Monitoring middleware for distributed applications

With growing maturity Internet services are proving integral to the provision of computer services. To provide consistent end-user experiences these services are increasingly augmented with some notion of 'Quality-of-Service' (QoS), which typically requires the management of computing resources to maintain a predictable level of service performance. It is difficult to guarantee consistent servIce provision In dynamic and open environments such as the Internet. However service monitoring can be used to inform compensatory actions by collecting meaningful service performance data from strategic points in an active service environment. Due to the unpredictable nature of the Internet distributed monitoring mechanisms face challenges with respect to the various communication protocols, application languages, and monitoring requirements associated with a service environment. With the growing popularity of Internet services creation of monitoring solutions on a per- service basis becomes time-consuming and misses opportunities to re-use existing logic. Ideally monitoring solutions would be domain-agnostic, automatically generated and automatically deployed. This thesis progresses these ambitions by providing a generic, distributed monitoring and evaluation framework based on Metric Collector (MeCo) components. These components can transparently gather measurement data across a range of service technologies as used within E-Commerce service environments. MeCo components form part of a framework which can interpret Service Level Agreements (SLAs) to automatically provide tailored service monitoring. The evaluation paradigms of the Meeo Framework are re-appropriated for use in Distributed Virtual Environments (DYEs). Quantifiable QoS requirements are established for Interest Management mechanisms (which limit message production based on object localities within a DYE). These are then incorporated into a DVE Simulator application. This application allows DYE application developers to evaluate Interest Management configurations for their suitability. Extensions to the DVE Simulator are exhibited in the Evolutionary Optimisation Simulator (EOS), which provides automated optimisation capabilities for DVE configurations through utilisation of genetic algorithm techniques.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications