Refining Transformation Rules For Converting UML Operations To Z Schema

The UML (Unified Modeling Language) has its origin in mainstream software engineering and is often used informally by software designers. One of the limitations of UML is the lack of precision in its semantics, which makes its application to safety critical systems unsuitable. A safety critical system is one in which any loss or misinterpretation of data could lead to injury, loss of human lives and/or property. Safety Critical systems are usually specified by very precisely and frequently required formal verification. With the continuous use of UML in the software industry, there is a need to augment the informality of software models produced to remove ambiguity and inconsistency in models for verification and validation. To overcome this well-known limitation of UML, formal specification techniques (FSTs), which are mathematically tractable, are often used to represent these models. Formal methods are mathematical techniques that allow software developers to produce softwares that address issues of ambiguity and error in complex and safety critical systems. By building a mathematically rigorous model of a complex system, it is possible to verify the system\u27s properties in a more thorough fashion than empirical testing. In this research, the author refines transformation rules for aspects of an informally defined design in UML to one that is verifiable, i.e. a formal specification notation. The specification language that is used is the Z Notation. The rules are applied to UML class diagram operation signatures iteratively, to derive Z schema representation of the operation signatures. Z representation may then be analyzed to detect flaws and determine where there is need to be more precise in defining the operation signatures. This work is an extension of previous research that lack sufficient detail for it to be taken to the next phase, towards the implementation of a tool for semi-automated transformation

Knowledge capturing and usage of evolving cloud application topologies

In the last few years, the advent of Cloud Computing has contributed to a considerable increase of the number of service offerings from a variety of providers and every time more applications are being partially or fully deployed in the cloud. Nowadays, experts invest considerable time and effort towards taking the maximum advantage of the benefits that the employment of cloud technologies brings. Such a wide spectrum of cloud offerings, however, increase the number of complex tasks and aspects that must be taken into account when distributing the components of an application such as the quality of service, adaptation to market changes, etc. Furthermore, the distribution of the components and the configuration of cloud resources may evolve over time and there is a lack of tooling support when it comes to assist the developers in the decision-making tasks of selecting an appropriate application distribution topology. One of the methods that human-beings use to solve problems consists of recalling past experiences and how they solved them at that time. Under this and other considerations the Case-Based Reasoning paradigm was conceived, as a mechanism for solving tasks by recalling past similar problems and adapting their solutions to new situations. This work aims to develop the concepts and mechanisms that enable the capturing and usage of knowledge that the evolution of a system brings along. Specifically, this thesis attempts to identify a set of characteristics that accurately describe a cloud application and to define the models that should be used to identify the cases solved in the past and whose solutions may be useful to solve a new problem. To achieve this, the Case-Based Reasoning with Similarity Retrieval approach is employed, to identify applications with similar characteristics, retrieve their solutions and offer means to refine them in order to obtain a distribution topology that fulfills the requirements of a given application. Furthermore, a prototypical implementation of the approach of this thesis is executed and also employed to validate the concepts and principles that this work follows

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

Verifying big data topologies by-design: a semi-automated approach

Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Apache Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors. Such complex architectures make possible refactoring of the applications a difficult task for software architects, as applications might be very different with respect to the initial designs. As an aid to designers and developers, we developed OSTIA (Ordinary Static Topology Inference Analysis) that allows detecting the occurrence of common anti-patterns across big data architectures and exploiting software verification techniques on the elicited architectural models. This paper illustrates OSTIA and evaluates its uses and benefits on three industrial-scale case-studies

A Process Model for the Integrated Reasoning about Quantitative IT Infrastructure Attributes

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently. This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness. Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.IT-Infrastrukturen können mit Attributen, wie Leistung und Energieeffizienz, quantitativ beschrieben werden. Nutzungsbedarfsänderungen und ökonomische Bestrebungen erfordern Kurz- und Langfristentscheidungen zur Anpassung einer IT-Infrastruktur und insbesondere ihre Attribute an dieses dynamische Umfeld. Potentielle Attribut-Zielkonflikte sowie die zentrale Rolle von IT-Infrastrukturen erfordern eine Entscheidungsfindung mittels Reasoning, einem Prozess, der Rückschlüsse (rein) aus Fakten und Prämissen zieht. Die Fokussierung auf spezifische Teile einer IT-Infrastruktur sowie die Beschränkung auf (sehr) wenige Attribute disqualifizieren bestehende Reasoning-Ansätze für dieses Vorhaben, da sie weder das komplexe Zusammenspiel von IT-Infrastruktur-Komponenten, noch Abhängigkeiten zwischen und innerhalb einzelner Attribute ausreichend berücksichtigen können. Diese Arbeit präsentiert ein Prozessmodell für das integrierte Reasoning über quantitative IT-Infrastruktur-Attribute. Die grundlegende Idee des Prozessmodells ist die Herleitung einer individuellen Reasoning-Funktion, einer mathematischen Abbildung von Einfluss- und Modifikationsparametern auf einen Attributvektor. Die Herleitung basiert auf der Integration bestehender (Attribut-)Modelle, um von deren Spezialisierung, Reife und Verbreitung profitieren zu können. Die erzielte Reasoning-Funktion verarbeitet ein individuelles Tupel aus IT-Infrastruktur-Komponenten, Attributen und externen Einflussfaktoren, um eine breite Anwendbarkeit zu gewährleisten. Das Prozessmodell formalisiert ein Reasoning-Vorhaben in drei Phasen. Zunächst werden die Reasoning-Ziele und -Parameter in einer Reasoning-Suite gesammelt und in einem Reasoning-Funktions-Gerüst formalisiert. Anschließend wird das Gerüst entsprechend den Vorgaben der Reasoning-Suite iterativ verfeinert. Abschließend wird die hergeleitete Reasoning-Funktion verwendet, um mittels “What-if”–Analysen, Optimierungsverfahren oder deskriptiver Statistik das Reasoning durchzuführen. Das Prozessmodell enthält fünf Template-Klassen, die den Prozess formalisieren, um Reproduzierbarkeit zu gewährleisten und Fehleranfälligkeit zu reduzieren. Das Prozessmodell wird auf drei Arten validiert. Ein kontrolliertes Experiment zeigt die Durchführbarkeit des Prozessmodells anhand des Reasonings zur Leistung und Energieeffizienz eines Raspberry Pi Clusters. Eine Anforderungsanalyse an einem Superrechner und an der europaweiten Ausführung von Hydro-Meteorologie-Modellen erläutert gemeinsam mit der Betrachtung verwandter Arbeiten den Innovationsgrad des Prozessmodells. Potentielle Erweiterungen nutzen die vorbereiteten Automatisierungsansätze, integrieren menschliche Faktoren, und generieren Modifikationsempfehlungen basierend auf Reasoning-Ergebnissen

Cloud-computing strategies for sustainable ICT utilization : a decision-making framework for non-expert Smart Building managers

Virtualization of processing power, storage, and networking applications via cloud-computing allows Smart Buildings to operate heavy demand computing resources off-premises. While this approach reduces in-house costs and energy use, recent case-studies have highlighted complexities in decision-making processes associated with implementing the concept of cloud-computing. This complexity is due to the rapid evolution of these technologies without standardization of approach by those organizations offering cloud-computing provision as a commercial concern. This study defines the term Smart Building as an ICT environment where a degree of system integration is accomplished. Non-expert managers are highlighted as key users of the outcomes from this project given the diverse nature of Smart Buildings’ operational objectives. This research evaluates different ICT management methods to effectively support decisions made by non-expert clients to deploy different models of cloud-computing services in their Smart Buildings ICT environments. The objective of this study is to reduce the need for costly 3rd party ICT consultancy providers, so non-experts can focus more on their Smart Buildings’ core competencies rather than the complex, expensive, and energy consuming processes of ICT management. The gap identified by this research represents vulnerability for non-expert managers to make effective decisions regarding cloud-computing cost estimation, deployment assessment, associated power consumption, and management flexibility in their Smart Buildings ICT environments. The project analyses cloud-computing decision-making concepts with reference to different Smart Building ICT attributes. In particular, it focuses on a structured programme of data collection which is achieved through semi-structured interviews, cost simulations and risk-analysis surveys. The main output is a theoretical management framework for non-expert decision-makers across variously-operated Smart Buildings. Furthermore, a decision-support tool is designed to enable non-expert managers to identify the extent of virtualization potential by evaluating different implementation options. This is presented to correlate with contract limitations, security challenges, system integration levels, sustainability, and long-term costs. These requirements are explored in contrast to cloud demand changes observed across specified periods. Dependencies were identified to greatly vary depending on numerous organizational aspects such as performance, size, and workload. The study argues that constructing long-term, sustainable, and cost-efficient strategies for any cloud deployment, depends on the thorough identification of required services off and on-premises. It points out that most of today’s heavy-burdened Smart Buildings are outsourcing these services to costly independent suppliers, which causes unnecessary management complexities, additional cost, and system incompatibility. The main conclusions argue that cloud-computing cost can differ depending on the Smart Building attributes and ICT requirements, and although in most cases cloud services are more convenient and cost effective at the early stages of the deployment and migration process, it can become costly in the future if not planned carefully using cost estimation service patterns. The results of the study can be exploited to enhance core competencies within Smart Buildings in order to maximize growth and attract new business opportunities

Blueprint model and language for engineering cloud applications

Abstract: The research presented in this thesis is positioned within the domain of engineering CSBAs. Its contribution is twofold: (1) a uniform specification language, called the Blueprint Specification Language (BSL), for specifying cloud services across several cloud vendors and (2) a set of associated techniques, called the Blueprint Manipulation Techniques (BMTs), for publishing, querying, and composing cloud service specifications with aim to support the flexible design and configuration of an CSBA.

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions