Modeling Adaptation with Klaim

In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present an investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and use of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some well-known adaptation techniques: the IBM MAPE-K loop, the Accord component-based framework for architectural adaptation, and the aspect- and context-oriented programming paradigms. We illustrate our approach through a simple example concerning a data repository equipped with an automated cache mechanism

Autonomic Management of Cloud Neighbourhoods through Pulse Monitoring

Abstract-This paper reports on autonomic computing research, including the development of a self-* proof of concept, for a cloud based environment. It monitors administrative boundaries from within an autonomic manager, with each manager operating in a peer-to-peer mode and utilizing a pulse monitor. The prototype was developed in Java utilizing SNMP to demonstrate the manager's self-situation and environment-awareness of the current state of the whole neighborhood and proves the feasibility of communicating the health of the neighborhood to peer managers using an XML pulse concept. Each manager houses the functionality to enact changes to their neighborhood using SNMP based rules. This enables the capability to provide self-healing, self-configuring, self-optimizing and self-protection to network neighborhoods within cloud computing

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

Knowledge-centric autonomic systems

Autonomic computing revolutionised the commonplace understanding of proactiveness in the digital world by introducing self-managing systems. Built on top of IBM’s structural and functional recommendations for implementing intelligent control, autonomic systems are meant to pursue high level goals, while adequately responding to changes in the environment, with a minimum amount of human intervention. One of the lead challenges related to implementing this type of behaviour in practical situations stems from the way autonomic systems manage their inner representation of the world. Specifically, all the components involved in the control loop have shared access to the system’s knowledge, which, for a seamless cooperation, needs to be kept consistent at all times.A possible solution lies with another popular technology of the 21st century, the Semantic Web,and the knowledge representation media it fosters, ontologies. These formal yet flexible descriptions of the problem domain are equipped with reasoners, inference tools that, among other functions, check knowledge consistency. The immediate application of reasoners in an autonomic context is to ensure that all components share and operate on a logically correct and coherent “view” of the world. At the same time, ontology change management is a difficult task to complete with semantic technologies alone, especially if little to no human supervision is available. This invites the idea of delegating change management to an autonomic manager, as the intelligent control loop it implements is engineered specifically for that purpose.Despite the inherent compatibility between autonomic computing and semantic technologies,their integration is non-trivial and insufficiently investigated in the literature. This gap represents the main motivation for this thesis. Moreover, existing attempts at provisioning autonomic architectures with semantic engines represent bespoke solutions for specific problems (load balancing in autonomic networking, deconflicting high level policies, informing the process of correlating diverse enterprise data are just a few examples). The main drawback of these efforts is that they only provide limited scope for reuse and cross-domain analysis (design guidelines, useful architectural models that would scale well across different applications and modular components that could be integrated in other systems seem to be poorly represented). This work proposes KAS (Knowledge-centric Autonomic System), a hybrid architecture combining semantic tools such as: • an ontology to capture domain knowledge,• a reasoner to maintain domain knowledge consistent as well as infer new knowledge, • a semantic querying engine,• a tool for semantic annotation analysis with a customised autonomic control loop featuring: • a novel algorithm for extracting knowledge authored by the domain expert, • “software sensors” to monitor user requests and environment changes, • a new algorithm for analysing the monitored changes, matching them against known patterns and producing plans for taking the necessary actions, • “software effectors” to implement the planned changes and modify the ontology accordingly. The purpose of KAS is to act as a blueprint for the implementation of autonomic systems harvesting semantic power to improve self-management. To this end, two KAS instances were built and deployed in two different problem domains, namely self-adaptive document rendering and autonomic decision2support for career management. The former case study is intended as a desktop application, whereas the latter is a large scale, web-based system built to capture and manage knowledge sourced by an entire (relevant) community. The two problems are representative for their own application classes –namely desktop tools required to respond in real time and, respectively, online decision support platforms expected to process large volumes of data undergoing continuous transformation – therefore, they were selected to demonstrate the cross-domain applicability (that state of the art approaches tend to lack) of the proposed architecture. Moreover, analysing KAS behaviour in these two applications enabled the distillation of design guidelines and of lessons learnt from practical implementation experience while building on and adapting state of the art tools and methodologies from both fields.KAS is described and analysed from design through to implementation. The design is evaluated using ATAM (Architecture Trade off Analysis Method) whereas the performance of the two practical realisations is measured both globally as well as deconstructed in an attempt to isolate the impact of each autonomic and semantic component. This last type of evaluation employs state of the art metrics for each of the two domains. The experimental findings show that both instances of the proposed hybrid architecture successfully meet the prescribed high-level goals and that the semantic components have a positive influence on the system’s autonomic behaviour

