Cloud-based Services Research Articles

Digital twinning of vertical centrifugal casting

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.

Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations

This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

Introduction to Cloud Gaming

Cloud Gaming can be defined as a technology that enables gamers to play video games using cloud-based servers and infrastructure, rather than relying on local hardware for processing and rendering game graphics. In cloud gaming, the games are streamed to the player's device with the help of the internet, allowing them to play high-quality games without needing to own a high-end gaming console or PC. The solution helps people to play games on their computers regardless of their own system as their gaming needs are met with the help of good enough customer service in the cloud. It also helps to overcome the problems of traditional games such as conflict and mobility. In this article, we will take a look at various aspects of Cloud Gaming System (CGS) to explain the many advantages and capabilities this concept has.

SolarWinds Attack: Stages, Implications, and Mitigation Strategies in the Cyber Age

SolarWinds is a software company based in the United States that provides IT monitoring and management tools. Founded in 1999, SolarWinds offers a variety of products that help organizations manage networks, systems, IT infrastructure, applications and cloud-based services. SolarWinds products are used for performance monitoring, log management, IT security, and data analysis. The company became widely known after a major cybersecurity incident came to light in late 2020, in which their network management software, Orion, was used as a vector for attacks by a state-backed hacking group. These attacks affected many organizations, including government agencies and private companies, and led to an increased focus on software supply chain security. This paper has reviewed stages, Implications, and mitigation strategies of SolarWinds.

Patient-Reported Outcomes Among Adults With Congenital Heart Disease in the Congenital Heart Initiative Registry

In the US, there are more than 1.5 million adults living with congenital heart disease (CHD). The Congenital Heart Initiative (CHI) is a digital, online, patient-empowered registry that was created to advance multicenter research and improve clinical care by gathering patient-reported outcomes (PROs) in adults with CHD. To report the initial findings of the PROs for adults with CHD from the first 3 years of the CHI. The CHI was launched nationally on December 7, 2020, as an observational cohort (survey) study. Data were collected virtually through December 31, 2023, and stored on Health Insurance Portability and Accountability Act-compliant cloud-based servers with restricted access. Adults with CHD were recruited through email, social media, general advertising through advocacy organizations, and targeted outreach (telephone and in-clinic recruitment) by clinical centers. Demographics and validated survey tools on quality of life, mental health, physical activity, and health care utilization were collected at baseline and every 4 months. Descriptive statistics were used to understand the associations between various factors, including the complexity of heart defects, physical activity levels, mental health comorbidities, and socioeconomic and health care access variables. All categorical variables were analyzed using χ2 or Fischer exact test as appropriate. By December 31, 2023, the CHI had enrolled 4558 participants (2530 female [56%]) with a mean (SD) age of 38.5 (13.9) years, representing all 50 states. Approximately 88% of participants (3998 participants) completed at least 1 electronic visit as of December 31, 2023. The most prevalent CHD anatomy included tetralogy of Fallot (883 participants [22%]), transposition of great arteries (452 participants [11%]), and coarctation of the aorta (429 participants [11%]). Approximately 88% of participants (3998 participants) reported at least 1 comorbidity, with arrhythmia (1300 participants [33%]) as the most common cardiac comorbidity and mood disorder (1326 participants [35%]) as the most common noncardiac comorbidity. Among female participants, 45% (1147 participants) reported having had a pregnancy, with 38% (967 participants) resulting in biological children. Participants with complex CHD were less likely than those with moderate CHD to meet recommended physical activity guidelines (χ22 = 15.9; n = 3320; P < .001), a factor that was more pronounced among female participants. Overall health-related quality of life was rated as good or better by 84% of participants who completed the quality of life PROs (2882 participants), with no difference by CHD complexity. In this cohort study of adults living with CHD, many patients reported mood disorders, but most reported good health-related quality of life. The CHI, the largest registry of adults with CHD, is poised to facilitate multicenter research with the goal of improving clinical outcomes for all adults with CHD.

Security Evaluation Framework for Virtualised Environments

With the expansion of cloud computing, virtual environments remain susceptible to sophisticated security threats, such as hypervisor vulnerabilities, VM escapes, inter-VM attacks, DoS attacks, and malware injections. Hence, ensuring that virtualised environments are secure, has become increasingly crucial. These threats are particularly affecting as many enterprises which have shifted to online services and remote work. This paper examines the existing frameworks which have been designed to address the existing challenges, their gaps and strengths; and proposes an enhanced security framework that can help to mitigate these threats. The framework was developed following an iterative process encompassing several stages. It has the hypervisor layer, virtual machine layer, network layer, management layer, and monitoring and response layer components. The framework aims to enhance detection capabilities, reduce response times, minimise system performance impact, and lower false positive rates while optimising resource utilisation by providing a practical and effective approach to securing virtualised infrastructures, thereby ensuring the resilience and reliability of cloud-based services.

Strategies for LinkedIn-Based Guest Speaker Recruitment and Establishment of Engaging and Comfortable Classroom Discussions: A Case Study from an Operations Management Course

This article describes workable strategies for instructors to introduce highly interactive question and answer (Q&amp;A) sessions with accomplished guest speakers from industry. Two innovative strategies serve as the key factors behind the series’ success: (1) using LinkedIn to partner with guest speakers, especially university alumni, who possess expertise in specific operations management disciplines, and (2) implementing a novel structured Q&amp;A approach that enables alignment with learning objectives while maximizing quality student engagement. Our approach accesses the resumes of tens of thousands of college alumni listed on LinkedIn and utilizes free cloud-based services such as Google Forms and Google Sheets for question submission (by students), review (by instructors), and selection (by the guests). These successfully implemented strategies (1) match qualified speakers to relevant course content, (2) boost student engagement by ensuring the participation of all students, (3) promote content alignment by first filtering out irrelevant student questions, and (4) increase speaker preparation and comfort level.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

Scalable Metadata-Hiding for Privacy-Preserving IoT Systems

Modern cloud-based IoT services comprise an integrator service and several device vendor services. The vendor services enable users to remotely control their devices, while the integrator serves as a central intermediary, offering a unified interface for managing devices from different vendors. Although such a model is quite beneficial for IoT services to evolve quickly, it also creates a serious privacy concern: the vendor and integrator services observe all interactions between users and devices. Toward this, we propose Mohito, a privacy-preserving IoT system that hides such interactions from both the integrator and the vendors. In Mohito, we protect both the interaction data and the metadata, so that no one learns which user is communicating with which device. By utilizing oblivious key-value storage as a primitive and leveraging the unique communication graph of IoT services, we build a scalable protocol specialized in handling large concurrent traffic, a common demand in IoT systems. Our evaluation shows that Mohito can achieve up to 600x more throughput than the state-of-the-art general-purpose systems that provide similar security guarantees.

Quantum Computing and Cloud Technologies: The Next Frontier in Computing Power

Abstract: This article explores the convergence of quantum computing and cloud technologies, representing a pivotal advancement in computational power with far-reaching implications across various industries and scientific domains. It examines the current state of quantum computing, elucidating its fundamental principles and recent hardware advancements, while comparing its capabilities to classical computing systems. The integration of quantum computing with cloud platforms is analyzed, highlighting the emergence of cloud-based quantum services and their advantages in democratizing access to this cutting-edge technology. The article delves into potential applications of quantum cloud computing, including cryptography, material science, artificial intelligence, and financial modeling, showcasing its transformative potential. Critical challenges facing quantum cloud computing are addressed, such as error correction, scalability issues, and the development of quantumsafe encryption methods. Looking ahead, the article discusses prospects and research directions, including anticipated breakthroughs in quantum hardware, emerging quantum cloud architectures, and the potential impact on various industries. By synthesizing current research and industry developments, this comprehensive overview provides insights into the revolutionary potential of quantum cloud computing and its role in shaping the future of computational capabilities.

Cloud-Based Service for Fingerprint Image Matching

Fingerprint matching is one of the most important services that related to the issue of identifying individuals, as it can be used to verify the validity of contracts, documents or in other security fields such as criminal investigations. Fingerprints are also one of the most common and accurate forms, as they are easy to obtain, it also has a unique features and cannot be identical to any individuals. The proposed service exploits the advantages provided by cloud computing, such as ease of use, availability of the service anytime and anywhere, and no need to own expensive devices with high specifications, in addition to being low cost. In this service, all fingerprint image preprocessing operations and images enhancement, in addition to the matching, verification and decision-making process are done with the help of tools which provided by cloud computing environment. While the client's role in this service is limited to feeding the system with the fingerprint to be matched. The experimental results proved that this service has achieved high accuracy in matching fingerprint images, in addition to greatly facilitating the matching process for the user, and it can be used anytime and anywhere thanks to the cloud environment facilities adopted by this service.

RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key generation, decryption, and ciphertext update while assuring their security. By implementing RT-PPS, organizations can take advantage of the benefits of IoT data while protecting the privacy of their users and maintaining the security of their systems.

CIPerf: a benchmark for continuous integration services performance and cost analysis

Continuous Integration is a crucial practice in modern software development, enabling teams to automate the process of building, testing, and merging code increments to ensure continuous delivery of high-quality software. Despite its growing adoption, the cost and performance of Continuous Integration services often go unexamined in sufficient detail. This paper presents CIPerf, a comprehensive benchmark designed to analyze both the performance and cost of cloud-based and self-hosted Continuous Integration services. The study centers on a comparison between two specific services: Bitbucket Pipelines, a cloud-based offering by Atlassian, and Hetzner, a self-hosted solution. By focusing on these platforms, the research aims to provide practical insights into the real-world costs and execution performance of Continuous Integration services. To achieve this, CIPerf conducted automated tests on an hourly basis over a two-month period, measuring critical timeframes such as resource provisioning, environment setup, and the actual test execution times. The results showed significant differences in both the cost efficiency and the consistency of performance between the two services. For instance, Bitbucket Pipelines, while convenient in its cloud-based offering, demonstrated higher variability in provisioning times compared to the stable, predictable performance of Hetzner’s self-hosted environment. The analysis also explored how these performance metrics influence key software development metrics, including deployment frequency and developer productivity. CIPerf provides a clear methodology for developers and organizations to objectively assess their Continuous Integration service options, ultimately helping them optimize their workflows. Moreover, this benchmark can serve as an ongoing tool to monitor service performance over time, identifying potential degradations or improvements in service quality, thus offering longterm value for teams that rely on Continuous Integration for their development processes.

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.

Penerapan Aplikasi Sicantik Cloud Terhadap Kualitas Pelayanan di Dinas Penanaman Modal, Pelayanan Terpadu Satu Pintu dan Tenaga Kerja Kota Lhokseumawe

The management information system applied in the government is known as the term electronic government (e-government) which is interpreted as the user of information technology transforming government activities with the aim of increasing the effectiveness and efficiency of government administration. SiCantik Cloud is a smart application for integrated public cloud-based licensing services that can be used by government agencies independently. SiCantik Cloud or an intelligent application system for integrated licensing services for the public, is intended for the public to make it easier to manage online licensing. The purpose of this study is to determine the application of the Sicantik Cloud application to Services at the PMPTSP and Naker Office and the inhibiting factors for the application of the Sicantik Cloud application to Services at the PMPTSP and Naker Office of Lhokseumawe City. This study is a study using a qualitative descriptive approach. The application of the SiCantik application to the quality of service at the Investment, One-Stop Integrated Service and Manpower Office of Lhokseumawe City is quite good, as known from several benchmarks of service quality from the Sicantik Cloud application, namely Accountability, Transparency, Effectiveness, and Efficiency. The lack of public awareness of the importance of using information technology means that many people have not been able to optimize the Sicantik application.

Modeling Approach of Cloud 4D Printing Service Composition Optimization Based on Non-Dominated Sorting Genetic Algorithm III

The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing design and functionality, necessitating the effective management of these emergent 4D printing services. This study aims to bridge the gap between the static nature of existing cloud manufacturing services and the dynamic requirements imposed by 4D printing technology. We propose a comprehensive multiobjective optimization model for cloud-based 4D printing service portfolios, incorporating the intricate complexities of 4D printing services and assessing the efficacy of the Non-Dominated Sorting Genetic Algorithm III (NSGA III) in optimizing these service portfolios to meet dynamic demands. In this research, the NSGA III algorithm is employed to develop a multiobjective optimization framework for 4D printing service portfolios, addressing critical issues such as service cost, time, quality, adaptability, and overall service optimization amidst fluctuating demand and service availability. The findings indicate that the NSGA III algorithm demonstrates superior performance in terms of generational distance (GD) and inverted generational distance (IGD), particularly excelling in convergence and diversity for high-dimensional optimization problems when compared to the comparison algorithms. The study concludes that the NSGA III algorithm exhibits significant potential in optimizing the orchestration of cloud-based 4D printing service portfolios, underscoring its effectiveness in managing the complexities associated with these services. This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field.

Building a framework to drive government systems' adoption of cloud computing through IT knowledge

abstractMany governments in developing countries are finding it challenging to integrate cloud computing into their e-government systems because of the limited effective identification and evaluation of important factors. Cloud computing usage in government organizations is still restricted due to concerns over security, regulatory challenges, cultural resistance to new technologies, infrastructure limitations, and other factors (Abied et al. in Sustainability 14:15590, 2022). This research suggests a model that analyses the factors influencing the adoption of cloud computing in e-government systems within Jordanian government organizations by decision-makers. The survey was distributed online to a purposive sample consisting of 373 IT professionals and top-level managers in government organizations in Jordan. The study demonstrates that the technological factors which include complexity, compatibility, relative advantage, and security, the organisational factors which consist of top management support and cost, and the environmental factors which include industry competition and government rules and regulations, had a significant and positive impact on the adoption of cloud-based services in government systems. Furthermore, IT knowledge held by decision makers was recognized as a key factor in the adoption of cloud computing for e-government systems. This research is anticipated to provide significant insights for any organizations making investment decisions about the use of cloud-based services. This research considers the assessment of multiple aspects to encourage top management to analyse the consequences of implementing cloud services for e-government systems. This case study might not be applied broadly to all types of organizations in Jordan. Future research should employ large-scale samples to ensure the study's representativeness and could also broaden the scope by including samples from similar countries to validate the model or its implementation across different cultures, IT infrastructure and geographical settings. In addition, it is recommended that upcoming research consider using the level of experience and education as moderators.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

A Retrospective and Prospective Analysis of Cloud Computing: Historical Context, Deployment Strategies, and Service Models

The network concerned usually discusses the use of the Internet for various services, including data storage, software, security, etc. These services are commonly referred to as cloud-based information services. Examples of such services include Gmail, YouTube, Amazon, and Dropbox. The paper covers the evolution of cloud computing from its early stages to the present day and explores the different services offered by cloud computing in both personal and business computing scenarios. It provides an overview of the history, deployment, and service models that led to the development of cloud computing.

An efficient model of enhanced optimization and dilated-GRU based secured multi-access edge computing with blockchain for VANET sector

The establishment of Vehicular Ad Hoc Networks (VANETs) has brought significant advantages to humans, yet it also raises crucial safety considerations. Security is one of the major challenges in VANETs and is receiving significant attention. Hostile Onboard Units (OBUs) can use a variety of tactics including blocking, monitoring and tampering to harm neighboring OBUs to make illicit profits. The widespread use of the decentralized architecture built around blockchain technology has enabled the clear, safe, and dispersed storage and transmission of VANET application-related information without the need for an administrative center of authority. Perhaps the most important parts of the blockchain-driven VANET system involve implementing an efficient and adaptable consensus system, which remains an open academic issue. The objective of the suggested solution is to create a deep learning model for blockchain that will guarantee VANET security. This network topology is comprised of three layers: “perception, edge computing, and services”. The initial layer that comprises the blockchain operation is employed to demonstrate the privacy of VANET information. Additionally, cloud-based services as well as edge computing are used by the perception layer. The data is safeguarded by the service layer through the application of the blockchain system and storage in the cloud. The last layer aids in meeting user criteria for throughput and QoS. The main objective of this model is to evaluate the reliability of vehicle nodes maintained on the blockchain. Here, the node authentication is handled by an Adaptive Dilated Gated Recurrent Unit (AD-GRU), where the hyper-parameters of the recommended AD-GRU model are optimally tuned by an Enhanced Osprey Optimization Algorithm (EOOA). Finally, the proposed system is simulated and its extensive results are carried out. In contrast with other approaches, the novel system delivers the superior results of securing the data over the VANET environment.