Acceptance Of Cloud Computing Research Articles

The influence of dependability in cloud computing adoption

Cloud computing (CC) has many benefits, so its use has spread rapidly, particularly in the business sector. An important consideration in the acceptance of CC is whether the CC system is dependable, and it can differ among industry and service type. However, little research has considered the effect of dependability (composed of availability, reliability, security, maintainability) on CC acceptance. Especially, group comparisons between high IT-intensive (Hi-ITi) and low IT-intensive (Lo-ITi) industries have not been reported, nor have comparisons between software-as-a-service (SaaS) and platform-as-a-service (PaaS)/infrastructure-as-a-service (IaaS). This study aims to explore how the dependability of CC affects users’ intent to accept it, with focus on how this intent is affected by intensity of IT use (by industry) and by the type of CC service used. To validate the proposed model, this study applied structural equation modeling and conducted multi-group analysis. A total of 230 business managers in South Korea represent the sample for our study. For the full dataset, the three dependability attributes (availability, reliability, security) do not affect the usefulness of CC, but do affect the ease of use of CC. The usefulness of CC is a determinant for positive intention to accept CC, whereas the ease of use of CC is not. Maintainability is the strongest determinant of CC adoption for the full dataset, and for all individual groups, except those that use SaaS. For Hi-ITi and Lo-ITi industries, results show that managers show no differences in their perceptions of the effect of dependability attributes (availability, reliability, security) on the usefulness and the ease of CC. The absence of such a difference in managers’ perception also applies to the relationship between two core variables of TAM (i.e., perceived usefulness, perceived ease of use) and behavioral intention to accept CC. For SaaS and PaaS/IaaS, managers have different perceptions of security on the usefulness of CC, and the effect of the usefulness of CC on the intention to accept CC. The findings can provide academic researchers and industry practitioners with a differentiated and in-depth perspective on the understanding and the spread of CC.

Exploring Cloud Computing Readiness and Acceptance in Higher Education Institution: A PLS-SEM Approach

Abstract: The COVID-19 pandemic has altered nearly every aspect of life, including education. Technology has replaced face-to-face teaching and learning nearly totally. This health disaster has accelerated digitization in the world of education, which was previously only available without a repulsive factor. Cloud computing technology has been widely used in education, including higher education, enabling teaching resources, educational information, notes, lectures, and academic assessments to be accessed and shared online. Yet, in celebrating the greatness of technology, are users ready to accept an explosion of information resources and access openly online through cloud-based services? Thus, this research will investigate the Higher Education Institution (HEI) users' readiness and acceptance of cloud computing. The research employs the Technology Acceptance Model (TAM) and Technology Readiness Index (TRI) model together with Structural Equation Modeling (SEM) to investigate 470 individuals from HEIs in Malaysia. Perceived Ease of Use and Perceived Usefulness are found to be positively significant in explaining why a user decides to use cloud computing. Optimism and innovativeness have found to affect the factors of technology acceptance significantly. In contrast, discomfort and insecurity do not affect technology acceptance factors, except for insecurity that negatively impacts Perceived Usefulness. This study contributes to another finding to studying technology readiness and acceptance, especially in higher education.
 
 Keywords: Cloud Computing, Technology Readiness, Technology Acceptance, HEI, SEM

A Conceptual Framework for Secure Cloud Migration

Cloud computing is a budding web-based technology that tends to prevail in our environment, especially in the areas of computing and information technology that require large-scale network computing. Cloud computing is a shared set of services gaining popularity due to its profitability, availability, and high production. Just on the security grounds, cloud computing’ immense potential cannot be overlooked: ongoing research and the investigation of consistent, robust, and integrated cloud computing security models may be the solitary motivational pathway. There are three service and four deployment models in cloud computing. Cloud computing, together with its several advantages, also brings a much more difficult condition concerning privacy, authenticated access, data protection, etc. Because of these problems, the acceptance of cloud computing is becoming difficult in the current era. In this study, several security concerns related to reliability and data privacy, main aspects impacting cloud computing, were tackled, and recommendations on specific areas were also discussed.

