By blending real world information with data from digital environments using modern visualizations empowered with augmented reality features, more comprehensive and attractive software solutions can be created. Augmented Realty and Internet of Things are among the state-of-the-art technologies that contribute to the adoption of new, disrupting solutions in the fields of health and urban planning. IoT body sensors can record real-time measurements and offer instant access to users' vital signs while in parallel, environmental IoT sensors record real-time parameters for weather and pollution. In this work we are introducing a prototype application as part of a Nature-based Solution in an urban environment that offers advanced interactivity features with the users and motivates them to increase their physical activity. AR objects are placed at certain locations in the park and present user-specific or location-related information. Modern gamification techniques are also utilized, such as leaderboards and badges with content generated from the users' health data. Furthermore, location-related information is visualized including weather forecasts, environmental data and or briefing and events regarding the intervention in the urban area. The evaluation results of the system in real-world scenarios highlight positive user engagement remarks for promoting mental health and wellbeing in urban environments.

The era of big data is surrounded by plenty of challenges, concerning aspects related to data quality, data management, and data analysis. Plenty of these challenges are met in several domains, such as the healthcare domain, where the corresponding healthcare platforms not only have to deal with managing and/or analyzing a tremendous quantity of health data, but also have to accomplish these actions in the most efficient and secure way possible. Towards this direction, medical institutions are paying attention to the replacement of traditional approaches such as the Monolithic and Service Oriented Architecture (SOA), which deal with many difficulties for handling the increasing amount of healthcare data. This paper presents a platform for overcoming these issues, by adopting the Microservice Architecture (MSA), being able to efficiently manage and analyze these vast amounts of data. More specifically, the proposed platform, namely beHEALTHIER, offers the ability to construct health policies out of data of collective knowledge, by utilizing a newly proposed kind of electronic health records (i.e., eXtended Health Records (XHRs)) and their corresponding networks, through the efficient analysis and management of ingested healthcare data. In order to achieve that, beHEALTHIER is architected based upon four (4) discrete and interacting pillars, namely the Data, the Information, the Knowledge and the Actions pillars. Since the proposed platform is based on MSA, it fully utilizes MSA's benefits, achieving fast response times and efficient mechanisms for healthcare data collection, processing, and analysis.

The abundance of activity trackers and biosignal sensors as well as the evolution of IoT and communication technologies have considerably advanced the concept of Quantified-Self. Nowadays there are several frameworks and applications that realize the concept, focusing though strictly on specific areas, from daily use to professional activities such as sport and healthcare. This work proposes a versatile, cross-domain solution for building quantified-self applications exploiting the capacities for open-design, modularity and extensibility of the AGILE IoT gateway.

Cloud services have emerged as an innovative IT provisioning model in the recent years. However, after their usage severe considerations have emerged with regard to their varying performance due to multitenancy and resource sharing issues. These issues make it very difficult to provide any kind of performance estimation during application design or deployment time. The aim of this paper is to present a mechanism and process for measuring the performance of various Cloud services and describing this information in machine understandable format. The framework is responsible for organizing the execution and can support multiple Cloud providers. Furthermore we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.

Cloud computing is still considered a disruptive technology in spite of being part of our lives for several years now. However, cloud computing is much more than a technology; it is also a business model. Many companies that have sold software in a traditional way are now attending to this revolution, wondering if that new technological and business shift is adequate for them, if they would be able to move their application towards the cloud, transforming alongside the company in a service oriented company and how they could do that. The European Project ARTIST aims to guide companies in this transition by providing them with methods, techniques, and tools, from when the migration is just a thought, until it can be provisioned as a service, taking into account technical, business and organizational aspects.

In modern utility computing infrastructures, like grids and clouds, one of the significant actions of a service provider is to predict the resources needed by the services included in its platform in an automated fashion for service provisioning optimization. Furthermore, a variety of software toolkits exist that implement an extended set of algorithms applicable to workload forecasting. However, their automated use as services in the distributed computing paradigm includes a number of design and implementation challenges. In this paper, a decoupled framework is presented, for taking advantage of software like GNU Octave in the process of creating and using prediction models during the service life cycle of a SOI. A performance analysis of the framework is also conducted. In this context, a methodology for creating parametric or gearbox services with multiple modes of operations based on the execution conditions is portrayed and is applied to transform the aforementioned service framework to optimize service performance. A new estimation algorithm is introduced, that creates performance rules of applications as black boxes, through the creation and usage of genetically optimized artificial neural networks. Through this combination, the critical parameters of the networks are decided through an evolutionary iterative process.

Nowadays the general interest on Cloud Computing as a technical and business solution is growing. The pool of available cloud applications, services and platforms is increasing leveraging new features for the cloud assets provisioned to the end user. This massive amount and divergence of cloud features across the cloud stack layers introduces considerable management overhead to the various stakeholders for the effective management of both the offered products and the system itself. In this paper we focus on the challenges arise for providing dynamic elasticity management as one the fundamental features of Clouds. To this direction, we introduce the concept of Elasticity as a Service and present a generic, service-oriented framework capable to fulfill, in a personalized manner, the elasticity management needs of any cloud environment and cloud-enabled application.

