An Energy Efficient Service Composition Mechanism Using a Hybrid Meta-heuristic Algorithm in a Mobile Cloud Environment

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms

A framework for QoS driven user-side cloud service management

This thesis presents a comprehensive framework that assists the cloud service user in making cloud service management decisions, such as service selection and migration. The proposed framework utilizes the QoS history of the available services for QoS forecasting and multi-criteria decision making. It then integrates all the inherent necessary processes, such as QoS monitoring, forecasting, service comparison and ranking to recommend the best and optimal decision to the user

Explainable, Security-Aware and Dependency-Aware Framework for Intelligent Software Refactoring

As software systems continue to grow in size and complexity, their maintenance continues to become more challenging and costly. Even for the most technologically sophisticated and competent organizations, building and maintaining high-performing software applications with high-quality-code is an extremely challenging and expensive endeavor. Software Refactoring is widely recognized as the key component for maintaining high-quality software by restructuring existing code and reducing technical debt. However, refactoring is difficult to achieve and often neglected due to several limitations in the existing refactoring techniques that reduce their effectiveness. These limitation include, but not limited to, detecting refactoring opportunities, recommending specific refactoring activities, and explaining the recommended changes. Existing techniques are mainly focused on the use of quality metrics such as coupling, cohesion, and the Quality Metrics for Object Oriented Design (QMOOD). However, there are many other factors identified in this work to assist and facilitate different maintenance activities for developers: 1. To structure the refactoring field and existing research results, this dissertation provides the most scalable and comprehensive systematic literature review analyzing the results of 3183 research papers on refactoring covering the last three decades. Based on this survey, we created a taxonomy to classify the existing research, identified research trends and highlighted gaps in the literature for further research. 2. To draw attention to what should be the current refactoring research focus from the developers’ perspective, we carried out the first large scale refactoring study on the most popular online Q&A forum for developers, Stack Overflow. We collected and analyzed posts to identify what developers ask about refactoring, the challenges that practitioners face when refactoring software systems, and what should be the current refactoring research focus from the developers’ perspective. 3. To improve the detection of refactoring opportunities in terms of quality and security in the context of mobile apps, we designed a framework that recommends the files to be refactored based on user reviews. We also considered the detection of refactoring opportunities in the context of web services. We proposed a machine learning-based approach that helps service providers and subscribers predict the quality of service with the least costs. Furthermore, to help developers make an accurate assessment of the quality of their software systems and decide if the code should be refactored, we propose a clustering-based approach to automatically identify the preferred benchmark to use for the quality assessment of a project. 4. Regarding the refactoring generation process, we proposed different techniques to enhance the change operators and seeding mechanism by using the history of applied refactorings and incorporating refactoring dependencies in order to improve the quality of the refactoring solutions. We also introduced the security aspect when generating refactoring recommendations, by investigating the possible impact of improving different quality attributes on a set of security metrics and finding the best trade-off between them. In another approach, we recommend refactorings to prioritize fixing quality issues in security-critical files, improve quality attributes and remove code smells. All the above contributions were validated at the large scale on thousands of open source and industry projects in collaboration with industry partners and the open source community. The contributions of this dissertation are integrated in a cloud-based refactoring framework which is currently used by practitioners.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/171082/1/Chaima Abid Final Dissertation.pdfDescription of Chaima Abid Final Dissertation.pdf : Dissertatio

Proceedings der 11. Internationalen Tagung Wirtschaftsinformatik (WI2013) - Band 1

