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El presente proyecto trata sobre la optimización energética del centro escolar Jesús-María Villafranqueza. Se realizará un análisis de la contabilidad energética a través de las facturas, instalaciones y horarios de uso. Se hará un desglose de las facturas indicando a que se debe cada coste y una representación de la variación de consumo a lo largo del año. Una vez finalizado el estudio de las facturas hay que recopilar información de los distintos elementos de consumo energético instalados en el centro, indicando la cantidad y potencia que presentan. Con estos datos y el horario de uso, se podrá generar una curva de funcionamiento, donde se comparen el consumo recopilado de las facturas eléctricas y el consumo calculado de las instalaciones. Al finalizar el estudio se analizan los resultados obtenidos y calculamos ratios energéticos de interés del edifico. Más adelante se realizarán distintas propuestas de mejora energética con el análisis económico de las mismas, generando un plan de actuación en función del tiempo de retorno., The present project deals with the energy optimization of Jesús-María Villafranqueza school center. An analysis of energy accounting will be carried out through invoices, installations and hours of use. A breakdown of the invoices will be made indicating which is due each cost and a representation of the variation of consumption throughout the year. Once the study of the invoices has been completed, information must be collected on the different elements of energy consumption installed in the center, indicating the quantity and power they present. With these data and the hours of use, an operating curve can be generated, where the consumption collected from the electric bills and the calculated consumption of the facilities are compared. At the end of the study, the results obtained are analyzed and we calculate energy ratios of interest for the building. Later, different energy improvement proposals will be made with the economic analysis of them, generating an action plan based on the return time.

Energy consumption is a key concern in cloud computing. The paper reports on a cloud architecture to support energy efficiency at service construction, deployment, and operation. This is achieved through SaaS, PaaS and IaaS intra-layer self-adaptation in isolation. The self-adaptation mechanisms are discussed, as well as their implementation and evaluation. The experimental results show that the overall architecture is capable of adapting to meet the energy goals of applications on a per layer basis., This work is partly supported by the European Commission under FP7-ICT-2013.1.2 contract 610874 (ASCETiC project), by the Spanish Goverment under contract TIN2015-65316-P and BES-2013-067167 and by the Generalitat de Catalunya under contract 2014-SGR-1051. Thanks to GreenPreFab Italia for providing the jEPlus application and TU Berlin for their technical support., Peer Reviewed, Postprint (published version)

The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error)., This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener- alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer)., Peer Reviewed, Postprint (author's final draft)

Creación de una web que permita el envío, descarga y reproducción de ficheros de audio creados por los usuarios, así como un sistema de votación y premios gestionado por la propia comunidad., We Love Remixes ( WLR ) is the practical case that has been chosen to achieve the main objective of the Project, that is the acquisition of advanced knowledge and experience in the most used technologies in web application development and web projects nowadays (HTML5 , CSS3 , symfony 2 jQuery, GIT, ... ) ., We Love Remixes (WLR) es el caso práctico que se ha escogido para conseguir el objetivo principal del proyecto, que es la adquisición de conocimientos avanzados y la experiencia en las tecnologías más usadas en el desarrollo de aplicaciones y proyectos web hoy en día (HTML5, CSS3, Symfony 2, jQuery, GIT, ...)., We Love Remixes (WLR) es el cas pràctic que s'ha escollit per aconseguir l'objectiu principal del projecte, que és l'adquisición de coneixements avançats i l'experiencia en les tecnologíes más utilitzades en el desenvolupament d'aplicacions i projectes web avui día (HTML5, CSS3, Symfony 2, jQuery, GIT, ...).

Cloud service providers offer access to their resources through formal service level agreements (SLA), and need well-balanced infrastructures so that they can maximise the quality of service (QoS) they offer and minimise the number of SLA violations. This paper focuses on a specific aspect of risk assessment as applied in cloud computing: methods within a framework that can be used by cloud service providers and service consumers to assess risk during service deployment and operation. It describes the various stages in the service lifecycle whereas risk assessment takes place, and the corresponding risk models that have been designed and implemented. The impact of risk on architectural components, with special emphasis on holistic management support at service operation, is also described. The risk assessor is shown to be effective through the experimental evaluation of the implementation, and is already integrated in a cloud computing toolkit., Peer Reviewed, Postprint (author's final draft)

The rapid increase of data centre industry has stimulated the interest of both researchers and professionals in order to reduce energy consumption and carbon footprint of these unique infrastructures. The implementation of energy efficiency strategies and the use of renewables play an important role to reduce the overall data centre energy demand. Information Technology (IT) equipment produce vast amount of heat which must be removed and therefore waste heat recovery is a likely energy efficiency strategy to be studied in detail. To evaluate the potential of heat reuse a unique liquid cooled data centre test bench was designed and built. An extensive thermal characterization under different scenarios was performed. The effective liquid cooling capacity is affected by the inlet water temperature. The lower the inlet water temperature the higher the liquid cooling capacity; however, the outlet water temperature will be also low. Therefore, the requirements of the heat reuse application play an important role in the optimization of the cooling configuration. The experimental data was then used to validate a dynamic energy model developed in TRNSYS. This model is able to predict the behaviour of liquid cooling data centres and can be used to study the potential compatibility between large data centres with different heat reuse applications. The model also incorporates normalized power consumption profiles for heterogeneous workloads that have been derived from realistic IT loads., Peer Reviewed, Postprint (author's final draft)

Online Reputation Systems help mitigate the information asymmetry between clients and providers in Cloud Computing Markets. However, those systems raise two main drawbacks: the disagreement for assuming the cost of ownership of such services and their vulnerability to reputation attacks from dishonest parties that want to increase their reputation. This article faces both problems by describing a decentralised (peer-to-peer) trust model that does not require the intervention of a central entity to manage it. This model includes mechanisms to allow participants to avoid dishonest behaviour from other peers: each client statistically analyses the external reports about providers and updates the trustworthiness of the peers. The trustworthiness values will be used to negotiate prices in later transactions. The trust model is then incorporated in the Service-Level Agreement negotiation and enforcement processes, prioritising trusted clients over non-trusted clients to minimise the consequences of low Quality of Service in relation to the trust of the provider, and incentivise accurate trust reports from the clients. Finally, this article evaluates and discusses the validity of the trust model under different attacks from dishonest clients and providers., Peer Reviewed, Postprint (author's final draft)

Resolved

The paper argues the need to provide novel methods and tools to support software developers aiming to optimise energy efficiency and minimise the carbon footprint resulting from designing, developing, deploying and running software in Clouds, while maintaining other quality aspects of software to adequate and agreed levels. A cloud architecture to support energy efficiency at service construction, deployment, and operation is discussed, as well as its implementation and evaluation plans., Postprint (published version)

Cloud Computing markets arise as an efficient way to allocate resources for the execution of tasks and services within a set of geographically dispersed providers from different organisations. Client applications and service providers meet in a market and negotiate for the sales of services by means of the signature of a Service Level Agreement that contains the Quality of Service terms that the Cloud provider has to guarantee by managing properly its resources. Current implementations of Cloud markets suffer from a lack of information flow between the negotiating agents, which sell the resources, and the resource managers that allocate the resources to fulfil the agreed Quality of Service. This thesis establishes an intermediate layer between the market agents and the resource managers. In consequence, agents can perform accurate negotiations by considering the status of the resources in their negotiation models, and providers can manage their resources considering both the performance and the business objectives. This thesis defines a set of policies for the negotiation and enforcement of Service Level Agreements. Such policies deal with different Business-Level Objectives: maximisation of the revenue, classification of clients, trust and reputation maximisation, and risk minimisation. This thesis demonstrates the effectiveness of such policies by means of fine-grained simulations. A pricing model may be influenced by many parameters. The weight of such parameters within the final model is not always known, or it can change as the market environment evolves. This thesis models and evaluates how the providers can self-adapt to changing environments by means of genetic algorithms. Providers that rapidly adapt to changes in the environment achieve higher revenues than providers that do not. Policies are usually conceived for the short term: they model the behaviour of the system by considering the current status and the expected immediate after their application. Thi, Postprint (published version)

Reducing energy consumption is increasingly gaining attention in the area of Cloud computing, as means to reduce costs and improve corporate sustainability image. ASCETiC is focused on providing novel methods and tools to support software developers to optimise energy efficiency and minimise the carbon footprint resulting from developing, deploying and running software in Clouds. At the same time, quality of service, experience and perception are still taken into account, so energy efficiency will complement them and boost Cloud efficiency at several dimensions. ASCETiC primary focus is to relate software design and energy use, which will depend on the deployment conditions and the correct operation of the software by means of an adaptive environment. This paper presents specific objectives for the project, as well as requirements, business goals and architecture for the resultant Open Source Cloud stack providing energy efficiency at software, platform and infrastructure Cloud layers., Peer Reviewed, Postprint (published version)

In order to improve service execution in Clouds, the management of Cloud Infrastructure has to take measures to adhere to Service Level Agreements and Business Level Objectives, from the application layer through to how services are supported at the lowest hardware levels. In this paper a risk model methodology and holistic management approach is developed specific to the operation of the Cloud Infrastructure Provider and is applied through improvements to SLA fault tolerance in Cloud Infrastructure. Risk assessments are used to analyse execution specific data from the Cloud Infrastructure and linked to a business driven holistic management component that is part of a Cloud Manager. Initial results show improved eco-efficiency, virtual machine availability and reductions in SLA failure across the whole Cloud infrastructure by applying our combined risk-based fault tolerance approach., Postprint (author’s final draft)

Resolved

Resolved

Success of Cloud computing requires that both customers and providers can be confident that signed Service Level Agreements (SLA) are supporting their respective business activities to their best extent. Currently used SLAs fail in providing such confidence, especially when providers outsource resources to other providers. These resource providers typically support very simple metrics like availability, or metrics that hinder an efficient exploitation of their resources. In this paper, we propose a resource-level metric for specifying fine-grain guarantees on CPU performance. This metric allows resource providers to allocate dynamically their resources among running services depending on their demand. This is accomplished by incorporating the customer’s CPU usage in the metric definition, but avoiding fake SLA violations when the customer’s task does not use all its allocated resources. We have conducted the evaluation in a virtualized provider where we have implemented the needed infrastructure for using our metric. As demonstrated in our evaluation, our solution presents fewer SLA violations than other CPU-related metrics while maintaining the Quality of Service., Peer Reviewed, Postprint (published version)

In Utility Computing business model, the owners of the computing resources negotiate with their potential clients to sell computing power. The terms of the Quality of Service (QoS) to be provided as well as the economic conditions are established in a Service-Level Agreement (SLA). There are situations in which providers must differentiate the SLAs in function of the type of Client that is willing to access the resources or the agreed QoS e.g. when the hardware resources are shared between users of the company that own the resources and external users. This paper proposes to consider the information of potential users when the SLA is under negotiation to allow providers to prioritize users (e.g. internal users over external users, or preferential users over common users). Two policies for negotiation are introduced: price discrimination and client-aware overselling of resources. The validity of the policies is demonstrated through exhaustive experiments., Postprint (author’s final draft)

In recent years, Electrical Bioimpedance (EBI) methods have gained importance. These methods are often based on obtaining impedance spectrum in the range of β-dispersion, i.e. from a few kHz up to some MHz. To measure EBI a constant current is often injected and the voltage across the tissue under study is recorded. Due to the performance of the current source influences the performance of the entire system, in terms of frequency range, several designs have been implemented and studied. In this paper the basic structure of a Voltage- Controlled Current Source based on a single Op-Amp in inverter configuration with a floating load, known as load-in-the-loop current source, is revisited and studied deeply. We focus on the dependence of the output impedance with the circuit parameters, i.e. the feedback resistor and the inverter-input resistor, and the Op-Amp main parameters, i.e. open loop gain, CMRR and input impedance. After obtaining the experimental results, using modern Op-Amps, and comparing to the theoretical and simulated ones, they confirm the design under study can be a good solution for multi-frequency wideband EBI applications because of higher values of the output impedance than 100kΩ at 1MHz are obtained. Furthermore, an enhancement of the basic design, using a current conveyor as a first stage, is proposed, studied and implemented., Postprint (published version)

Commercialization of computing resources will become more and more important as the transition from Grid computing in academic environments to commercial services based on concepts such as utility or Cloud computing progresses. This results in the necessity to not only base components on technical aspects, but also to include economical aspects in their design. This paper presents a framework that links technical and economical aspects to the management of computational resources. Economic enhancements like dynamic pricing and client classification are introduced based on a technical resource management environment and positioned within this resulting in a proposed architecture for an Economically Enhanced Resource Manager (EERM). The introduced approach is evaluated considering various economic design criteria and example scenarios., Peer Reviewed, Postprint (published version)

Success of Cloud computing requires that both customers and providers can be confident that signed Service Level Agreements (SLA) are supporting their respective business activities to their best extent. Currently used SLAs fail in providing such confidence, especially when providers outsource resources to other providers. These resource providers typically support very simple metrics, or metrics that hinder an efficient exploitation of their resources. In this paper, we propose a resource-level metric for specifying finegrain guarantees on CPU performance. This metric allows resource providers to allocate dynamically their resources among the running services depending on their demand. This is accomplished by incorporating the customer’s CPU usage in the metric definition, but avoiding fake SLA violations when the customer’s task does not use all its allocated resources. As demonstrated in our evaluation, which has been conducted in a virtualized provider where we have implemented the needed infrastructure for using our metric, our solution presents fewer SLA violations than other CPU-related metrics., Peer Reviewed, Postprint (published version)

Markets arise as an efficient way of organising resources in Cloud Computing scenarios. In Cloud Computing Markets, Brokers that represent both Clients and Service Providers meet in a Market and negotiate for the sales of resources or services. This paper defends the idea that efficient negotiations require of the usage of resource-level information for increasing the accuracy of negotiated Service Level Agreements and facilitating the achievement of both performance and business goals. A negotiation model based on the maximisation of nonadditive utility functions that considers multiple objectives is defined, and its validity is demonstrated in the experiments., Postprint (published version)

This paper proposes a bidirectional communication between market brokers and resource managers in Cloud Computing Markets. This communication is implemented by means of an Economically Enhanced Resource Manager (EERM), that supports the negotiation process by deciding which tasks can be allocated or not, and under which economic and technical conditions. The EERM also uses the economic information that collects from market layers to manage the resources accordingly to concrete BLOs. This paper shows several Business Policies and Rules for maximizing the revenue of a Cloud Provider that sells its services and resources in a market. Their validity is demonstrated through several experiments that shown how the application of these rules can have a positive influence in the revenue and minimize the violations of Service-Level Agreements., Preprint

Postprint (published version)

The Cloud computing paradigm is offering an innovative and promising vision concerning the Information and Communications Technology (ICT). Notwithstanding, the use of Cloud resources, which usually are external assets to their consumers, implies risk issues that must be taken into account. In this paper, we present a Cloud computing risk management approach aware of the Business-Level Objectives (BLOs) of a given Cloud organization. More to the point, we propose an innovatory SEmi-quantitative BLO-driven Cloud Risk Assessment (SEBCRA) as its core subprocess. In addition, we present, as a use case, a Cloud Service Provider (CSP) that is able to improve the achievement of a BLO, i.e. profit maximization, by managing, assessing, and treating Cloud risks. As demonstrated in the experimentation, this provider maximizes its profit by transferring risks of provisioning its private Cloud to third-party providers of Cloud infrastructures., Postprint (published version)

