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The purpose of this paper is to define and describe virtualization of servers and cloud computing. Understanding the differences and similarities between the two technology models will help digital library managers make better decisions related to hosting of application services.

The paper is a general overview of the principles and techniques of virtualization and cloud computing.

Both virtualization and cloud computing can be effective methods of optimizing hardware resources used to run digital library applications; however, system managers should not overlook the potential for security problems and management problems given the outsourced nature of the computing resources.

The paper fills a gap in the digital library project management literature by providing an overview of the server virtualization and cloud computing models which could be applied to digital library projects.

This paper aims to examine the types of cloud computing, models, characteristics, descriptions, applications, considerations for use and benefits of the following technologies to library users. Cloud computing has taken libraries in Kenya by storm. Because of this, many libraries do not know what to do. They are at cross-roads. They lack policies to govern them. However, the recent realization by information professionals that cloud computing has a critical role to play in the provision of information services has created a desire to effectively harness and manage them for scholarly communication.

The research reviewed previous studies on the topic by examining both electronic and print information resources available in the libraries and internet.

The findings revealed that libraries in Kenya are yet to fully embrace cloud computing in their operations. There are mixed feelings by librarians on the degree at which they should integrate these technologies in their service provisions. However, there is a positive progress toward this noble venture.

Kenya as a developing nation has limited local content on the subject. The study established that some of the available literature is restricted by intellectual property rights. This forced the researcher to rely heavily on foreign literature for the study.

Appreciation of emerging cloud computing technologies by libraries is inevitable for the sustainability and management of modern libraries. Cloud computing makes libraries more competitive by adequately meeting user’s technological needs. This study will also act as a benchmark for the Government of Kenya to improve the current information communication technology (ICT) national policy.

Apart from revolutionalizing library operations and delivery of services, cloud computing will tremendously revolutionize the social-cultural and communication landscape of the society.

The paper provides vital information and insights into how libraries are embracing cloud computing in the provision and dissemination of varied information services to library users.

The purpose of this paper is to explain how enterprise resource planning (ERP) implementation evolves by cloud computing in different industries with different delivery models of cloud ERP. This paper also investigates infrastructure as a service (IaaS) as a delivery approach for cloud ERP. Case research on IaaS is rarely found in the literature. In addition, this paper intends to reveal how this transformation from on-premises to the cloud would influence the ERP implementation process.

A multiple-case study is conducted to identify the different deployed models of cloud ERP systems in the implementation projects. The influences of emerging cloud computing technology on ERP implementation are investigated by interviewing consultants related to the projects.

The findings illustrate that not only software as a service (SaaS) but also IaaS and platform as a service cloud computing services are widely applied in cloud ERP implementation. This study also indicates that certain technical limitations of cloud ERP might have a positive effect on the outcome of ERP implementation.

This study investigates how cloud computing influences ERP implementation from different aspects. The result identifies both SaaS and IaaS as two different approaches widely adopted in cloud ERP implementation. Besides, this study has discussed in-depth and analyzed these two cloud ERP paradigms in five factors, including functionality, performance, portability, security, cost and customization. The classification and suggestions are original to the literature.

The case deals with a chain of hospitals, that has grown vary fast in last few years as a result of various acquisitions and new developments. The hospital chain is lagging behind in use of technology. The IT department is inward looking and the focus is more on provide support services rather than strategic orientation. A new CIO takes charge of the IT department and decides to transform IT from playing a support to strategic role. He identifies cloud computing as a tool to take the leap. The case provides an opportunity to discuss the type of service and deployment models of benefits of cloud technology. A rough data to do financial evaluation of cloud technology is presented. Evaluation parameters that may be used to decide on cloud versus in-house technology are also discussed.

The purpose of this paper is to explore the evolving field of cloud computing and its usefulness for library technology departments. It seeks to consider what types of projects are good candidates for the cloud and which are not.