Acceptance of cloud computing in an airline company based on Roger’s diffusion of innovation theory

using cloud computing develops, one of the problems that managers face at the organization level is that the personnel cannot work with these systems, users do not accept these new systems and the problem of accepting these technologies, effective factors in accepting them and the existing barriers in accepting them by users are very important. In many organizations wherein cloud computing has been launched, a time period is required to accept this new system by their personnel. If this time period is less and the personnel can work with these systems earlier, the productivity increases in the organization. The present paper aims the analysis of effective factors on acceptance of cloud computing by personnel working in Birjand International Airport in South Khorasan County (Iran) based on Roger?s diffusion of innovation theory. Examination of effective factors on acceptance of cloud computing in an organization can pave the way for improving its implementation and performance. This research has been done by descriptive survey method and its community includes personnel working in different official and informatics departments of Birjand International Airport. The data gathering instrument was questionnaire and for determining questionnaire validity opinions of masters and for determining consistency Cronbach's alpha has been used. For data analysis descriptive and inferential statistics have been used. The results of present paper indicated that there is meaningful and positive relationship between acceptance features of cloud computing and perception elements of testability, comparative advantage, visibility, complexity, and compatibility of cloud computing with its acceptance rate and there is meaningful and negative relationship between features of perception of not needing cloud computing and its acceptance rate. But there was no meaningful relationship between perception of cloud computing test opportunity and its acceptance rate. As one of the modern ways of providing electronic service has been cloud computing in recent years and it has many benefits for air transportation systems, this paper presents a model for analysis of effective factors in acceptance of cloud computing among personnel of an airline company (airport) and it can be used for examining cloud acceptance in other air companies.

An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud

With the increasing popularity and acceptance of cloud computing, it is being applied in services like executing large-scale applications, where cloud environment is selected by the scientific associations to easily execute the computation intensive workflows. However, cloud computing can have higher failure rates due to the larger number of servers and components filled with the intensive workloads. These failures may lead to the unavailability of virtual machines (VMs) for computation. Hence, this issue of fault occurrences can be tolerated by adopting an effective and efficient fault tolerant strategy. The goal of our research in this paper is to develop an adaptive fault detector strategy based on Improved Differential Evolution (IDE) algorithm in cloud computing that can minimize the energy consumption, the makespan, the total cost and, at the same time, tolerate up faults when scheduling scientific workflows. This proposed work applies an adaptive network-based fuzzy inference system (ANFIS) prediction model to proactively control resource load fluctuation that increases the failure prediction accuracy before fault/failure occurrence. In addition, it applies a reactive fault tolerance technique for when a processor fails and the scheduler must allocate a new VM to execute the workflow tasks. The experimental results show that compared with existing techniques, the proposed approach significantly improves the overall scheduling performance, achieves a higher degree of fault tolerance with high HyperVolume (HV) compared with the ICFWS, IDE, and ACO algorithms, minimizes the makespan, the energy consumption and task fault ratio, and reduces the total cost.

ARCN: Authenticated Routing on Cloud Network to Mitigate Insider Attacks on Infrastructure as a Service

Cloud security is one of the major issues which are affecting the acceptance of Cloud computing (CC). In spite of the number of benefits offered by CC, it also suffers from a myriad of security vulnerabilities. Security is the prime concern while we are accessing the resources from the Cloud through the Internet. This research work is primarily on IAAS (Infrastructure as a service). Here a protocol called Authenticated Routing on Cloud Network (ARCN) has been proposed, to secure the communication route between the client and service provider. The proposed protocol ARCN uses the public key cryptography to mitigate the various security attacks like malicious insider attacks, Spoofing, Falsified routes, DoS, etc.

Modelling of Cloud Computing Enablers Using MICMAC Analysis and TISM

This article describes how Cloud Computing is not just a buzzword but a shift from IT departments to the outsourcing vendors without impacting business efficiency. Some organizations are moving towards cloud computing but many have resistance to adopting cloud computing due to limitations in knowledge and awareness of the classifying elements, which effect decisions on the acceptance of cloud computing. Therefore, this article has focused on accumulating the elements, which can act as enablers, by reviewing existing literature and studies from both professional and academic viewpoints. All the identified enablers have been structurally modeled to develop the relationship matrix and establish the driving power and dependence power of every element. This is done by employing Total Interpretive Structural Modeling (TISM) and Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis.

Smart Virtual Machine Placement Using Learning Automata to Reduce Power Consumption in Cloud Data Centers

Today, cloud computing is one of the most challenging research topics in the field of information technology. It is so important for computer researchers that it was included on a list of top ten technologies in the world. Data centers include reservoirs where processing power can meet the needs of many users computing. The popularity and acceptance of cloud computing has increased the number of these centers in recent years. One of the challenging issues in cloud computing environments is high energy consumption in data centers, which has been ignored in the corporate competition to develop data centers. High energy consumption by data centers leads to increased costs, as well as CO2 emissions. Researchers are now struggling to find an effective approach to decrease energy consumption in data centers. In recent years, many attempts have been made to reduce the power consumption of data centers, and many approaches have been proposed to reduce power consumption, such as hardware and software approaches and approaches using virtualization technology. In fact, placement of a virtual machine (VM) means finding a suitable physical place for the VM. The placement goal can either maximize the usage of available resources or it can save power by being able to shut down some servers. In this paper, we present an approach based on a best-fit decreasing (BFD) algorithm, which uses learning automata to reach a compromise between decreasing energy consumption and violating service level agreements.

Pyramid: A Case Study of Cyber Security in India

As the world becomes increasingly interlinked through the Internet, cyberspace frauds are also on the rise. This is a case study on a company, Pyramid Cyber Security (P) Ltd., which specializes in digital crime, fraud and forensic solutions and services in India. Over the years, the company has established several digital forensics laboratories and security projects for agencies in law enforcement, the public sector and corporate organizations. With the scalability, flexibility and economic advantage offered by cloud computing, more and more organizations are moving towards cloud for their applications. With all the benefits of cloud computing, it also opens up a company to the danger of digital crime and security breaches on the cloud platform. This has thrown open new vistas for Pyramid, putting it in a dilemma of whether to focus on the existing business or explore new opportunities in cloud forensics investigation thrown by the wide acceptance of cloud computing. It also poses the question whether a company should go in for pre-incident or post-incident digital network security architecture. It is a teaching case.

User value design for cloud courseware system

A cloud learning environment enables an enriched learning experience compared to conventional methods of learning. Employing a value-sensitive approach, we undertook theoretical and empirical analyses to explore the values that influence potential users’ adoption of cloud courseware, by integrating cognitive motivations and user values as primary determining factors. We found that users’ intentions and behaviours are largely influenced by their perceptions of what is valuable about the cloud courseware in terms of sociability, learnability, and usability. These evaluations were found to be significant antecedents of cloud-computing intentions. This study makes a contribution to theory development as our model extends existing technology acceptance models and can be used to design user interfaces and promote the acceptance of cloud computing. For practical applications, the study findings can be used by industries promoting cloud services to increase user acceptance by addressing user values and incorporating them into cloud-computing design.

Users’ Acceptance of Cloud Computing in Saudi Arabia

Cloud computing has become a popular topic in the research community because of its ability to transform computer software, platforms, and infrastructure as a service. However, cloud computing literature currently lacks user studies despite the fact that users play a crucial role in the success and failure of emerging technologies. This paper presents a study aimed at investigating users’ acceptance of cloud computing in Saudi Arabia. As a baseline, it utilizes the Technology Acceptance Model (TAM) along with five additional factors believed to affect users’ acceptance of new technology in the region in order to achieve the study goals. These factors are gender, age, education level, job domain, and nationality. The results demonstrated a high level of acceptance of cloud computing and a valid TAM in its standard form. The results also indicated that age, education, job domain, and nationality have a significant effect on users’ attitudes toward the adoption of cloud computing. However, no difference was found in the attitude toward the adoption of cloud computing between male and female employees.