This paper introduces several Business Rules for maximising the revenue of Providers in Cloud Computing Markets. These rules apply in both negotiation and execution time, and enforce the achievement of Business-Level Objectives by establishing a bidirectional data flow between market and resource layers. The experiments demonstrate that the revenue is maximized by using both resource data when negotiating, and economic information when managing the resources., Postprint (published version)

Cloud computing is strongly based on virtualization, allowing applications to be multiplexed onto a physical resource while isolated from other applications sharing that physical resource. This technology simplifies the management of e-Infrastructures, but also requires additional effort if users are to benefit from it. Cloud computing must hide its underlying complexity from users: the key is to provide users with a simple but functional interface for accessing IT resources "as a service", while allowing providers to build costeffective self-managed systems for transparently managing these resources. System developers should be also supported with simple tools that allow them to exploit the facilities of cloud infrastructures., Peer Reviewed, Postprint (published version)

Market-based resource allocation is a promising model for dealing with the growing Utility Computing environments, such as Grid or Cloud Computing. Agents that represent both service clients and providers meet in a market to negotiate the terms of the sale of resources. Additive negotiation models are extended because they are simple, but they are not valid for negotiations whose terms are not independent between them. This paper proposes a simple non-additive model for performing negotiations and demonstrates its validity through simulation experiments., Postprint (published version)

The aim of the chapter is to describe how service-level agreements (SLAs) could be utilized to provide the basis for resource trading based on economic models. SLAs enable a service user to identify their requirements, and a provider to identify their capabilities. Subsequently, the terms in an SLA are necessary to ensure that mutually agreeable quality is being delivered by the provider according to the agreement. The use of service-level agreements (SLAs) in a resource management system to support Grid computing applications is described. To this end, we provide an architecture that supports the creation and management of SLAs. The architecture of the system, in terms of the components and their interactions, is first presented, followed by a description of the specific requirements for a marketoriented Grid economy. We use SLAs as a means to support reliable quality of service for Grid jobs. The creation of such an SLA requires planning and orchestration mechanisms. We will discuss these functionalities and also consider the economic aspects such as dynamic pricing and negotiation mechanisms. These mechanisms are necessary to enable SLA formation and use, and to ensure that an SLA is being adhered to during service provision., Postprint (published version)

The trustworthiness of commodity providers is a key issue to motivate customers to participate in a market. Several Grid Market frameworks are being developed and they are not an exception in the need of demonstrate their trustworthiness to clients. This paper proposes a simple reputation mechanism for auctions within Grid Markets, which is easy to embed in old Grid solutions that do not provide reputation yet. Effectiveness of this mechanism is proved through simulation in different market scenarios, by showing the direct correlation between reputation and revenue of Grid service providers., Postprint (published version)

Traditional resource management has had as its main objective the optimization of throughput, based on parameters such as CPU, memory, and network bandwidth. With the appearance of Grid markets, new variables that determine economic expenditure, benefit and opportunity must be taken into account. The Self-organizing ICT Resource Management (SORMA) project aims at allowing resource owners and consumers to exploit market mechanisms to sell and buy resources across the Grid. SORMA’s motivation is to achieve efficient resource utilization by maximizing revenue for resource providers and minimizing the cost of resource consumption within a market environment. An overriding factor in Grid markets is the need to ensure that the desired quality of service levels meet the expectations of market participants. This paper explains the proposed use of an economically enhanced resource manager (EERM) for resource provisioning based on economic models. In particular, this paper describes techniques used by the EERM to support revenue maximization across multiple service level agreements and provides an application scenario to demonstrate its usefulness and effectiveness., Peer Reviewed, Postprint (published version)

Commercialization of Grid resources will become more and more important as utility computing and the deployment of Grids gains momentum. This results in the necessity to not only base Grid components on technical aspects, but also to include economical aspects in their design. This paper presents a framework that links technical and economical aspects to the management of computational resources. Economic enhancements like dynamic pricing and client classification are introduced based on a technical resource management environment and positioned within this resulting in a proposed architecture for an Economically Enhanced Resource Manager (EERM). The introduced approach is evaluated considering various economic design criteria and example scenarios., Postprint (published version)

Hay mucha excitación en relación con el análisis del big data, pero también mucha confusión en decidir por dónde empezar para aquellos que quieren iniciarse en la programación en este apasionante mundo. Este libro proporciona al lector una oportunidad para empezar a programar y manejar datos a través del ecosistema Apache Spark. Spark es actualmente uno de los paquetes de código abierto más importantes en el espacio del big data y por el que importantes empresas, como IBM, SAP, Oracle o Amazon, han apostado, al tiempo que son también grandes contribuidoras. Este libro, que puede utilizarse como texto de autoestudio o de soporte a cursos que requieran una introducción a Apache Spark, contiene unaexcelente visión introductoria de Apache Spark, una descripción de su ecosistema y de sus características básicas e incluye ejemplos de código para que el lector los pueda probar en su propio PC si lo desea y así tener una comprensión de primera mano de algunas de sus posibilidades.

Hay mucha excitación en relación con el análisis del big data, pero también mucha confusión en decidir por dónde empezar para aquellos que quieren iniciarse en la programación en este apasionante mundo. Este libro proporciona al lector una oportunidad para empezar a programar y manejar datos a través del ecosistema Apache Spark. Spark es actualmente uno de los paquetes de código abierto más importantes en el espacio del big data y por el que importantes empresas, como IBM, SAP, Oracle o Amazon, han apostado, al tiempo que son también grandes contribuidoras. Este libro, que puede utilizarse como texto de autoestudio o de soporte a cursos que requieran una introducción a Apache Spark, contiene unaexcelente visión introductoria de Apache Spark, una descripción de su ecosistema y de sus características básicas e incluye ejemplos de código para que el lector los pueda probar en su propio PC si lo desea y así tener una comprensión de primera mano de algunas de sus posibilidades.

Hay mucha excitación en relación con el análisis del big data, pero también mucha confusión en decidir por dónde empezar para aquellos que quieren iniciarse en la programación en este apasionante mundo. Este libro proporciona al lector una oportunidad para empezar a programar y manejar datos a través del ecosistema Apache Spark. Spark es actualmente uno de los paquetes de código abierto más importantes en el espacio del big data y por el que importantes empresas, como IBM, SAP, Oracle o Amazon, han apostado, al tiempo que son también grandes contribuidoras. Este libro, que puede utilizarse como texto de autoestudio o de soporte a cursos que requieran una introducción a Apache Spark, contiene unaexcelente visión introductoria de Apache Spark, una descripción de su ecosistema y de sus características básicas e incluye ejemplos de código para que el lector los pueda probar en su propio PC si lo desea y así tener una comprensión de primera mano de algunas de sus posibilidades.

Hay mucha excitación en relación con el análisis del big data, pero también mucha confusión en decidir por dónde empezar para aquellos que quieren iniciarse en la programación en este apasionante mundo. Este libro proporciona al lector una oportunidad para empezar a programar y manejar datos a través del ecosistema Apache Spark. Spark es actualmente uno de los paquetes de código abierto más importantes en el espacio del big data y por el que importantes empresas, como IBM, SAP, Oracle o Amazon, han apostado, al tiempo que son también grandes contribuidoras. Este libro, que puede utilizarse como texto de autoestudio o de soporte a cursos que requieran una introducción a Apache Spark, contiene unaexcelente visión introductoria de Apache Spark, una descripción de su ecosistema y de sus características básicas e incluye ejemplos de código para que el lector los pueda probar en su propio PC si lo desea y así tener una comprensión de primera mano de algunas de sus posibilidades.