The authors spent a year trying out different projects using Amazon's Web Service. They discuss what went right and what went wrong. They brought up their own machine image and tested out web sites and applications in the cloud. They discuss the different types of cloud services, evaluating and choosing a provider and the types of projects which best fit into this architecture. Advantages and considerations are highlighted as well as an alternative to the public cloud.

There are some projects that are better suited to cloud computing than others. Flexibility and cost savings are the best reason for moving projects to the cloud. There are also good and valid reasons not to move some projects off into the cloud.

This is an on‐the‐ground look at running projects in the cloud that used to be done on back‐end servers. The bulk of the paper looks at infrastructure as a service. All of the work that has been done is in production and has been tested for over a year.

This study aims to explain the implementation of Cloud enterprise resource planning (ERP) system and underlying factors and challenges that might be practiced by the users. It also provides a comparison between traditional and Cloud ERP systems.

The study uses qualitative case study and analyzes the primary evidences from in-depth interviews. It conducts a thematic analysis of the interviews' findings. Furthermore, the current study uses three groups of factors (technological, management and environmental) that are expected to be best determinants of the Cloud ERP implementation.

The findings provide an evidence that using the Cloud EPR system, as alternative to on premise traditional ERP system, is constructive to the success of organizations and improve the quality of their decision-making process. The findings also reveal that effectiveness of implementing Cloud ERP is reliable on the provider's professionalism; hence resulting in issues related to minimize organizational independence.

This paper is subjected to case studies limitations, as it lacks rigor and generalization. The paper has important implications for practitioners and decision-makers alike as it presents real-life example about Cloud ERP implementation. It thus enhances decision-makers' ability to make a relevant reporting process in the small and medium enterprises (SMEs).

This study can be considered as a one of very few case studies that discusses Cloud ERP implementation in UAE organizations particularly SMEs. It also provides three groups of factors (technological, management and environmental) that are influenced by the Cloud ERP implementation.

The purpose of this paper is to classify and categorize the vulnerability types emerged with time as information technology (IT) systems evolved. This comparative study aims to compare the seriousness of the old well-known vulnerabilities that may still exist with lower possibility of happening with that of new technologies like cloud computing with Mobility access. Cloud computing is a new structure of IT that is becoming the main part of the new model of business environment. However, issues regarding such new hype of technology do not come without obstacles. These issues have to be addressed before full acceptability of cloud services in a globalized business environment. Businesses need to be aware of issues of concerns before joining the cloud services. This paper also highlights these issues and shows the comparison table to help businesses with appropriate decision-making when joining the cloud.

A historical review of emerged vulnerabilities as IT systems evolved was conducted, then these vulnerabilities were categorized into eight different categories, each of which composed of multiple vulnerability types. Simple scoring techniques were used to build a “risk” analysis table where each vulnerability type was given a score based on availability of matured solution and the likeliness of happening, then in case of vulnerability type, another score was used to derive the impact of such vulnerability. The resulted weighted score can be derived from the multiplication of likeliness to happen score with that of its impact in case it did happen. Percentage of seriousness represented by the percentage of the derived weighted score of each of the vulnerabilities can then be concluded. Similar table was developed for issues related to cloud computing environment in specific.

After surveying the historical background of IT systems and emerged vulnerabilities as well as reviewing the common malicious types of system vulnerabilities, this paper identifies 22 different types of vulnerability categorized in eight different categories. This comparative study explores amount of possible vulnerabilities in new technology like cloud computing services. Specific issues for cloud computing were also explored and a similar comparative study was developed on these issues. The result of the comparative study between all types of vulnerabilities since the start of IT system development till today’s technology of cloud computing, shows that the highest percentage vulnerability category was the one related to mobility access as mobile applications/systems are relatively newly emerged and do not have a matured security solution(s).

Learning from history, one can conclude the current risk factor in dealing with new technology like cloud computing. Businesses can realize that decision to join the cloud requires thinking about the issues mentioned in this paper and identifying the most vulnerability types to try to avoid them.

A new comparative study and new classification of vulnerabilities demonstrated with risk analysis using simple scoring technique.

The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.

The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.

Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.

Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.