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Data centres have become the backbone of large Cloud services and applica-tions, providing virtually unlimited elastic and scalable computational and storage resources. The search for the efficiency and optimisation of resources is one of the current key aspects for large Cloud Service Providers and is becoming more and more challenging, since new computing paradigms such as Internet of Things, Cyber-Physical Systems and Edge Computing are spreading. One of the key aspects to achieve efficiency in data centres consists of the discovery and proper analysis of the data-centre behaviour. In this paper, we present a model to automatically retrieve execution workflows of existing data-centre logs by employing process mining tech-niques. The discovered processes are characterised and analysed according to the understandability and complexity in terms of execution efficiency of data-centre jobs. We finally validate and demonstrate the usability of the proposal by applying the model in a real scenario, that is, the Google Cluster traces

Data centres are quickly evolving to support new demands for Cloud-Computing services. Extreme workload peaks represent a challenge for the maintenance of the performance and service level agreements, even more when operation costs need to be minimised. In this paper, we first present an extensive analysis of the impact of extreme workloads in large-scale realistic Cloud-Computing data centres, including a comparison between the most relevant centralised resource managing models. Moreover, we extend our previous works by proposing a new energy-efficiency policy called Bullfighter which is able to keep performance key performance indicators while reducing energy consumption in extreme scenarios. This policy employs queue-theory distributions to foresee workload demands and adapt automatically to workload fluc tuations even in extreme environments, while avoiding the fine-tuning required for other energy policies. Finally, it is shown through extensive simulation that Bullfighter can save more than 40% of energy in the aforementioned scenarios without exerting any noticeable impact on data-centre performance.

This chapter presents the authors’ work for the Case Study entitled “Delivering Social Media with Scalability” within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with potential solutions addressing the problems of the area and described how these fit in the general ecosystem.

The management and processing of the data generated by healthcare systems is getting more and more attention because of the digitalization of a traditionally analogical sector such as healthcare. Various data management frameworks and solutions have emerged to face some of the multiple challenges present in this environment, includ ing: (a) Data privacy and security; (b) Inter-operability between het erogeneous systems; and (c) Usability and readability of health-related sensitive information. In this paper, the authors share their experience on lecturing on how to address such issues by developing web-based software from several points of views: (a) Healthcare professional needs and requirements in terms of usability, accessibility and easiness; (b) Technical requirements and knowledge required to develop all the layers of a fully functional example of a micro health information system; and (c) Technical require ments to share and distribute the data required by several agents of the healthcare environment, focusing on the adoption of international stan dards such as HL7; (d) Perform of all operations in a secure, available and reliable way. It is concluded that the application of Flipped Teaching among with Project-based Learning may have a very positive impact on both in grades and drop rates for late-years (junior and senior) courses within the Health Engineering bachelor’s degrees.

LECOMP is a mesh network protocol that is focused on minimizing bat tery consumption of nodes in a Wireless Sensor Network (WSN). This paper is a improvement of a first approach [1] which proposes a method to extend the life time and the reliability in a WSN balancing transmissions saving energy throughout different nodes in order to consume homogeneously nodes’ batteries, and taking into account distances and hops of routes. First, a central node analyzes messages from nodes during a training period to determine new routing rules for each sensor. Second, the server configures the network adding the computed rules to the nodes. Third, central server can reconfigure the network routes when new balance of load is needed

This chapter presents a e-health system for dependent people installed in a home environment. After reviewing the state of art in e-health applications and technologies several limitations have been detected because many solutions are proprietary and lack interoperability. The developed home e-health system provides an architecture capable to integrate different telecare services in a smart home gateway hardware independent from the application layer. We propose a rule system to define users’ behavior and monitor relevant events. Two example systems have been implemented to monitor patients. A data model for the e-health platform is described as well.

As a result of the increment of population in countries of Europe, a lot of efforts from European Authorities are coming from. In our research we want to bring forward a suite of developments related to build a ubiquitous AAL (Ambient Assisted Living) environment. We consider that recent approaches are based on ad-hoc technologies so its application is in this context isolated just in one domain of application. Our approach addresses to a reliable services platform for heterogeneous devices integration. On this basis we want to consider as well, the underlying benefits that a Service-oriented platform is giving to us in our Ambient Assisted Living Applications.

The automation of smart environment systems is one of the main goals of smart home researching. This paper focus on learning user lighting preference, considering a working field like a standard office. A review of the smart environment and devices setup is done, showing a real configuration for test purposes. Suitable learning machine techniques are exposed in order to learn these preferences, and suggest the actions the smart environment should execute to satisfy the user preferences. Learning machine techniques proposed are fed with a database, so a proposal for the vectorization of data is described and analyzed.

This communication proposes a basic software and hardware architecture of a controller for the X10 technology interface CM11A, oriented to the world of home automation. The implementation of the system is based in the use of programmable devices such as FPGA (Field Programmable Gate Array). With this controller an end user will be able to control and to manage a set of devices distributed in a domotics space.

This article describes a service architecture for ambient assisted liv ing and in particular an orientation navigation service in open places for persons with memory problems such as those patients suffering from Alzheimer’s in its early stages. The service has the following characteristics: one-day system autonomy; self-adjusting interfaces for simple interaction with patients, based on behavioural patterns to predict routes and destinations and to detect lost situations; easy browsing through simple spoken commands and use of photo graphs for reorientation, and independence of GISs (Geographic Information Systems) to reduce costs and increase accessibility. Initial testing results of the destination prediction algorithm are very positive. This system is integrated in a global e-health/e-care home service architecture platform (OSGi) that enables remote management of services and devices and seamless integration with other home service domains.

This paper reviews the goals of the Domoweb project and the solutions adopted to achieve them. As a result we enjoy a great support to develop smart home techniques and solutions. As a consequence of the acquired experiences a Smart home model is proposed as a division of four main categories. In relation with the smart home model, we show the essential features a smart environment prediction algorithm should satisfy and a procedure to select relevant information from the model to achieve artificial intelligence based solutions.