An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach

Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac

A Hybrid Data-Driven Web-Based UI-UX Assessment Model

Today, a large proportion of end user information systems have their Graphical User Interfaces (GUI) built with web-based technology (JavaScript, CSS, and HTML). Some of these web-based systems include: Internet of Things (IOT), Infotainment (in vehicles), Interactive Display Screens (for digital menu boards, information kiosks, digital signage displays at bus stops or airports, bank ATMs, etc.), and web applications/services (on smart devices). As such, web-based UI must be evaluated in order to improve upon its ability to perform the technical task for which it was designed. This study develops a framework and a processes for evaluating and improving the quality of web-based user interface (UI) as well as at a stratified level. The study develops a comprehensive framework which is a conglomeration of algorithms such as the multi-criteria decision making method of analytical hierarchy process (AHP) in coefficient generation, sentiment analysis, K-means clustering algorithms and explainable AI (XAI)

AHP-based Adaptive Resource Selection for Cognitive Platform in Cloud Gaming Service

Cloud gaming service enables offloading heavy video-processing tasks up to the cloud server so that simple computers or mobile devices can be eligible to run sophisticated games, but on the expense of high network communications. In this regard, the adequate network utilization must be realized for delivering good gaming experiences to the game players. This necessitates a cognitive platform, which is capable of modifying its multimedia quality requirement in response to the network constraint, and notifying the cloud gaming server for updating the corresponded workload. In this regard, the Analytic Hierarchy Process (AHP) method has been proposed to deploy at the cognitive platform for cloud gaming service to select an optimal resource allocation strategy that satisfies various multimedia requirements and energy-awareness. Experiment results can confirm that the proposed method is flexible to enhance the capability of cloud gaming service in term of more efficient cloud gaming resource utilization, particularly during heavy-congested periods, while players’ quality of gaming experience can be still maintained under the mandate of intelligent agent on the player devices

Framework for collaborative knowledge management in organizations

Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value products and services. The capability to agilely respond to new market demands became a strategic pillar for innovation, and knowledge management could support organizations to achieve that goal. However, current knowledge management approaches tend to be over complex or too academic, with interfaces difficult to manage, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit knowledge management should be addressed to achieve knowledge handling with precise and semantically meaningful definitions. Moreover, with the increase of Internet usage, the amount of available information explodes. It leads to the observed progress in the creation of mechanisms to retrieve useful knowledge from the huge existent amount of information sources. However, a same knowledge representation of a thing could mean differently to different people and applications. Contributing towards this direction, this thesis proposes a framework capable of gathering the knowledge held by domain experts and domain sources through a knowledge management system and transform it into explicit ontologies. This enables to build tools with advanced reasoning capacities with the aim to support enterprises decision-making processes. The author also intends to address the problem of knowledge transference within an among organizations. This will be done through a module (part of the proposed framework) for domain’s lexicon establishment which purpose is to represent and unify the understanding of the domain’s used semantic

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

Technical Conception and Implementation of an IT-System supporting the flexible Distribution of Documents within a large scale Sales Organization

This thesis aims to discuss and present an IT system to distribute documents within a large scale sales organization. Therefore, different sales applications on the market are analyzed and described. Based on this analysis, requirements for the IT system are defined. The requirements are divided into client-side and server-side requirements. The server application is realized using an existing ECM system to support the content throughout the enterprise content life cycle. Therefore, an evaluation of existing ECM systems is performed with a criteria catalog based on the requirements. Afterwards, the conception and architecture of the IT system is described, followed by further insights into the technical implementation of the mobile application. Finally, the features of the overall IT system are discussed and an outlook on how to extend the IT system is presented