Self-adaptation via concurrent multi-action evaluation for unknown context

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project. Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach. The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains. The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason. The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered. In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques

Extra Functional Properties Evaluation of Self-managed Software Systems with Formal Methods

Multitud de aplicaciones software actuales están abocadas a operar en contextos dinámicos. Estos pueden manifestarse en términos de cambios en el entorno de ejecución de la aplicación, cambios en los requisitos de la aplicación, cambios en la carga de trabajo recibida por la aplicación, o cambios en cualquiera de los elementos que la aplicación software pueda percibir y verse afectada. Además, estos contextos dinámicos no están restringidos a un dominio particular de aplicaciones sino que se pueden encontrar en múltiples dominios, tales como: sistemas empotrados, arquitecturas orientadas a servicios, clusters para computación de altas prestaciones, dispositivos móviles o software para el funcionamiento de la red. La existencia de estas características disuade a los ingenieros de desarrollar software que no sea capaz de cambiar de modo alguno su ejecución para acomodarla al contexto en el que se está ejecutando el software en cada momento. Por lo tanto, con el objetivo de que el software pueda satisfacer sus requisitos en todo momento, este debe incluir mecanismos para poder cambiar su configuración de ejecución. Además, debido a que los cambios de contexto son frecuentes y afectan a múltiples dispositivos de la aplicación, la intervención humana que cambie manualmente la configuración del software no es una solución factible. Para enfrentarse a estos desafíos, la comunidad de Ingeniería del Software ha propuesto nuevos paradigmas que posibilitan el desarrollo de software que se enfrenta a contextos cambiantes de un modo automático; por ejemplo las propuestas Autonomic Computing y Self-* Software. En tales propuestas es el propio software quien gestiona sus mecanismos para cambiar la configuración de ejecución, sin requerir por lo tanto intervención humana alguna. Un aspecto esencial del software auto-adaptativo (Self-adaptive Software es uno de los términos más generales para referirse a Self-* Software) es el de planear sus cambios o adaptaciones. Los planes de adaptación determinan tanto el modo en el que se adaptará el software como los momentos oportunos para ejecutar tales adaptaciones. Hay un gran conjunto de situaciones para las cuales la propiedad de auto- adaptación es una solución. Una de esas situaciones es la de mantener al sistema satisfaciendo sus requisitos extra funcionales, tales como la calidad de servicio (Quality of Service, QoS) y su consumo de energía. Esta tesis ha investigado esa situación mediante el uso de métodos formales. Una de las contribuciones de esta tesis es la propuesta para asentar en una arquitectura software los sistemas que son auto-adaptativos respecto a su QoS y su consumo de energía. Con este objetivo, esta parte de la investigación la guía una arquitectura de tres capas de referencia para sistemas auto-adaptativos. La bondad del uso de una arquitectura de referencia es que muestra fácilmente los nuevos desafíos en el diseño de este tipo de sistemas. Naturalmente, la planificación de la adaptación es una de las actividades consideradas en la arquitectura. Otra de las contribuciones de la tesis es la propuesta de métodos para la creación de planes de adaptación. Los métodos formales juegan un rol esencial en esta actividad, ya que posibilitan el estudio de las propiedades extra funcionales de los sistemas en diferentes configuraciones. El método formal utilizado para estos análisis es el de las redes de Petri markovianas. Una vez que se ha creado el plan de adaptación, hemos investigado la utilización de los métodos formales para la evaluación de QoS y consumo de energía de los sistemas auto-adaptativos. Por lo tanto, se ha contribuido a la comunidad de análisis de QoS con el análisis de un nuevo y particularmente complejo tipo de sistemas software. Para llevar a cabo este análisis se requiere el modelado de los cambios din·micos del contexto de ejecución, para lo que se han utilizado una variedad de métodos formales, como los Markov modulated Poisson processes para estimar los parámetros de las variaciones en la carga de trabajo recibida por la aplicación, o los hidden Markov models para predecir el estado del entorno de ejecución. Estos modelos han sido usados junto a las redes de Petri para evaluar sistemas auto-adaptativos y obtener resultados sobre su QoS y consumo de energía. El trabajo de investigación anterior sacó a la luz el hecho de que la adaptabilidad de un sistema no es una propiedad tan fácilmente cuantificable como las propiedades de QoS -por ejemplo, el tiempo de respuesta- o el consumo de energÌa. En consecuencia, se ha investigado en esa dirección y, como resultado, otra de las contribuciones de esta tesis es la propuesta de un conjunto de métricas para la cuantificación de la propiedad de adaptabilidad de sistemas basados en servicios. Para conseguir las anteriores contribuciones se realiza un uso intensivo de modelos y transformaciones de modelos; tarea para la que se han seguido las mejores prácticas en el campo de investigación de la Ingeniería orientada a modelos (Model-driven Engineering, MDE). El trabajo de investigación de esta tesis en el campo MDE ha contribuido con: el aumento de la potencia de modelado de un lenguaje de modelado de software propuesto anteriormente y métodos de transformación desde dos lenguajes de modelado de software a redes de Petri estocasticas

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

All versus one: An empirical comparison on retrained and incremental machine learning for modeling performance of adaptable software

Given the ever-increasing complexity of adaptable software systems and their commonly hidden internal information (e.g., software runs in the public cloud), machine learning based performance modeling has gained momentum for evaluating, understanding and predicting software performance, which facilitates better informed self-adaptations. As performance data accumulates during the run of the software, updating the performance models becomes necessary. To this end, there are two conventional modeling methods: the retrained modeling that always discard the old model and retrain a new one using all available data; or the incremental modeling that retains the existing model and tunes it using one newly arrival data sample. Generally, literature on machine learning based performance modeling for adaptable software chooses either of those methods according to a general belief, but they provide insufficient evidences or references to justify their choice. This paper is the first to report on a comprehensive empirical study that examines both modeling methods under distinct domains of adaptable software, 5 performance indicators, 8 learning algorithms and settings, covering a total of 1,360 different conditions. Our findings challenge the general belief, which is shown to be only partially correct, and reveal some of the important, statistically significant factors that are often overlooked in existing work, providing evidence-based insights on the choice.<br