The two volumes represent the proceedings of the 11th International Conference on Wirtschaftsinformatik WI2013 (Business Information Systems). They include 118 papers from ten research tracks, a general track and the Student Consortium. The selection of all submissions was subject to a double blind procedure with three reviews for each paper and an overall acceptance rate of 25 percent. The WI2013 was organized at the University of Leipzig between February 27th and March 1st, 2013 and followed the main themes Innovation, Integration and Individualization.:Track 1: Individualization and Consumerization Track 2: Integrated Systems in Manufacturing Industries Track 3: Integrated Systems in Service Industries Track 4: Innovations and Business Models Track 5: Information and Knowledge ManagementDie zweibändigen Tagungsbände zur 11. Internationalen Tagung Wirtschaftsinformatik (WI2013) enthalten 118 Forschungsbeiträge aus zehn thematischen Tracks der Wirtschaftsinformatik, einem General Track sowie einem Student Consortium. Die Selektion der Artikel erfolgte nach einem Double-Blind-Verfahren mit jeweils drei Gutachten und führte zu einer Annahmequote von 25%. Die WI2013 hat vom 27.02. - 01.03.2013 unter den Leitthemen Innovation, Integration und Individualisierung an der Universität Leipzig stattgefunden.:Track 1: Individualization and Consumerization Track 2: Integrated Systems in Manufacturing Industries Track 3: Integrated Systems in Service Industries Track 4: Innovations and Business Models Track 5: Information and Knowledge Managemen

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Predictive Modeling for Navigating Social Media

Social media changes the way people use the Web. It has transformed ordinary Web users from information consumers to content contributors. One popular form of content contribution is social tagging, in which users assign tags to Web resources. By the collective efforts of the social tagging community, a new information space has been created for information navigation. Navigation allows serendipitous discovery of information by examining the information objects linked to one another in the social tagging space. In this dissertation, we study prediction tasks that facilitate navigation in social tagging systems. For social tagging systems to meet complex navigation needs of users, two issues are fundamental, namely link sparseness and object selection. Link sparseness is observed for many resources that are untagged or inadequately tagged, hindering navigation to the resources. Object selection is concerned when there are a large number of information objects that are linked to the current object, requiring to select the more interesting or relevant ones for guiding navigation effectively. This dissertation focuses on three dimensions, namely the semantic, social and temporal dimensions, to address link sparseness and object selection. To address link sparseness, we study the task of tag prediction. This task aims to enrich tags for the untagged or inadequately tagged resources, such that the predicted tags can serve as navigable links to these resources. For this task, we take a topic modeling approach to exploit the latent semantic relationships between resource content and tags. To address object selection, we study the task of personalized tag recommendation and trend discovery using social annotations. Personalized tag recommendation leverages the collective wisdom from the social tagging community to recommend tags that are semantically relevant to the target resource, while being tailored to the tagging preferences of individual users. For this task, we propose a probabilistic framework which leverages the implicit social links between like-minded users, i.e. who show similar tagging preferences, to recommend suitable tags. Social tags capture the interest of the users in the annotated resources at different times. These social annotations allow us to construct temporal profiles for the annotated resources. By analyzing these temporal profiles, we unveil the non-trivial temporal trends of the annotated resources, which provide novel metrics for selecting relevant and interesting resources for guiding navigation. For trend discovery using social annotations, we propose a trend discovery process which enables us to analyze trends for a multitude of semantics encapsulated in the temporal profiles of the annotated resources

Architectural stability of self-adaptive software systems

This thesis studies the notion of stability in software engineering with the aim of understanding its dimensions, facets and aspects, as well as characterising it. The thesis further investigates the aspect of behavioural stability at the architectural level, as a property concerned with the architecture's capability in maintaining the achievement of expected quality of service and accommodating runtime changes, in order to delay the architecture drifting and phasing-out as a consequence of the continuous unsuccessful provision of quality requirements. The research aims to provide a systematic and methodological support for analysing, modelling, designing and evaluating architectural stability. The novelty of this research is the consideration of stability during runtime operation, by focusing on the stable provision of quality of service without violations. As the runtime dimension is associated with adaptations, the research investigates stability in the context of self-adaptive software architectures, where runtime stability is challenged by the quality of adaptation, which in turn affects the quality of service. The research evaluation focuses on the effectiveness, scale and accuracy in handling runtime dynamics, using the self-adaptive cloud architectures