A Cloud-Based Service for Affordable Cost Analysis

Cost reduction and cost-awareness were, are, and probably will always be one of the major concerns of all organizations worldwide. This has been accentuated by difficult economic times, and is particularly true among small and medium enterprises (SMEs) where competition is even fiercer. But as essential as it looks, advanced costing techniques are expensive to implement and require expertise not present in most organizations, making them inaccessible to most of SMEs. In this paper we present a proposal of a cloud-based solution offered as a service designed to address this problem based on re-utilization, integration with ERPs and better data visualization that will make it affordable and easier to use for SMEs. This proposal was demonstrated with a limited prototype applied in a community pharmacy and it was evaluated through interviews with experts and potential users

Cloud Workload Allocation Approaches for Quality of Service Guarantee and Cybersecurity Risk Management

It has become a dominant trend in industry to adopt cloud computing --thanks to its unique advantages in flexibility, scalability, elasticity and cost efficiency -- for providing online cloud services over the Internet using large-scale data centers. In the meantime, the relentless increase in demand for affordable and high-quality cloud-based services, for individuals and businesses, has led to tremendously high power consumption and operating expense and thus has posed pressing challenges on cloud service providers in finding efficient resource allocation policies. Allowing several services or Virtual Machines (VMs) to commonly share the cloud\u27s infrastructure enables cloud providers to optimize resource usage, power consumption, and operating expense. However, servers sharing among users and VMs causes performance degradation and results in cybersecurity risks. Consequently, how to develop efficient and effective resource management policies to make the appropriate decisions to optimize the trade-offs among resource usage, service quality, and cybersecurity loss plays a vital role in the sustainable future of cloud computing. In this dissertation, we focus on cloud workload allocation problems for resource optimization subject to Quality of Service (QoS) guarantee and cybersecurity risk constraints. To facilitate our research, we first develop a cloud computing prototype that we utilize to empirically validate the performance of different proposed cloud resource management schemes under a close to practical, but also isolated and well-controlled, environment. We then focus our research on the resource management policies for real-time cloud services with QoS guarantee. Based on queuing model with reneging, we establish and formally prove a series of fundamental principles, between service timing characteristics and their resource demands, and based on which we develop several novel resource management algorithms that statically guarantee the QoS requirements for cloud users. We then study the problem of mitigating cybersecurity risk and loss in cloud data centers via cloud resource management. We employ game theory to model the VM-to-VM interdependent cybersecurity risks in cloud clusters. We then conduct a thorough analysis based on our game-theory-based model and develop several algorithms for cybersecurity risk management. Specifically, we start our cybersecurity research from a simple case with only two types of VMs and next extend it to a more general case with an arbitrary number of VM types. Our intensive numerical and experimental results show that our proposed algorithms can significantly outperform the existing methodologies for large-scale cloud data centers in terms of resource usage, cybersecurity loss, and computational effectiveness

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Healthcare Data Analytics on the Cloud

Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution

Analysis and Summary of Feasible Marketplace Business Models Covering All Trials

This report analyses the market for cloud services of the type that POSTMARK is pioneering including type, scale, readiness and barriers to adoption. The POSTMARK trials are reviewed in terms of the business benefits that the services offer, the validation of these by those involved in the trials, and the further work needed to move from trials to a fully commercial and production setting. Business benefits were wide ranging and included lowering costs, better meeting of deadlines, increase in volume of business, and improved customer service. Response from the trials was positive in all cases. This was echoed by the two public demonstration events hosted by the project in 2012 where the attendees (around 100 in total from the film and post communities) were positive about the services that POSTMARK is offering. The main barriers encountered were security, the need for high levels of availability and trust, the need for complete out-of-the box solutions, and of course concerns over cost. These mirror findings in industry surveys and analyst reports. These barriers are not insurmountable but do require further investment to address. The key question is whether the market as a whole is sufficiently aware of, and ready to adopt, new services of the type developed by POSTMARK.Indications are good, but large scale adoption would be necessary to give sufficient return on the investmentneeded to provide fully operational POSTMARK services on a supported and commercial basis