7 research outputs found
Heuristiken zur multikriteriellen Komposition von Diensten in dienstbasierten Informationssystemen
Service-orientierte Architekturen unterstützen die Bereitstellung von Anwendungsfunktionalität durch Dienstkomposition. Dabei werden nicht-funktionale Attribute betrachtet, um zwischen funk-tional gleichwertigen Diensten zu unterscheiden. Wir untersuchen die Auswahl von Diensten aus einer Menge von Dienstkandidaten für den Fall einer sequentiellen Komposition, so dass die Kosten des komponierten Dienstes eine vorgegebene Schranke nicht überschreiten und gleichzeitig die Ausführungszeit minimiert und die Verfügbarkeit des komponierten Dienstes maximiert wird. Da dieses Problem NP-schwer ist, wird ein genetischer Algorithmus zur Ermittlung der Menge von Pareto-optimalen Lösungen vorgeschlagen, der mit problemspezifischen Heuristiken kombiniert wird. Die Ergebnisse numerischer Experimente mit zufällig erzeugten Probleminstanzen zeigen die Leistungsfähigkeit des Ansatzes
Genetic Programming for QoS-Aware Data-Intensive Web Service Composition and Execution
Web service composition has become a promising technique to build powerful enterprise applications by making use of distributed services with different functions. In the age of big data, more and more web services are created to deal with a large amount of data, which are called data-intensive services. Due to the explosion in the volume of data, providing efficient approaches to composing data-intensive services will become more and more important in the field of service-oriented computing. Meanwhile, as numerous web services have been emerging to offer identical or similar functionality on the Internet, web service composition is usually performed with end-to-end Quality of Service (QoS) properties which are adopted to describe the non-functional properties (e.g., response time, execution cost, reliability, etc.) of a web service. In addition, the executions of composite web services are typically coordinated by a centralized workflow engine. As a result, the centralized execution paradigm suffers from inefficient communication and a single point of failure. This is particularly problematic in the context of data-intensive processes. To that end, more decentralized and flexible execution paradigms are required for the execution of data-intensive applications.
From a computational point of view, the problems of QoS-aware data-intensive web service composition and execution can be characterised as complex, large-scale, constrained and multi-objective optimization problems. Therefore, genetic programming (GP) based solutions are presented in this thesis to address the problems. A series of simulation experiments are provided to demonstrate the performance of the proposed approaches, and the empirical observations are also described in this thesis.
Firstly, we propose a hybrid approach that integrates the local search procedure of tabu search into the global search process of GP to solving the problem of QoS-aware data-intensive web service composition. A mathematical model is developed for considering the mass data transmission across different component services in a data-intensive service composition. The experimental results show that our proposed approach can provide better performance than the standard GP approach and two traditional optimization methods.
Next, a many-objective evolutionary approach is proposed for tackling the QoS-aware data-intensive service composition problem having more than three competing quality objectives. In this approach, the original search space of the problem is reduced before a recently developed many-objective optimization algorithm, NSGA-III, is adopted to solve the many-objective optimization problem. The experimental results demonstrate the effectiveness of our approach, as well as its superiority than existing single-objective and multi-objective approaches.
Finally, a GP-based approach to partitioning a composite data-intensive service for decentralized execution is put forth in this thesis. Similar to the first problem, a mathematical model is developed for estimating the communication overhead inside a partition and across the partitions. The data and control dependencies in the original composite web service can be properly preserved in the deployment topology generated by our approach. Compared with two existing heuristic algorithms, the proposed approach exhibits better scalability and it is more suitable for large-scale partitioning problems
Quality-of-Service-Aware Service Selection in Mobile Environments
The last decade is characterized by the rise of mobile technologies (UMTS, LTE, WLAN, Bluetooth, SMS, etc.) and devices (notebooks, tablets, mobile phones, smart watches, etc.). In this rise, mobiles phones have played a crucial role because they paved the way for mobile pervasion among the public. In addition, this development has also led to a rapid growth of the mobile service/application market (Statista 2017b). As a consequence, users of mobile devices nowadays find themselves in a mobile environment, with (almost) unlimited access to information and services from anywhere through the Internet, and can connect to other people at any time (cf. Deng et al. 2016; Newman 2015). Additionally, modern mobile devices offer the opportunity to select the services or information that best fit to a user’s current context.
