Utilization Of Cloud Services Research Articles

SUCCESSFUL DATA MANAGEMENT: A CASE STUDY OF AIRLINE COMPANY IN KAZAKHSTAN

The strategic utilization of Cloud Services by Airline company in Kazakhstan (hereinafter Airline company) to optimize report generation processes, alleviate strain on its ERP (Enterprise Resource Planning) system, and enhance overall data management efficiency is examined comprehensively in this study. With the increasing volume and complexity of data generated in modern business operations, the need for scalable and efficient data management solutions has become paramount. Through interviews with key stakeholders, analysis of system performance metrics, and observation of implementation processes, a thorough understanding of the challenges and strategies associated with cloud integration is attained. The findings underscore the pivotal role of Cloud Solutions in improving data management practices, ensuring reliable report generation processes, and facilitating seamless scalability to meet evolving business needs. The study demonstrates a significant reduction in report generation times (95% improvement) and highlights the challenges and mitigation strategies during the migration process. This case study offers valuable insights for organizations seeking to enhance their data management capabilities and leverage cloud technology for strategic advantage, showcasing the transformative potential of cloud-based solutions in optimizing operations, fostering innovation, and driving sustainable growth in today’s dynamic business landscape.

From Grapes to Clicks : A Cloud-Based Wine-Selling Solution for Single Vendors

This document outlines the design and creation of a robust wine vending application for a single vendor, which combines a web platform, an Android mobile app, and a cloud-based framework. This solution addresses the increasing demand for online wine sales while delivering a smooth, secure, and adaptable shopping experience. Essential features of the system include an intuitive web interface and an application focused on effective product searching, ordering, and payment processing. Cloud infrastructure is implemented to guarantee high availability, scalability, and secure data management. The paper discusses various architectural alternatives, such as utilizing cloud services for load balancing, data handling, and disaster recovery. It also emphasizes the necessity of strong security protocols, including encryption, user authentication, and adherence to industry standards for online transactions. Additionally, the document addresses challenges related to regulatory compliance, including age verification mandates and data protection laws. Lastly, it explores strategies for performance enhancement, improvements to user experience, and potential avenues for future expansion. The proposed solution aims to deliver a dependable, secure, and scalable platform tailored to the requirements of individual wine retailers while accommodating future growth.

LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS

The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.

Оценка готовности российских регионов к использованию облачных услуг

The objective of the study is to develop methodological approaches for the comparative assessment of Rus-sian regions’ readiness to adopt cloud services and the intensity of their usage, in order to substantiate strategic directions for the development of the infocommunication sector at the regional level. The approach is based on the construction of integrated indices of economic and infrastructural conditions favorable to cloud service adoption, as well as an index of the intensity of their usage. The selection of statistical indicators included in these indices is justified. A methodology for the comparative evaluation of Russian regions’ readiness to utilize cloud services is proposed. Readiness assessments of Russian regions for the development of cloud services were constructed based on current data from Rosstat for 2022 and 2023. The regions were grouped analo-gously to the BCG matrix and Gartner’s quadrant. Framework recommendations were developed, taking into account the identified specifics of regional conditions for the development of cloud service markets and the index of cloud service usage intensity. These recommendations can be used to substantiate strategic directions for the development of the infocommunication sector in Russian regions.

Students’ Perceptions and Experiences of Using Cloud Services in Online Education During Pandemic Time: A Post-Covid Study

ABSTRACT The study examines how Dhaka University students used cloud technologies to assist their online learning throughout the pandemic. A quantitative survey was carried out to collect data from the students of Dhaka University. The questionnaire included both open and close-ended questions and 5-point Likert scale items. Data were collected both online and using printed questionnaires. In this way, a total of 388 responses were recorded. IBM SPSS version 20 was used to analyze the collected data. This study found that the majority of the students used Zoom and Gmail for their online lessons. Students were pleased with the different features of cloud services including the privacy and security of data, free or pay-per-use opportunities, and personalized learning opportunities. The most common hurdle encountered by students, according to the results, was a poor internet connection. The study found no significant differences in the students’ experiences of utilizing cloud services in online learning based on their educational level, gender, age, location of attending online classes, computer experience, cloud use training, and duration of internet use.

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

An improved hunger game search optimizer based IoT task scheduling in cloud–fog computing

Due to the rapid expansion of Internet of Things (IoT)-related applications, the utilization of cloud services is experiencing significant growth. Although cloud computing has proven its effectiveness in processing and storing data for various applications, it faces challenges in addressing certain requirements, such as the growing need for real-time or latency-sensitive applications and the limitations of network bandwidth. As a result, fog computing is often seen as a supplementary paradigm to cloud computing, providing additional capabilities and benefits to the traditional cloud paradigm, aiming to extend cloud services to edge devices and end-users. However, the limited capabilities of fog nodes require lighter tasks while other tasks that need more processing time are processed in the cloud. In the present research paper, we propose a novel algorithm that is customized for task scheduling within the context of cloud–fog computing on the Internet of Things (IoT) framework. Our approach builds upon the Hunger Game Search algorithm (HGS) as its foundation. To improve the exploitative capabilities of the HGS, our proposed method, called HGSMPA, incorporates the Marine Predator Algorithm (MPA). Through experimental evaluation using various workload traces, we have demonstrated the efficacy of HGSMPA. The findings reveal that HGSMPA surpasses alternative algorithms in terms of reducing energy consumption and minimizing the makespan time. Specifically, The empirical evaluation indicates that HGSMPA can reduce the makespan time by 19.31% for synthetic workloads and by 17.47% for real workloads as compared to similar scheduling algorithms. Moreover, HGSMPA can reduce energy consumption by 14.72% for synthetic workloads and by 17.68% for real workloads as compared to other methods.

Secure Cloud Architecture

Cloud computing refers to the availability of online resources and services. Global data centers serve as the delivery hubs for cloud services. Cloud computing helps its users by giving them access to virtual resources over the internet. Pay-Per Use-On-Demand mode is provided by cloud computing and e-trade. It may be easily accessible and shared via IT resources with the help of the internet. The primary obstacle in cloud computing is to protection and privacy issues arising from its multi-tenancy structure, as well as the outsourcing of critical applications, infrastructure, and sensitive data. Businesses are utilizing cloud services at a rapid pace; therefore, it is necessary to implement measures that ensure businesses have security and can select a provider that suits them. An advancement in current technology that fulfills the long-held dream of computing as a utility is cloud computing. The majority of private and public sector businesses have been deceived by the emergence of this innovative technology in the IT industry. The two main security-based approaches now in use for cloud-based platforms are holomorphic encryption and single tamper-proof hardware. Scalability is a problem for hardware-based solutions, but holomorphic encryptions are merely an idea. Furthermore, because of the different nature of its services and deployment strategy, cloud-based platforms cannot directly incorporate standard defense in-depth security mechanisms. To secure the cloud-based platform, however, the same idea of a multi-layered security mechanism can be suggested

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

Implementation of Cellular-Based Drone Module using Cloud Services

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>The notion of a smart city incorporates the integration of infrastructure, services, and the community and encompasses the deployment of unmanned aerial vehicles (UAVs) for monitoring crop fields, facilitating logistics delivery, and performing high-altitude cleaning tasks. In a smart city, the interconnectedness of devices is realized through the medium of the Internet of Things (IoT). This research endeavors to explore the usage of Beyond Visual Line of Sight (BVLOS) for enabling remote command and control of UAV/UGV modes, leveraging 4G/LTE connectivity as an enabler. 4G/LTE connectivity is known for its improvement in data transfer speed and network capacity, which potentially enables the connection of more devices, including drones. The high availability and scalability of cloud services have become crucial factors in utilizing cloud services as the most cost-effective and expedient relay for connecting two nodes over the internet globally for now. The proposed methodology would be integrated into a smart drone module, which would be deployed at a small scale as a component of the Intelligent Transportation System (ITS).<br /><br /></p></div></div></div>

Organizational and behavioral attributes' roles in adopting cloud services: An empirical study in the healthcare industry.

The need for cloud services has been raised globally to provide a platform for healthcare providers to efficiently manage their citizens' health records and thus provide treatment remotely. In Iraq, the healthcare records of public hospitals are increasing progressively with poor digital management. While recent works indicate cloud computing as a platform for all sectors globally, a lack of empirical evidence demands a comprehensive investigation to identify the significant factors that influence the utilization of cloud health computing. Here we provide a cost-effective, modular, and computationally efficient model of utilizing cloud computing based on the organization theory and the theory of reasoned action perspectives. A total of 105 key informant data were further analyzed. The partial least square structural equation modeling was used for data analysis to explore the effect of organizational structure variables on healthcare information technicians' behaviors to utilize cloud services. Empirical results revealed that Internet networks, software modularity, hardware modularity, and training availability significantly influence information technicians' behavioral control and confirmation. Furthermore, these factors positively impacted their utilization of cloud systems, while behavioral control had no significant effect. The importance-performance map analysis further confirms that these factors exhibit high importance in shaping user utilization. Our findings can provide a comprehensive and unified guide to policymakers in the healthcare industry by focusing on the significant factors in organizational and behavioral contexts to engage health information technicians in the development and implementation phases.

Achieving Seamless Migration to Private-Cloud Infrastructure for Multi-Campus Universities

Maintaining IT infrastructure is a major challenge for multi-campus universities. The difficulties they face include inflexibility, inefficiency, outdated technology, and redundant resources, all of which negatively impact the quality of services provided to students, faculty, and researchers. Consequently, there is a growing need for a more agile, efficient, and cost-effective solution that can cater to the everchanging IT service demands of these institutions. Universities can boost their agility, scalability, and cost-effectiveness while enhancing service quality by utilizing cloud services. Cloud computing enables universities to quickly and easily access computing resources that can be scaled up or down as per their IT requirements, providing them with the necessary flexibility to meet dynamic demands. Private cloud migration further offers universities complete control over their internal security and SLA obligations, allowing them to guarantee a high level of service delivery. This paper contends that private cloud migration is an effective solution that can help multi-campus universities surmount their IT infrastructure challenges and enhance their IT service delivery capabilities. This paper presents a case study analysis of the University of the Aegean's migration to a private cloud and offers insights into the factors that influence successful private cloud migration in multi-campus universities. The paper argues that private cloud migration and cloud computing are effective solutions for resolving IT infrastructure issues and improving service delivery in higher education institutions. The paper provides recommendations for universities considering private cloud migration, offering a roadmap for successful migration. Additionally, the study identifies critical success factors that can help universities achieve a successful migration. In future work, this study could be expanded to include more universities and explore the challenges and benefits of private cloud migration in a broader context. Furthermore, it would be interesting to investigate the impact of private cloud migration on student outcomes, faculty satisfaction, and institutional performance.

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

<span>The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.</span>

A Framework for Providing Augmented Reality as a Service Provided by Cloud Computing for E-Learning

The main objective of the proposed study is to develop an e-learning system using augmented reality technology one of the main problems faces using AR in education is the huge computational power needed to transfer 2D animation to enrich learning facilities. Such problem increases when using smart mobile devices that suffer from hardware limitation. A promising framework is used to utilize cloud services to support augmented reality applications on the cloud. Such method significantly reduces consumption of memory and processing units when dealing with large size videos or images. Hence the augmented reality processing is speeded up to meet the requirements of E-learning systems.
 The proposed work was conducted on 100 students from different academic levels in the first semester of the year 2022. Three experiments were conducted for different fields of education including two-dimensional images using Unity Program (3D Software) to draw 3D objects and Vufoira software development kit. The experimental results showed promising results as the application has the flexibility to work on different platforms. Moreover the consumed memory to run the application is reduced significantly. The results also showed high performance for the application when drawing complex 3D images and when dealing with different animations.
 The study supported with a detailed questioner that proofs the importance of AR in the field of E-learning.

An optimal solution to reduce virtual machine migration SLA using host power

The growing demand for cloud services from users, organizations are planning to build more and more data centers to meet the demand. This is expected to increase energy consumption, which in turn will lead to an increase in operating costs and CO2 emissions. Virtual machine live migration technology can be used to reduce the amount of energy consumed by virtual machines. By doing so, virtual machines can be migrated from one server to another and therefore cause a delay in delivering the services to the users. Service Level Agreement (SLA) violation is one of the important parameters to be considered for better utilization of cloud services. By applying the proposed algorithm, SLA violations will be reduced through the process of selecting the host to which the Virtual Machine will be migrated according to the remaining power of the host, and thus the SLAs will be more enforced in the long run. The proposed algorithm has been implemented in CloudSim. In the comparison of the results achieved with the existing algorithms and the proposed algorithm, it is found that the proposed algorithm is capable of reducing the violation of the SLA by 13.6% when compared to the existing algorithms.

An Efficient and Secure Big Data Storage in Cloud Environment by Using Triple Data Encryption Standard

In recent decades, big data analysis has become the most important research topic. Hence, big data security offers Cloud application security and monitoring to host highly sensitive data to support Cloud platforms. However, the privacy and security of big data has become an emerging issue that restricts the organization to utilize Cloud services. The existing privacy preserving approaches showed several drawbacks such as a lack of data privacy and accurate data analysis, a lack of efficiency of performance, and completely rely on third party. In order to overcome such an issue, the Triple Data Encryption Standard (TDES) methodology is proposed to provide security for big data in the Cloud environment. The proposed TDES methodology provides a relatively simpler technique by increasing the sizes of keys in Data Encryption Standard (DES) to protect against attacks and defend the privacy of data. The experimental results showed that the proposed TDES method is effective in providing security and privacy to big healthcare data in the Cloud environment. The proposed TDES methodology showed less encryption and decryption time compared to the existing Intelligent Framework for Healthcare Data Security (IFHDS) method.

Harnessing the Potential of Function-Reuse in Multimedia Cloud Systems

Cloud-based computing systems can get oversubscribed due to the budget constraints of their users or limitations in certain resource types. The oversubscription can, in turn, degrade the users perceived Quality of Service (QoS). The approach we investigate to mitigate both the oversubscription and the incurred cost is based on smart reusing of the computation needed to process the service requests (i.e., tasks). We propose a reusing paradigm for the tasks that are waiting for execution. This paradigm can be particularly impactful in serverless platforms where multiple users can request similar services simultaneously. Our motivation is a multimedia streaming engine that processes the media segments in an on-demand manner. We propose a mechanism to identify various types of “mergeable” tasks and aggregate them to improve the QoS and mitigate the incurred cost. We develop novel approaches to determine when and how to perform task aggregation such that the QoS of other tasks is not affected. Evaluation results show that the proposed mechanism can improve the QoS by significantly reducing the percentage of tasks missing their deadlines and reduce the overall time (and subsequently the incurred cost) of utilizing cloud services by more than 9 percent.

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies.