Use of Self-Healing Techniques for Highly-Available Distributed Monitoring

The paper addresses the self-healing aspects of the monitoring systems. Nowadays, when the complex distributed systems are concerned, the monitoring system should become "intelligent" - as the first step it can guide the user what should be monitored. The next level of the "intelligence" can be described by the term "self-healing". The goal is to provide the capability that a decision made automatically by the monitoring system should force the system under monitoring to behave more stable, reliable and predictable. In the paper a new monitoring system is presented: AgeMon is an agent based, distributed monitoring system with strictly defined roles which can be performed by the agents. In the paper we discuss self-healing in the context of monitoring. When the self-healing of the monitoring system is concerned, a good example is the case where it is possible to lose the monitoring data due to the storage problems. AgeMon can handle such problems and automatically elects substitute persistence agents to store the data

Utilising stream reasoning techniques to underpin an autonomous framework for cloud application platforms

As cloud application platforms (CAPs) are reaching the stage where the human effort required to maintain them at an operational level is unsupportable, one of the major challenges faced by the cloud providers is to develop appropriate mechanisms for run-time monitoring and adaptation, to prevent cloud application platforms from quickly dissolving into a non-reliable environment. In this context, the application of intelligent approaches to Autonomic Clouds may offer promising opportunities. In this paper we present an approach to providing cloud platforms with autonomic capabilities, utilising techniques from the Semantic Web and Stream Reasoning research fields. The main idea of this approach is to encode values, monitored within cloud application platforms, using Semantic Web languages, which then allows us to integrate semantically-enriched observation streams with static ontological knowledge and apply intelligent reasoning. Using such run-time reasoning capabilities, we have developed a conceptual architecture for an autonomous framework and describe a prototype solution we have constructed which implements this architecture. Our prototype is able to perform analysis and failure diagnosis, and suggest further adaptation actions. We report our experience in utilising the Stream Reasoning technique in this context as well as further challenges that arise out of our work

EXCLAIM framework: a monitoring and analysis framework to support self-governance in Cloud Application Platforms

The Platform-as-a-Service segment of Cloud Computing has been steadily growing over the past several years, with more and more software developers opting for cloud platforms as convenient ecosystems for developing, deploying, testing and maintaining their software. Such cloud platforms also play an important role in delivering an easily-accessible Internet of Services. They provide rich support for software development, and, following the principles of Service-Oriented Computing, offer their subscribers a wide selection of pre-existing, reliable and reusable basic services, available through a common platform marketplace and ready to be seamlessly integrated into users' applications. Such cloud ecosystems are becoming increasingly dynamic and complex, and one of the major challenges faced by cloud providers is to develop appropriate scalable and extensible mechanisms for governance and control based on run-time monitoring and analysis of (extreme amounts of) raw heterogeneous data. In this thesis we address this important research question -- \textbf{how can we support self-governance in cloud platforms delivering the Internet of Services in the presence of large amounts of heterogeneous and rapidly changing data?} To address this research question and demonstrate our approach, we have created the Extensible Cloud Monitoring and Analysis (EXCLAIM) framework for service-based cloud platforms. The main idea underpinning our approach is to encode monitored heterogeneous data using Semantic Web languages, which then enables us to integrate these semantically enriched observation streams with static ontological knowledge and to apply intelligent reasoning. This has allowed us to create an extensible, modular, and declaratively defined architecture for performing run-time data monitoring and analysis with a view to detecting critical situations within cloud platforms. By addressing the main research question, our approach contributes to the domain of Cloud Computing, and in particular to the area of autonomic and self-managing capabilities of service-based cloud platforms. Our main contributions include the approach itself, which allows monitoring and analysing heterogeneous data in an extensible and scalable manner, the prototype of the EXCLAIM framework, and the Cloud Sensor Ontology. Our research also contributes to the state of the art in Software Engineering by demonstrating how existing techniques from several fields (i.e., Autonomic Computing, Service-Oriented Computing, Stream Processing, Semantic Sensor Web, and Big Data) can be combined in a novel way to create an extensible, scalable, modular, and declaratively defined monitoring and analysis solution