In this regard, mobile information services support users in retrieving context and non-context information, such as about the current traffic situation, public transport options, and flight connections, as well as about real-world entities, such as sights, museums, and restaurants (cf. Deng et al. 2016; Heinrich and Lewerenz 2015; Ventola 2014). An example of the application of mobile information services is several users planning a joint city day trip. Here, the users could utilize information retrieved about real-world entities for their planning. Such a trip constitutes a process with multiple participating users and may encompass actions such as visiting a museum and having lunch. For each action, mobile information services (e.g., Yelp, TripAdvisor, Google Places) can help locate available alternatives that differ only in attributes such as price, average length of stay (i.e., duration), or recommendations published by previous visitors. In addition, context information (e.g., business hours, distance) can be used to more effectively support the users in their decisions. Moreover, because multiple users are participating in the same trip, some users want to or must conduct certain actions together.
However, decision-makers (e.g., mobile users) attempting to determine the optimal solution for such processes – meaning the best alternative for each action and each participating user – are confronted with several challenges, as shown by means of the city trip example: First, each user most likely has his or her own preferences and requirements regarding attributes such as price and duration, which all must be considered. Furthermore, for each action of the day trip, a huge number of alternatives probably exist. Thus, users might face difficulties selecting the optimal alternatives because of an information overload problem (Zhang et al. 2009). Second, taking multiple users into account may require the coordination of their actions because of potential dependencies among different users’ tours, which, for example, is the case when users prefer to conduct certain actions together. This turns the almost sophisticated decision problem at hand into a problem of high complexity. The problem complexity is increased further when considering context information, because this causes dependencies among different actions of a user that must be taken into account. For instance, the distance to cover by a user to reach a certain restaurant depends on the location of the previously visited museum. In conclusion, it might be impossible for a user to determine an optimal city trip tour for all users, making decision support by an information system necessary. Because the available alternatives for each action of the process can be denoted as (information) service objects (cf. Dannewitz et al. 2008; Heinrich and Lewerenz 2015; Hinkelmann et al. 2013), the decision problem at hand is a Quality-of-Service (QoS)-aware service selection problem.
This thesis proposes novel concepts and optimization approaches for QoS-aware service selection regarding processes with multiple users and context information, focusing on scenarios in mobile environments. In this respect, the developed multi user context-aware service selection approaches are able to deal with dependencies among different users’ service compositions, which result from the consideration of multiple users, as well as dependencies within a user’s service composition, which result from the consideration of context information. Consequently, these approaches provide suitable support for decision-makers, such as mobile users
Metaheuristic models for decision support in the software construction process
En la actualidad, los ingenieros software no solo tienen la responsabilidad de construir
sistemas que desempe~nen una determinada funcionalidad, sino que cada vez
es más importante que dichos sistemas también cumplan con requisitos no funcionales
como alta disponibilidad, efciencia o seguridad, entre otros. Para lograrlo,
los ingenieros se enfrentan a un proceso continuo de decisión, pues deben estudiar
las necesidades del sistema a desarrollar y las alternativas tecnológicas existentes
para implementarlo. Todo este proceso debe estar encaminado a la obtención de
sistemas software de gran calidad, reutilizables y que faciliten su mantenimiento y
modificación en un escenario tan exigente y competitivo.
La ingenierÃa del software, como método sistemático para la construcción de software,
ha aportado una serie de pautas y tareas que, realizadas de forma disciplinada
y adaptadas al contexto de desarrollo, posibilitan la obtención de software de calidad.
En concreto, el proceso de análisis y diseño del software ha adquirido una gran
importancia, pues en ella se concibe la estructura del sistema, en términos de sus bloques
funcionales y las interacciones entre ellos. Es en este momento cuando se toman
las decisiones acerca de la arquitectura, incluyendo los componentes que la conforman,
que mejor se adapta a los requisitos, tanto funcionales como no funcionales,
que presenta el sistema y que claramente repercuten en su posterior desarrollo. Por
tanto, es necesario que el ingeniero analice rigurosamente las alternativas existentes,
sus implicaciones en los criterios de calidad impuestos y la necesidad de establecer
compromisos entre ellos. En este contexto, los ingenieros se guÃan principalmente
por sus habilidades y experiencia, por lo que dotarles de métodos de apoyo a la
decisión representarÃa un avance significativo en el área.
La aplicación de técnicas de inteligencia artificial en este ámbito ha despertado un
gran interés en los últimos años. En particular, la inteligencia artificial ha encontrado
en la ingenierÃa del software un ámbito de aplicación complejo, donde diferentes
técnicas pueden ayudar a conseguir la semi-automatización de tareas tradicionalmente
realizadas de forma manual. De la unión de ambas áreas surge la denominada
ingenierÃa del software basada en búsqueda, que propone la reformulación de las
actividades propias de la ingenierÃa del software como problemas de optimización.
A continuación, estos problemas podrÃan ser resueltos mediante técnicas de búsqueda
como las metaheurÃsticas. Este tipo de técnicas se caracterizan por explorar el
espacio de posibles soluciones de una manera \inteligente", a menudo simulando
procesos naturales como es el caso de los algoritmos evolutivos.
A pesar de ser un campo de investigación muy reciente, es posible encontrar propuestas
para automatizar una gran variedad de tareas dentro del ciclo de vida del software, como son la priorización de requisitos, la planifcación de recursos, la refactorización del código fuente o la generación de casos de prueba. En el ámbito del
análisis y diseño de software, cuyas tareas requieren de creatividad y experiencia,
conseguir una automatización completa resulta poco realista. Es por ello por lo que
la resolución de sus tareas mediante enfoques de búsqueda debe ser tratada desde la
perspectiva del ingeniero, promoviendo incluso la interacción con ellos. Además, el
alto grado de abstracción de algunas de sus tareas y la dificultad de evaluar cuantitativamente
la calidad de un diseño software, suponen grandes retos en la aplicación
de técnicas de búsqueda durante las fases tempranas del proceso de construcción de
software.
Esta tesis doctoral busca realizar aportaciones significativas al campo de la ingenierÃa
del software basada en búsqueda y, más concretamente, al área de la optimización
de arquitecturas software. Aunque se están realizando importantes avances en este
área, la mayorÃa de propuestas se centran en la obtención de arquitecturas de bajo
nivel o en la selección y despliegue de artefactos software ya desarrollados. Por tanto,
no existen propuestas que aborden el modelado arquitectónico a un nivel de abstracción elevado, donde aún no existe un conocimiento profundo sobre cómo será el
sistema y, por tanto, es más difÃcil asistir al ingeniero. Como problema de estudio,
se ha abordado principalmente la tarea del descubrimiento de arquitecturas software
basadas en componentes. El objetivo de este problema consiste en abstraer los bloques
arquitectónicos que mejor definen la estructura actual del software, asà como
sus interacciones, con el fin de facilitar al ingeniero su posterior análisis y mejora.
Durante el desarrollo de esta tesis doctoral se ha explorado el uso de una gran variedad
de técnicas de búsqueda, estudiando su idoneidad y realizando las adaptaciones
necesarias para hacer frente a los retos mencionados anteriormente. La primera propuesta
se ha centrado en la formulación del descubrimiento de arquitecturas como
problema de optimización, abordando la representación computacional de los artefactos
software que deben ser modelados y definiendo medidas software para evaluar
su calidad durante el proceso de búsqueda. Además, se ha desarrollado un primer
modelo basado en algoritmos evolutivos mono-objetivo para su resolución, el cual ha
sido validado experimentalmente con sistemas software reales. Dicho modelo se caracteriza
por ser comprensible y
exible, pues sus componentes han sido diseñados
considerando estándares y herramientas del ámbito de la ingenierÃa del software,
siendo además configurable en función de las necesidades del ingeniero.
A continuación, el descubrimiento de arquitecturas ha sido tratado desde una perspectiva
multiobjetivo, donde varias medidas software, a menudo en con
icto, deben
ser simultáneamente optimizadas. En este caso, la resolución del problema se ha
llevado a cabo mediante ocho algoritmos del estado del arte, incluyendo propuestas recientes del ámbito de la optimización de muchos objetivos. Tras ser adaptados al
problema, estos algoritmos han sido comparados mediante un extenso estudio experimental
con el objetivo de analizar la ifnuencia que tiene el número y la elección
de las métricas a la hora de guiar el proceso de búsqueda. Además de realizar una
validación del rendimiento de estos algoritmos siguiendo las prácticas habituales
del área, este estudio aporta un análisis detallado de las implicaciones que supone
la optimización de múltiples objetivos en la obtención de modelos de soporte a la
decisión.
La última propuesta en el contexto del descubrimiento de arquitecturas software
se centra en la incorporación de la opinión del ingeniero al proceso de búsqueda.
Para ello se ha diseñado un mecanismo de interacción que permite al ingeniero indicar
tanto las caracterÃsticas deseables en las soluciones arquitectónicas (preferencias
positivas) como aquellos aspectos que deben evitarse (preferencias negativas). Esta
información es combinada con las medidas software utilizadas hasta el momento,
permitiendo al algoritmo evolutivo adaptar la búsqueda conforme el ingeniero interactúe. Dadas las caracterÃsticas del modelo, su validación se ha realizado con la
participación de ingenieros con distinta experiencia en desarrollo software, a fin de
demostrar la idoneidad y utilidad de la propuesta.
En el transcurso de la tesis doctoral, los conocimientos adquiridos y las técnicas
desarrolladas también han sido extrapolados a otros ámbitos de la ingenierÃa del
software basada en búsqueda mediante colaboraciones con investigadores del área.
Cabe destacar especialmente la formalización de una nueva disciplina transversal,
denominada ingenierÃa del software basada en búsqueda interactiva, cuyo fin es promover
la participación activa del ingeniero durante el proceso de búsqueda. Además,
se ha explorado la aplicación de algoritmos de muchos objetivos a un problema clásico
de la computación orientada a servicios, como es la composición de servicios web.Nowadays, software engineers have not only the responsibility of building systems that provide a particular functionality, but they also have to guarantee that these systems ful l demanding non-functional requirements like high availability, e ciency or security. To achieve this, software engineers face a continuous decision process, as they have to evaluate system needs and existing technological alternatives to implement it. All this process should be oriented towards obtaining high-quality and reusable systems, also making future modi cations and maintenance easier in such a competitive scenario. Software engineering, as a systematic method to build software, has provided a number of guidelines and tasks that, when done in a disciplinarily manner and properly adapted to the development context, allow the creation of high-quality software. More speci cally, software analysis and design has acquired great relevance, being the phase in which the software structure is conceived in terms of its functional blocks and their interactions. In this phase, engineers have to make decisions about the most suitable architecture, including its constituent components. Such decisions are made according to the system requirements, either functional or non-functional, and will have a great impact on its future development. Therefore, the engineer has to rigorously analyse existing alternatives, their implications on the imposed quality criteria and the need of establishing trade-o s among them. In this context, engineers are mostly guided by their own capabilities and experience, so providing them with decision support methods would represent a signi cant contribution. The application of arti cial intelligent techniques in this area has experienced a growing interest in the last years. Particularly, software engineering represents a complex application domain to arti cial intelligence, whose diverse techniques can help in the semi-automation of tasks traditionally performed manually. The union of both elds has led to the appearance of search-based software engineering, which proposes reformulating software engineering activities as optimisation problems. For their resolution, search techniques like metaheuristics can be then applied. This type of technique performs an \intelligent" exploration of the space of candidate solutions, often inspired by natural processes as happens with evolutionary algorithms. Despite the novelty of this research eld, there are proposals to automate a great variety of tasks within the software lifecycle, such as requirement prioritisation, resource planning, code refactoring or test case generation. Focusing on analysis and design, whose tasks require creativity and experience, trying to achieve full automation is not realistic. Therefore, solving design tasks by means of search approaches should be oriented towards the engineer's perspective, even promoting their interaction. Furthermore, design tasks are also characterised by a high level of abstraction and the di culty of quantitatively evaluating design quality. All these aspects represent key challenges for the application of search techniques in early phases of the software construction process. The aim of this Ph.D. Thesis is to make signi cant contributions in search-based software engineering and, specially, in the area of software architecture optimisation. Although it is an area in which signi cant progress is being done, most of the current proposals are focused on generating low-level architectures or selecting and deploying already developed artefacts. Therefore, there is a lack of proposals dealing with architectural modelling at a high level of abstraction. At this level, engineers do not have a deep understanding of the system yet, meaning that assisting them is even more di cult. As case study, the discovery of component-based software architectures has been primary addressed. The objective for this problem consists in the abstraction of the architectural blocks, and their interactions, that best de ne the current structure of a software system. This can be viewed as the rst step an engineer would perform in order to further analyse and improve the system architecture. In this Ph.D. Thesis, the use of a great variety of search techniques has been explored. The suitability of these techniques has been studied, also making the necessary adaptations to cope with the aforementioned challenges. A rst proposal has been focused on the formulation of software architecture discovery as an optimisation problem, which consists in the computational representation of its software artefacts and the de nition of software metrics to evaluate their quality during the search process. Moreover, a single-objective evolutionary algorithm has been designed for its resolution, which has been validated using real software systems. The resulting model is comprehensible and exible, since its components have been designed under software engineering standards and tools and are also con gurable according to engineer's needs. Next, the discovery of software architectures has been tackled from a multi-objective perspective, in which several software metrics, often in con ict, have to be simultaneously optimised. In this case, the problem is solved by applying eight state-of-theart algorithms, including some recent many-objective approaches. These algorithms have been adapted to the problem and compared in an extensive experimental study, whose purpose is to analyse the in uence of the number and combination of metrics when guiding the search process. Apart from the performance validation following usual practices within the eld, this study provides a detailed analysis of the practical
implications behind the optimisation of multiple objectives in the context of
decision support.
The last proposal is focused on interactively including the engineer's opinion in the
search-based architecture discovery process. To do this, an interaction mechanism
has been designed, which allows the engineer to express desired characteristics for
the solutions (positive preferences), as well as those aspects that should be avoided
(negative preferences). The gathered information is combined with the software
metrics used until the moment, thus making possible to adapt the search as the
engineer interacts. Due to the characteristics of the proposed model, engineers of
di erent expertise in software development have participated in its validation with
the aim of showing the suitability and utility of the approach.
The knowledge acquired along the development of the Thesis, as well as the proposed
approaches, have also been transferred to other search-based software engineering
areas as a result of research collaborations. In this sense, it is worth noting the
formalisation of interactive search-based software engineering as a cross-cutting discipline,
which aims at promoting the active participation of the engineer during the
search process. Furthermore, the use of many-objective algorithms has been explored
in the context of service-oriented computing to address the so-called web service
composition problem