Growth and advancement of the Deep Learning (DL) and the Internet of Things (IoT) are figuring out their way over the modern contemporary world through integrating various technologies in distinct fields viz, agriculture, manufacturing, energy, transportation, supply chains, cities, healthcare, and so on. Researchers had identified the feasibility of integrating deep learning, cloud, and IoT to enhance the overall automation, where IoT may prolong its application area through utilizing cloud services and the cloud can even prolong its applications through data acquired by IoT devices like sensors and deep learning for disease detection and diagnosis. This study explains a summary of various techniques utilized in smart healthcare, i.e., deep learning, cloud-based-IoT applications in smart healthcare, fog computing in smart healthcare, and challenges and issues faced by smart healthcare and it presents a wider scope as it is not intended for a particular application such aspatient monitoring, disease detection, and diagnosing and the technologies used for developing this smart systems are outlined. Smart health bestows the quality of life. Convenient and comfortable living is made possible by the services provided by smart healthcare systems (SHSs). Since healthcare is a massive area with enormous data and a broad spectrum of diseases associated with different organs, immense research can be done to overcome the drawbacks of traditional healthcare methods. Deep learning with IoT can effectively be applied in the healthcare sector to automate the diagnosing and treatment process even in rural areas remotely. Applications may include disease prevention and diagnosis, fitness and patient monitoring, food monitoring, mobile health, telemedicine, emergency systems, assisted living, self-management of chronic diseases, and so on.

OB-MECC: An Efficient Confidentiality and Security Enhancement for Cloud Storage System

Cloud computing (CC) is one amongst the developing technologies, which get more attention from academia as well as industries. It offers diverse benefits like sharing computing resources, service flexibility, reducing costs, etc. The Cloud Services Provider (CSP) is accountable for the data that are delivered to the cloud. The threat of seeing the stored data and using sensitive raw data by strangers is the main barrier in the utilization of cloud services. So, Data Security (DS) along with privacy is the chief issue, which is an obstacle while adopting the CC. Countless techniques are existent for ensuring data confidentiality, but they do not completely give protection to the data. To trounce these drawbacks, this paper introduces the Obfuscation (OB) based Modified Elliptical Curve Cryptography (MECC) algorithm for protecting data as of malicious attacks, which is termed as OB-MECC. Primarily, the proposed method obfuscates the data before they are uploaded to the cloud. For the OB of the data, the proposed work employs methods like substitution cipher (SC), position update, Ceaser cipher, binary conversion, 8-bit binary conversion, decimal(), two complex(), and ASCII(). Then, encryption of the obfuscated data is done with the utilization of the MECC algorithm. After encryption, the data on the cloud is retrieved. The retrieved data is then decrypted by reversing the OB and encryption process to get the actual data. The outcomes corroborate that the confidentiality and security level are maximum for the proposed OB-MECC when contrasted to the existing approaches.

Hash Function Based Keyword Searchable Encryption Framework in Cloud Server Using MD5 and MECC

Cloud Computing expands its usability to various fields that utilize data and store it in a common space that is required for computing and the purpose of analysis as like the IoT devices. These devices utilize the cloud for storing and retrieving data since the devices are not capable of storing processing data on its own. Cloud Computing provides various services to the users like the IaaS, PaaS and SaaS. The major drawback that is faced by cloud computing include the Utilization of Cloud services for the storage of data that could be accessed by all the users related to cloud. The use of Public Key Encryptions with keyword search (PEKS) provides security against the untrustworthy thirdparty search capability on publicly encryption keys without revealing the data’s contents. But the Security concerns of PEKs arise when Inside Keywords Guessing attacks (IKGA), is identified in the system due to the untrusted server presume the keyword in trapdoor. This issue could be solved by using various algorithms like the Certificateless Hashed Public Key Authenticated Encryption with Keyword Search (CL-HPAEKS) which utilizes the Modified Elliptic Curve Cryptography (MECC) along with the Mutation Centred flower pollinations algorithm (CM-FPA) that is used in enhancing the performance of the algorithm using the Optimization in keys. The additional use of Message Digests 5 (MD5) hash function in the system enhances the security Level that is associated with the system. The system that is proposed achieves the security level performance of 96 percent and the effort consumed by the algorithm is less compared to the other encryption techniques.