Cloud computing has changed considerably the architecture of modern IT systems introducing new design principles based on the notion of the service as main building unit. Decoupling the service offering from the underlying infrastructure, cloud computing allows for more efficient management of the required resources which in business level is translated into higher flexibility and increased profit. To this end, Clouds are not only an innovative technological environment but also a beneficial market for all the involved stakeholders. This market expands continuously and new service aggregations, customizations and business models are offered in order to cover the needs of all end-users. However, even in this new business ecosystem, the question still remains: “Which offering should I buy”? In this paper we present an approach for incorporating business intelligence in cloud marketplaces through a mechanism for answering this question based on the individual requirements and use profile of each particular cloud customer.

Nowadays cloud computing can be considered as a key element of modern ICT systems, changing the technological and architectural aspects that these systems are designed and managed. The number and variety of applications, exploiting the advantages of this new computing paradigm, is increasing, emerging a new market of services and resources. Modern applications, from enterprise software to mobile and social networking apps are adapted and become available through the Cloud, allowing wider adoption and advanced functionality. Besides the numerous technical and technological advancements, cloud computing also leverages new business models and value networks. To this direction more entities are involved in the service delivery process and marketplaces are created to ease the development of applications through reusability and aggregation of services and resources. In this paper we present an innovative mechanism for the resolution of the customers' requirements which enhances the process of selecting cloud services from the business point of view.

The emergence of new environments such as Cloud computing highlighted new challenges in traditional fields like performance estimation. Most of the current cloud environments follow the Software, Platform, Infrastructure service model in order to map discrete roles / providers according to the offering in each “layer”. However, the limited amount of information passed from one layer to the other has raised the level of difficulty in translating user-understandable application terms from the Software layer to resource specific attributes, which can be used to manage resources in the Platform and Infrastructure layers. In this paper, a generic black box approach, based on Artificial Neural Networks is used in order to perform the aforementioned translation. The efficiency of the approach is presented and validated through different application scenarios (namely FFMPEG encoding and real-time interactive e-Learning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.

Abstract The advent of heterogeneous and distributed environments, such as Grid environments, made feasible the solution to computational‐intensive problems in a reliable and cost‐effective manner. In parallel, workflows with increased complexity that require specialized systems to deal with them are emerging, so as to carry out more composite and mission‐critical applications. In that rationale, quality‐of‐service (QoS) issues need to be tackled in order to ensure that each application satisfies the corresponding user requirements. Therefore, considering the quality provision aspect as fundamental for enabling Grid applications to become QoS compliant, we present an approach for service selection using QoS criteria. The latter is achieved with a suite of components that allow the different mappings of application workflow processes to Grid services that not only meet the user goals and requirements but also maximize his/her benefit in terms of the offered QoS level. We also demonstrate the operation of the aforementioned suite of components and evaluate its performance and effectiveness using a Grid scenario, based on a 3D image rendering application. Copyright © 2008 John Wiley & Sons, Ltd.

A constantly increasing number of applications from various scientific sectors are finding their way towards adopting Grid technologies in order to take advantage of their capabilities: the advent of Grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. The aim of this paper is to demonstrate how multilevel tumour growth and response to therapeutic treatment models can be used in order to simulate clinical trials, with the long-term intention of better designing clinical studies and understanding their outcome based on basic biological science. For this purpose, a computer simulation model of glioblastoma multiforme response to radiotherapy has been applied to perform the aforementioned simulation in a real Grid environment by also taking into account historical data. The proposed approach yields very good results for the conducted virtual trial since these are in agreement with the outcome of the real clinical study, while the use of Grid technologies demonstrate and highlight their added-value.

This paper advocates the need for a mechanism that will allow the evaluation of the provided quality of service (QoS) by a service provider to a service customer in B2B service provisioning. Furthermore, this study goes on with presenting the feasibility of this mechanism by designing and testing a reference implementation that can be used in a service oriented architecture (SOA) environment which is able to support business application services. Experience gained in the frame of the NextGRID IST project that focused on the business perspectives of Grid computing and adopted SOA as its baseline architecture, has shown that such a mechanism is essential for enabling the economic viability of such large scale computing platforms.

A constantly increasing number of applications from various scientific sectors are finding their way towards adopting grid technologies in order to take advantage of their capabilities: the advent of grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. In this paper we present a grid-based approach for aggregation of data that are obtained from various sources (e.g. cameras, sensors) and their analysis with the use of genetic algorithms. By also taking into consideration general historical data and patient-specific medical information, we present the realization of the proposed approach with an application scenario for personalized healthcare and medicine.

Abstract Enabling e‐Business applications on Grid computing has been an important topic of research as the beneficial perspective of Grids lies on solving computational intensive problems by large‐scale distributed resources. In this paper we present a toolkit for centralized workload prediction and access, in order to meet the requirements of execution of commercial business processes on Grid infrastructures as well as to take advantage of their cost‐effective capability for industrial users. The implementation of centralized workload prediction is achieved with an artificial neural network—available in the toolkit—each time a user completes a job execution. This approach has been adopted and validated within the framework of a GRIA IST project for aspecific industrial application, namely 3D image rendering, which meets the e‐Business application's standards. The accuracy of the prediction showed promising results and in combination with the centralized access capability makes e‐Business applications feasible on Grids. Copyright © 2006 John Wiley & Sons, Ltd.