Converged Reality: A Data Management Research Agenda for a Service-, Cloud-, and Data-Driven Era

We are accustomed to distinguishing activities that occur on or through the Internet as distinct from activities that occur in the physical world: online versus offline, virtual reality versus reality, and so on. As Internet-based services have evolved, this distinction has continued to blur. We now have a converged reality: the online does not merely augment the offline; rather, the two are increasingly indistinguishable. Mobility, cloud computing, servicedriven technology, cognitive computing, and Big Data analytics are some of the distinct but related innovations driving this shift. Because the shift is happening in pieces across multiple areas and sectors, our converged reality is emergent and grassroots, not a carefully planned joint effort. There are therefore areas that have been and will be slow to acknowledge and adapt to this shift; data management is one of these areas. This paper describes how this converged reality grew from previous research into bridging online and offline worlds, and how it will lead to a cognitive reality. It identifies enablers and dampeners, and describes a data management research agenda specifically for converged reality. The proposed research agenda is intended to spark discussion and engage further work in this area

Development of an intelligent e-commerce assurance model to promote trust in online shopping environment

Electronic commerce (e-commerce) markets provide benefits for both buyers and sellers; however, because of cyber security risks consumers are reluctant to transact online. Trust in e-commerce is paramount for adoption. Trust as a subject for research has been a term considered in depth by numerous researchers in various fields of study, including psychology and information technology. Various models have been developed in e-commerce to alleviate consumer fears, thus promoting trust in online environments. Third-party web seals and online scanning tools are some of the existing models used in e-commerce environments, but they have some deficiencies, e.g. failure to incorporate compliance, which need to be addressed. This research proposes an e-commerce assurance model for safe online shopping. The machine learning model is called the Page ranking analytical hierarchy process (PRAHP). PRAHP builds complementary strengths of the analytical hierarchy process (AHP) and Page ranking (PR) techniques to evaluate the trustworthiness of web attributes. The attributes that are assessed are Adaptive legislation, Adaptive International Organisation for Standardisation Standards, Availability, Policy and Advanced Security login. The attributes were selected based on the literature reviewed from accredited journals and some of the reputable e-commerce websites. PRAHP’s paradigms were evaluated extensively through detailed experiments on business-to-business, business-to-consumer, cloud-based and general e-commerce websites. The results of the assessments were validated by customer inputs regarding the website. The reliability and robustness of PRAHP was tested by varying the damping factor and the inbound links. In all the experiments, the results revealed that the model provides reliable results to guide customers in making informed purchasing decisions. The research also reveals hidden e-commerce topics that have not received attention, which generates knowledge and opens research questions for future researchers. These ultimately made significant contributions in e-commerce assurance, in areas such as security and compliance through the fusing of AHP and PR, integrated into a decision table for alleviating trustworthiness anxiety in various e-commerce transacting partners, e-commerce platforms and markets.College of Engineering, Science and TechnologyD. Phil. Information System

Data Safety Contact Control Model of Cloud Computing

The progress of cloud computing are facing by many difficulties, which one of the most important challenge is data security problem. Everyone wants to use cloud computing due to cost saving and new agile business model which resulted by its dynamic, sharing, openness and highly centralized data. There complex data security challenges in cloud computing. From the view of users the research about data security focused on methodologies that ensuring the safety of data and storage. This paper provides a control model of a secured data access by on MAC access control. This is origin experience from the government cloud platform construction. This model provides the most important technical and management techniques with the security of data accessing. In shortly, the practical applications test showed that the model with corresponding control mechanism cloud meet the necessaries for reliable applications of government cloud. Keywords: Control Model of Access Control, Security of Cloud Computing, Access Control of Secured Data

Development of a secure monitoring framework for optical disaggregated data centres

Data center (DC) infrastructures are a key piece of nowadays telecom and cloud services delivery, enabling the access and storage of enormous quantities of information as well as the execution of complex applications and services. Such aspect is being accentuated with the advent of 5G and beyond architectures, since a significant portion of the network and service functions are being deployed as specialized virtual elements inside dedicated DC infrastructures. As such, the development of new architectures to better exploit the resources of DC becomes of paramount importanceThe mismatch between the variability of resources required by running applications and the fixed amount of resources in server units severely limits resource utilization in today's Data Centers (DCs). The Disaggregated DC (DDC) paradigm was recently introduced to address these limitations. The main idea behind DDCs is to divide the various computational resources into independent hardware modules/blades, which are mounted in racks, bringing greater modularity and allowing operators to optimize their deployments for improved efficiency and performance, thus, offering high resource allocation flexibility. Moreover, to efficiently exploit the hardware blades and establish the connections across them according to upper layer requirements, a flexible control and management framework is required. In this regard, following current industrial trends, the Software Defined Networking (SDN) paradigm is one of the leading technologies for the control of DC infrastructures, allowing for the establishment of high-speed, low-latency optical connections between hardware components in DDCs in response to the demands of higher-level services and applications. With these concepts in mind, the primary objective of this thesis is to design and carry out the implementation of the control of a DDC infrastructure layer that is founded on the SDN principles and makes use of optical technologies for the intra-DC network fabric, highlighting the importance of quality control and monitoring. Thanks to several SDN agents, it becomes possible to gather statistics and metrics from the multiple infrastructure elements (computational blades and network equipment), allowing DC operators to monitor and make informed decisions on how to utilize the infrastructure resources to the greatest extent feasible. Indeed, quality assurance operations are of capital importance in modern DC infrastructures, thus, it becomes essential to guarantee a secure communication channel for gathering infrastructure metrics/statistics and enforcing (re-)configurations, closing the full loop, then addressing the security layer to secure the communication channel by encryption and providing authentication for the server and the client

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G

Mobile Oriented Future Internet (MOFI)

This Special Issue consists of seven papers that discuss how to enhance mobility management and its associated performance in the mobile-oriented future Internet (MOFI) environment. The first two papers deal with the architectural design and experimentation of mobility management schemes, in which new schemes are proposed and real-world testbed experimentations are performed. The subsequent three papers focus on the use of software-defined networks (SDN) for effective service provisioning in the MOFI environment, together with real-world practices and testbed experimentations. The remaining two papers discuss the network engineering issues in newly emerging mobile networks, such as flying ad-hoc networks (FANET) and connected vehicular networks

Particle swarm optimization with composite particles in dynamic environments

This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1