to structure security game plans that give the purchasers more noteworthy detectable quality and control over their cloud-based resources and right now some security and insurance concerns and fears related with cloud perspective. By the day's end, these game plans make it feasible for the dares to get cloud perspective and re-proper hardware, establishment and programming while simultaneously keeping up order over their characters and data CloudSim is a .NET-based lattice enlisting structure that gives the Runtime gear and programming condition required to make computational framework. In the wake of setting up system condition, we have concentrated existing change in accordance with inside dissatisfaction in CloudSim in detail, and have chosen the standard clarifications behind disappointments in it. To manage a touch of the apparent needs we have proposed bolster boss idea. In case there should rise an occasion of disappointment of the focal regulator, bolster boss will expel its control and stays from the framework to bite the dust. Assets are quick and heterogeneous in nature. So the pile of each advantage fluctuates with change in course of action of matrix additionally the event of disappointment of focal points is altogether increasingly fundamental. Other than the slip-up of the advantages impacts the work execution mortally. This makes stack changing and acclimation to inside frustration dynamically fundamental if there should arise an occurrence of getting ready framework. This paper second issue presents an estimation which adjusts the heap among the preferences in addition amasses the resolute idea of the grid condition. Undertaking will be moved from vivaciously stacked focus point to deliberately stacked focus point. Weight changing is a structure to update assets, using parallelism, redesign throughput, and to slice reaction time through an authentic dispersal of the applications. The focal objective of weight changing is to give a passed on, immaterial effort, invent that alters the heap over the entirety of the processors. This assessment will help society with lessening the execution time of utilization after disillusionment of structure and move the load beginning with one system then onto the following structure in over-trouble structure.

Collision avoidance in 5G using MEC and NFV: The vulnerable road user safety use case

Automotive is considered one of the driving use cases for the 5th Generation (5G) systems, which currently formulates numerous scenarios and Key Performance Indicators (KPIs), via advanced Vehicle-to-everything (V2X) services and applications. Minimum end-to-end delay, as well as advanced contextual awareness requirements, pose novel architectural and functional challenges. This paper exploits two key enablers, namely Multiple Access/Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), and acts in a two-fold manner: Firstly, it proposes a hybrid architecture for 5G systems, which exploits the afore-mentioned technologies, and performs computing resources’ selection among MEC and/or centralized, cloud-based resources (as VNFs), towards efficient service orchestration. The second contribution of this paper is a novel V2X service and algorithm, namely VRU-safe, that operates on top of the proposed architecture. VRU-Safe is an efficient, lightweight, low time complexity scheme, capable of identifying and predicting potential imminent road hazards between moving vehicles and Vulnerable Road Users (VRUs). The performance and viability of the proposed solutions are evaluated in a real-world 5G testbed in Europe.

Predictable Opportunities in Public and Private Sectors through Advanced Cloud Computing

The majority of computers and data management is supposed to be handled in the future through cloud computing. In future, the inclination to use cloud platforms and improvements in the IT environment would be unavoidable. In IT-dependent sectors, this transition to cloud computing would be a major step. The utilisation of cloud systems is one of the key parts of this diversity. This approach influences information engineering events in the development of software. Programmers may use the cloud architecture to write applications that can both operate in a data centre and use available cloud-based resources. This innovation also delivers centralised elastic machine tools under the pay-per-use paradigm. Also, it allows smartphone users access to info, apps and cloud via the Internet. This innovation is predicted to be used in the future in schooling, urban and rural growth, wellness and more practical social media. The current article explored digital cloud computing, which presents great opportunities for advancement in IoT, Smart Grid, IoV, UAVs, 5G and several other fields of use.