Cloud Infrastructure Services Selection and Evaluation

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant increase of new cloud services almost every month by both large corporations (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g. Rackspace and FlexiScale), the selection scenarios become more and more complex. This is aggregated by confusing and ambiguous terminology and non-standardized interfaces. This is challenging for decision-makers such as application developers and chief information officers as they are overwhelmed by various choices available. In this thesis, I will address the above challenges by developing several techniques. Firstly, I define the Cloud Computing Ontology (CoCoOn). CoCoOn defines concepts, features, attributes and relations of Cloud infrastructure services. Secondly, I propose a service selection method that adopts an analytic hierarchy process (AHP)-based multi-criteria decision-making technique. It allows users to define multiple design-time constraints like renting costs, data centre locations, service features and real-time constraints, such as end-to-end message latency and throughput. These constraints are then matched against our model to compute the possible best-fit combinations of cloud Infrastructure, offered as a Service (IaaS). Pairwise comparisons are used to help users determine a relative preference among a pool of nonnumerical attributes. Criteria that are taken into consideration during comparison can be grouped into two categories: the benefit and the cost. Based on this, I define a cost-benefit-ratio-based evaluation function to calculate the ranking for Cloud service options. Thirdly, I suggest a theory-based queuing approach for estimating IaaS usage. Queuing theory is a widely studied method in QoS modelling and optimization. From the infrastructure system administrator perspective, I explore several ways to apply the queuing theory model to estimate the best-fit resource allocation for achieving the desired SLA. Finally, the thesis shows how an integrated system, CloudRecommender, can be built from our proposed approaches

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen