Utilization Of Cloud Services Research Articles

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.

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.

Adaptive context‐aware service optimization in mobile cloud computing accounting for security aspects

SummaryIn this article, we present an original agent‐based adaptive task scheduling system which optimizes the performance of services in the mobile cloud computing environment using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the mobile device and the cloud. Decisions are made taking into account the context (e.g., network connection type, location, security level). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online on a mobile device. To verify the solution proposed, appropriate software has been developed and a series of experiments has been conducted. Results demonstrate that owing to the experience gathered and the learning process performed, the decision module becomes more efficient in assigning the task to either the mobile device or cloud resources. In the face of presented improvements, the security issues inherent in the context of mobile services/applications and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often ruling out the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering performance.

GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.

Cloud-based Healthcare data management Framework

Cloud computing services changed the way the data are managed across the healthcare system that can improve patient care. Currently, most healthcare organizations are using cloud-based applications and related services to deliver better healthcare facilities. But architecting a cloud-based healthcare system needs deep knowledge about the working nature of these services and the requirements of the healthcare environment. The success is based on the usage of appropriate cloud services in the architecture to manage the data flow across the healthcare system.Cloud service providers offer a wide variety of services to ingest, store and process healthcare data securely. The top three public cloud providers- Amazon, Google, and Microsoft offers advanced cloud services for the solution that the healthcare industry is looking for. This article proposes a framework that can effectively utilize cloud services to handle the data flow among the various stages of the healthcare infrastructure. The useful cloud services for ingesting, storing and analyzing the healthcare data for the proposed framework, from the top three cloud providers are listed in this work. Finally, a cloud-based healthcare architecture using Amazon Cloud Services is constructed for reference.

Cache optimization cloud scheduling (COCS) algorithm based on last level caches

<p><span>Recently, the utilization of cloud services like storage, various software, networking resources has extremely enhanced due to widespread demand of these cloud services all over the world. On the other hand, it requires huge amount of storage and resource management to accurately cope up with ever-increasing demand. The high demand of these cloud services can lead to high amount of energy consumption in these cloud centers. Therefore, to eliminate these drawbacks and improve energy consumption and storage enhancement in real time for cloud computing devices, we have presented Cache Optimization Cloud Scheduling (COCS) Algorithm Based on Last Level Caches to ensure high cache memory Optimization and to enhance the processing speed of I/O subsystem in a cloud computing environment which rely upon Dynamic Voltage and Frequency Scaling (DVFS). The proposed COCS technique helps to reduce last level cache failures and the latencies of average memory in cloud computing multi-processor devices. This proposed COCS technique provides an efficient mathematical modelling to minimize energy consumption. We have tested our experiment on Cybershake scientific dataset and the experimental results are compared with different conventional techniques in terms of time taken to accomplish task, power consumed in the VMs and average power required to handle tasks.</span></p>

Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (i.e., transcoding) video streams to match the characteristics of viewers’ devices (e.g., in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.