A Model Traceability Framework for Network Service Management

Automating the enactment of processes using model-driven methods and tools paves the way for streamlining or optimizing these processes. Establishing traceability in automated processes is instrumental in carrying out analysis of the process and the involved artifacts. In this thesis, we propose a traceability information generation, visualization and analysis approach integrated with process modelling and enactment. A process model (PM) defined as an Activity Diagram has associated model transformations implementing the various activities and actions in the process. Enactment of the PM is carried out with the use of model transformation chaining in cooperation with model management means, in particular, megamodelling. We have incorporated both traceability in the small (at the model transformation level) and traceability in the large (at the PM level) in our approach. The traceability information is retained in the megamodel and forms the basis for traceability analysis of the enacted process. We have built a change impact analysis which allows the impact of a change in a model involved in the process to be assessed with the help of the derived megamodel. We further extended our approach with the notion of intents. We propose the usage of intents at both the PM and model-transformation levels as part of our traceability information. We define intents as information representing the objective of the PM actions/activities and their implementations. Furthermore, we have incorporated traceability visualization support to visualize trace links relating models at different levels through the captured intents. The intent-enriched traceability information and the enhanced visualization enable semantically richer traceability analysis. We applied our work to Network Service (NS) management in the context of the Network Functions Virtualization (NFV) paradigm.We believe automation of the orchestration and management of network services can progress rapidly with the help of model-driven engineering methods and tools. We applied our approach on a NS design process to analyze the impact of changing input models on output models as well as to show the benefits of intents not only in the context of this process, but also for the whole NS lifecycle management operations. Our work is concretized in a tool, MAPLE-T, built as an Eclipse plugin. It extends MAPLE, an integrated process modelling and enactment environment

A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Challenges and Opportunities in Applied System Innovation

This book introduces and provides solutions to a variety of problems faced by society, companies and individuals in a quickly changing and technology-dependent world. The wide acceptance of artificial intelligence, the upcoming fourth industrial revolution and newly designed 6G technologies are seen as the main enablers and game changers in this environment. The book considers these issues not only from a technological viewpoint but also on how society, labor and the economy are affected, leading to a circular economy that affects the way people design, function and deploy complex systems

Live Testing of Cloud Services

Service providers use the cloud due to the dynamic infrastructure it offers at a low cost. However, sharing the infrastructure with other service providers as well as relying on remote services that may be inaccessible from the development environment create major limitations for development time testing. Modern service providers have an increasing need to test their services in the production environment. Such testing helps increase the reliability of the test results and detect problems that could not be detected in the development environment such as the noisy neighbor problem. Furthermore, testing in production enables other software engineering activities such as fault prediction and fault localization and makes them more efficient. Test interferences are a major problem for testing in production as they can have damaging effects ranging from unreliable and degraded performance to a malfunctioning or inaccessible system. The countermeasures that are taken to alleviate the risk of test interferences are called test isolation. Existing approaches for test isolation have limited applicability in the cloud context because the assumptions under which they operate are seldom satisfied in the cloud context. Moreover, when running tests in production, failures can happen and whether they are due to the testing activity or not the damage they cause cannot be ignored. To deal with such issues and manage to quickly get the system back to a healthy state in the case of a failure, human intervention should be reduced in the orchestration and execution of testing activities in production. Thus, the need for a solution that automates the orchestration of tests in production while taking into consideration the particularity of a cloud system such as the existence of multiple fault tolerance mechanisms. In this thesis, we define live testing as testing a system in its production environment, while it is serving, without causing any intolerable disruption to its usage. We propose an architecture that can help cope with the major challenges of live testing, namely reducing human intervention and providing test isolation. Our proposed architecture is composed of two building blocks, the Test Planner and the Test Execution Framework. To make the solution we are proposing independent from the technologies used in a cloud system, we propose the use of UML Testing Profile (UTP) to model the artifacts involved in this architecture. To reduce human intervention in testing activities, we start by automating test execution and orchestration in production. To achieve this goal, we propose an execution semantics that we associate with UTP concepts that are relevant for test execution. Such an execution semantics represent the behavior that the Test Execution Framework exhibits while executing tests. We propose a test case selection method and test plan generation method to automate the activities that are performed by the Test Planner. To alleviate the risk of test interferences, we also propose a set of test methods that can be used for test isolation. As opposed to existing test isolation techniques, our test methods do not make any assumptions about the parts of the system for which test isolation can be provided, nor about the feature to be tested. These test methods are used in the design of test plans. In fact, the applicability of each test method varies according to several factors including the risk of test interferences that parts of the system present, the availability of resources, and the impact of the test method on the provisioning of the service. To be able to select the right test method for each situation, information about the risk of test interference and the cost of test isolation need to be provided. We propose a method, configured instance evaluation method, that automates the process of obtaining such information. Our method evaluates the software involved in the realization of the system in terms of the risk of test interference it presents, and the cost to provide test isolation for that software. In this thesis, we also discuss the feasibility of our proposed methods and evaluate the provided solutions. We implemented a prototype for the test plan generation and showcased it in a case study. We also implemented a part of the configured instance evaluation method, and we show that it can help confirm the presence of a risk of test interference. We showcase one of our test methods on a case study using an application deployed in a Kubernetes managed cluster. We also provide proof of the soundness of our execution semantics. Furthermore, we evaluate, in terms of the resulting test plan’s execution time, the algorithms involved in the test plan generation method. We show that for two of the activities in our solution our proposed algorithms provide optimal solutions; and, for one activity we identify in which situations our algorithm does not manage to give the optimal solution. Finally, we prove that our test case selection method reduces the test suite without compromising the configuration fault detection power

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

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen