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The fast development of the Internet of Things (IoT) changes our life in many areas, especially in the health domain. For example, remote disease diagnosis can be achieved more efficiently with advanced IoT-technologies which not only include hardware but also smart IoT data processing and learning algorithms, e.g. image-based disease classification. In this paper, we work in a specific area of skin condition classification. This research work aims to provide an implementable solution for IoT-led remote skin disease diagnosis applications. The research output can be concluded into three folders. The first folder is about dynamic AI model configuration supported IoT-Fog-Cloud remote diagnosis architecture with hardware examples. The second folder is the evaluation survey regarding the performances of machine learning models for skin disease detection. The evaluation contains a variety of data processing methods and their aggregations. The evaluation takes account of both training-testing and cross-testing validations on all seven conditions and individual condition. In addition, the HAM10000 dataset is picked for the evaluation process according to the suitability comparisons to other relevant datasets. In the evaluation, we discuss the earlier work of ANN, SVM and KNN models, but the evaluation process mainly focuses on six widely applied Deep Learning models of VGG16, Inception, Xception, MobileNet, ResNet50 and DenseNet161. The result shows that each of the top four models for the major seven skin conditions has better performance for the specific condition than others. Based on the evaluation discovery, the last folder proposes a novel classification approach of the Targeted Ensemble Machine Classify Model (TEMCM) to enable dynamically combining a suitable model in a two-phase detection process. The final evaluation result shows the proposed model can archive better performance.

The cloud ecosystem provides transformative advantages that allow elastically offering on-demand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients' needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.

The Internet of Things consists of “things” made up of small sensors and actuators capable of interacting with the environment. The combination of devices with sensor networks and Internet access enables the communication between the physical world and cyberspace, enabling the development of solutions to many real-world problems. However, most existing applications are dedicated to solving a specific problem using only private sensor networks, which limits the actual capacity of the Internet of Things. In addition, these applications are concerned with the quality of service offered by the sensor network or the correct analysis method that can lead to inaccurate or irrelevant conclusions, which can cause significant harm for decision makers. In this context, we propose two systematic methods to analyze spatially distributed data Internet of Things. We show with the results that geostatistics and spatial statistics are more appropriate than classical statistics to do this analysis.

Science Gateways have been widely accepted as an important tool in academic research, due to their flexibility, simple use and extension. However, such systems may yield performance traps that delay work progress and cause waste of resources or generation of poor scientific results. This paper addresses an investigation on some of the failures in a Galaxy system and analyses of their impacts. The use case is based on protein structure prediction experiments performed. A novel science gateway component is proposed towards the definition of the relation between general parameters and capacity of machines. The machine-learning strategies used appoint the best machine setup in a heterogeneous environment and the results show a complete overview of Galaxy, a diverse platform organization, and the workload behavior. A Support Vector Regression (SVR) model generated and based on a historic data-set provided an excellent learning module and proved a varied platform configuration is valuable as infrastructure in a science gateway. The results revealed the advantages of investing in local cluster infrastructures as a base for scientific experiments.

One of the strength of Virtual Organisations is their ability to dynamically and rapidly adapt in response to changing environmental conditions. Dynamic adaptability has been studied in other system areas as well and system management through policies has crystallized itself as a very prominent solution in system and network administration. However, these areas are often concerned with very low-level technical aspects. Previous work on the APPEL policy language has been aimed at dynamically adapting system behaviour to satisfy end-user demands and – as part of STPOWLA – APPEL was used to adapt workflow instances at runtime. In this paper we explore how the ideas of APPEL and STPOWLA can be extended from workflows to the wider scope of Virtual Organisations. We will use a Travel Booking VO as example.

This paper provides an outline of a formal approach that we are developing for modelling Virtual Organisations (VOs) and their Breeding Environments (VBEs). We propose different levels of representation for the functional structures and processes that VBEs and VOs involve, which are independent of the specificities of the infrastructures (organisational and technical) that support the functioning of VBEs. This allows us to reason about properties of tasks performed within VBEs and services provided through VOs without committing to the way in which they are implemented.

Service-Oriented Computing delivers the promise of configuring and reconfiguring software systems to address user's needs in a dynamic way. Context-aware computing promises to capture the user's needs and hence the requirements they have on systems. The marriage of both can deliver ad-hoc software solutions relevant to the user in the most current fashion. However, here it is a key to gather information on the users' activity (that is what they are doing). Traditionally any context sensing was conducted with hardware sensors. However, software can also play the same role and in some situations will be more useful to sense the activity of the user. Furthermore they can make use of the fact that Service-oriented systems exchange information through standard protocols. In this paper we discuss our proposed approach to sense the activity of the user making use of software.

This paper proposes a system named AWSCS (Automatic Web Service Composition System) to evaluate different approaches for automatic composition of Web services, based on QoS parameters that are measured at execution time. The AWSCS is a system to implement different approaches for automatic composition of Web services and also to execute the resulting flows from these approaches. Aiming at demonstrating the results of this paper, a scenario was developed, where empirical flows were built to demonstrate the operation of AWSCS, since algorithms for automatic composition are not readily available to test. The results allow us to study the behaviour of running composite Web services, when flows with the same functionality but different problem-solving strategies were compared. Furthermore, we observed that the influence of the load applied on the running system as the type of load submitted to the system is an important factor to define which approach for the Web service composition can achieve the best performance in production.

Cloud computing is a computational model in which resource providers can offer on-demand services to clients in a transparent way. However, to be able to guarantee quality of service without limiting the number of accepted requests, providers must be able to dynamically manage the available resources so that they can be optimized. This dynamic resource management is not a trivial task, since it involves meeting several challenges related to workload modeling, virtualization, performance modeling, deployment and monitoring of applications on virtualized resources. This paper carries out a performance evaluation of a module for resource management in a cloud environment that includes handling available resources during execution time and ensuring the quality of service defined in the service level agreement. An analysis was conducted of different resource configurations to define which dimension of resource scaling has a real influence on client requests. The results were used to model and implement a simulated cloud system, in which the allocated resource can be changed on-the-fly, with a corresponding change in price. In this way, the proposed module seeks to satisfy both the client by ensuring quality of service, and the provider by ensuring the best use of resources at a fair price.