Sensae Console - Platforma de support para serviços baseados em IoT

Today there are more smart devices than people. The number of devices worldwide is forecast to almost triple from 8.74 billion in 2020 to more than 25.4 billion devices in 2030. The Internet of Things (IoT) is the connection of millions of smart devices and sensors connected to the Internet. These connected devices and sensors collect and share data for use and analysis by many organizations. Some examples of intelligent connected sensors are: GPS asset tracking, parking spots, refrigerator thermostats, soil condition and many others. The limit of different objects that can become intelligent sensors is limited only by our imagination. But these devices are mostly useless without a platform to analyze, store and present the aggregated data into business-oriented information. Recently, several platforms have emerged to address this need and help companies/governments to increase efficiency, cut on operational costs and improve safety. Sadly, most of these platforms are tailor made for the devices that the company offers. This dissertation presents the (Sensae Console) platform that enables and promotes the development of IoT-based business-oriented applications. This platform attempts to be device-neutral, IoT middleware-neutral and provide flexible upstream integration and hosting options while providing a simple and concise data streaming Application Programming Interface (API). Three IoT-based business-oriented applications built on top of the Sensae Console platform are presented as Proof of Concept (PoC) of its capabilities.Atualmente, existem mais sensores inteligentes do que pessoas. O número de sensores em todo o mundo deve quase triplicar de 8,74 bilhões em 2020 para mais de 25,4 bilhões em 2030. O conceito de IoT está relacionado com a interação entre milhões de dispositivos inteligentes através da Internet. Estes dispositivos e sensores conectados recolhem e disponibilizam dados para uso e análise por parte de muitas organizações. Alguns exemplos de sensores inteligentes e seus usos são: dispositivos GPS para rastreamento de ativos, monitorização de vagas de estacionamento, termostatos em arcas frigoríficas, condição do solo e muitos outros. O número de diferentes objetos que podem vir-se a tornar sensores inteligentes é limitado apenas pela nossa imaginação. Mas estes dispositivos são praticamente inúteis sem uma plataforma para analisar, armazenar e apresentar os dados agregados em informação relevante para o negócio em questão. Recentemente, várias plataformas surgiram para responder a essa necessidade e ajudar empresas/governos a aumentar a sua eficiência, reduzir custos operacionais e melhorar a segurança dos espaços e negócios. Infelizmente, a maioria dessas plataformas é feita à medida para os dispositivos que a empresa em questão oferece. Esta tese apresenta uma plataforma (Sensae Console) focada em que propiciar a criação de aplicações relacionados com IoT para negócios específicos. Esta plataforma procura ser agnóstica em relação aos dispositivos inteligentes e middleware de IoT usados por terceiros, oferece variadas e flexíveis opções de integração e hosting como também uma API de streaming simples e concisa. Três aplicações relacionadas com IoT, orientadas ao seu negócio e construídas com base na plataforma Sensae Console são apresentadas como provas de conceito das capacidades da plataforma