Papers by Maria Mercedes Amor Pinilla
Resumen Cada vez existen más dispositivos de la Internet de las Cosas conectados a Internet que g... more Resumen Cada vez existen más dispositivos de la Internet de las Cosas conectados a Internet que generan una gran cantidad de datos que pueden llegar a congestionar la red en su camino hacia la Nube. Para paliar esta congestión, tecnoloǵıas recientes, como el Edge Computing y el Fog Computing, proponen realizar el procesamiento de los datos en dispositivos más cercanos al origen de estos datos. Esto hace que las infraestructuras sobre las que se despliegan las aplicaciones sean cada vez más variables (diferentes tipo de dispositivos, capacidades de cómputo, caracteŕısticas de red, etc). En este trabajo se presenta una solución para la asignación óptima de tareas a dispositivos del borde, con el objetivo de minimizar el consumo energético de la ejecución de las aplicaciones. Para ello, utilizamos modelos de variabilidad de Lineas de Producto Software para configurar tanto las aplicaciones como las infraestructuras de despliegue, presentando un modelo general para este último. La confi...
In this paper we discuss the shortcomings derived from having coordination and computation tangle... more In this paper we discuss the shortcomings derived from having coordination and computation tangled in the same software entities and from having coordination protocols scattered through the several components participating in an interaction. We show a possible solution to this problem by using aspect-oriented techniques to separate coordination as an independent entity in middleware infrastructures.
13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, 2019
In the last few years, the number of devices connected to the Internet has increased considerably... more In the last few years, the number of devices connected to the Internet has increased considerably; so has the data interchanged between these devices and the Cloud, as well as energy consumption and the risk of network congestion. The problem can be alleviated by reducing communication between Internet-of-Things devices and the Cloud. Recent paradigms, such as Edge Computing and Fog Computing, propose to move data processing tasks from the Cloud to nearby devices to where data is produced or consumed. One of the main challenges of these paradigms is to cope with the heterogeneity of the infrastructures where tasks can be offloaded. This paper presents a solution for the optimal allocation of computational tasks to edge devices, with the aim of minimizing the energy consumption of the overall application. The heterogeneity is represented and managed by using Feature Models, widely employed in Software Product Lines. Given the application and infrastructure configurations, our Optimal Tasks Assignment Framework generates the optimal task allocation and resources assignment. The resultant deployment represents the most energy efficient configuration at load-time, without compromising the user experience. The scalability and energy saving of the approach are evaluated in the domain of augmented reality applications.
2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2017
Currently, mobile devices are the most popular pervasive computing device, and they are becoming ... more Currently, mobile devices are the most popular pervasive computing device, and they are becoming the primer way for Web access. Energy is a critical resource in such pervasive computing devices, being network communication one of the primary energy consuming operations in mobile apps. Indeed, web-based communication is the most used, but also energy demanding. So, mobile web developers should be aware of how much energy consumes the different web-based communication alternatives. The goal of this paper is to measure and compare the energy consumption of three asynchronous Web-based methods in mobile devices. Our experiments consider three different Web applications models that allow a web server to push data to a browser: Polling, Long Polling and WebSockets. The obtained results are analyzed to get more accurate understanding of the impact in energy consumption of a mobile browser for each of these three methods. The utility of these experiments is to show developers what are the factors that influence the energy consumption when different web-based asynchronous communication is used. With this information mobile web developers could reduce the power consumption of web applications on mobile devices, by selecting the most appropriate method for asynchronous server communication.
Advanced smart home appliances and new models of energy tariffs imposed by energy providers pose ... more Advanced smart home appliances and new models of energy tariffs imposed by energy providers pose new challenges in the automation of home energy management. Users need some assistant tool that helps them to make complex decisions with different goals, depending on the current situation. Multi-agent systems have proved to be a suitable technology to develop self-management systems, able to take the most adequate decision under different context-dependent situations, like the home energy management. The heterogeneity of home appliances and also the changes in the energy policies of providers introduce the necessity of explicitly modeling this variability. But, multi-agent systems lack of mechanisms to effectively deal with the different degrees of variability required by these kinds of systems. Software Product Line technologies, including variability models, has been successfully applied to different domains to explicitly model any kind of variability. We have defined a software prod...
13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, 2019
Nowadays, more than one billion people are in need of one or more assistive technologies, and thi... more Nowadays, more than one billion people are in need of one or more assistive technologies, and this number is expected to increase beyond two billion by 2050. The majority of assistive technologies are supported by battery-operated devices like smartphones and wearables. This means that battery weight is an important concern in such assistive devices because it may affect negatively its ergonomics. Saving power in these assistive devices is of utmost importance for its potential twofold benefits: extend the device life and reduce the global warming aggravated by billion of these devices. Dynamic Software Product Lines (DSPLs) are a suitable technology that supports system adaptation, in this case, to reduce energy consumption at runtime, considering contextual information and the current state of the device. However, a reduction in battery consumption could negatively affect other quality of service parameters, like response time. Therefore, it is important to trade-off battery savin...
Advances in Intelligent Systems and Computing, 2016
One of the most important challenges of this decade is the Internet of Things (IoT) that pursues ... more One of the most important challenges of this decade is the Internet of Things (IoT) that pursues the integration of real-world objects in Internet. One of the key areas of the IoT is the Ambient Assisted Living (AAL) systems, which should be able to react to variable and continuous changes while ensuring their acceptance and adoption by users. This means that AAL systems need to work as self-adaptive systems. The autonomy property inherent to software agents, makes them a suitable choice for developing self-adaptive systems. However, agents lack the mechanisms to deal with the variability present in the IoT domain with regard to devices and network technologies. To overcome this limitation we have already proposed a Software Product Line (SPL) process for the development of self-adaptive agents in the IoT. Here we analyze the challenges that poses the development of self-adaptive AAL systems based on agents. To do so, we focus on the domain and application engineering of the self-adaptation concern of our SPL process. In addition, we provide a validation of our development process for AAL systems.
IEEE Internet of Things Journal, 2020
In order to lower latency and reduce energy consumption, Edge Computing proposes offloading some ... more In order to lower latency and reduce energy consumption, Edge Computing proposes offloading some computation intensive tasks usually performed in the Cloud onto nearby devices in the frontier/Edge of the access networks. However, current task offloading approaches are often quite simple. They neither consider the high diversity of hardware and software technologies present in edge network devices, nor take into account that some tasks may require some specific software and hardware infrastructure to be executed. This paper proposes a task offloading process that leans on Software Product Line technologies, which are a very good option to model the variability of software and hardware present in edge environments. Firstly, our approach automates the separation of application tasks, considering the data and operation needs and restrictions among them, and identifying the hardware and software resources required by each task. Secondly, our approach models and manages separately the infrastructure available for task offloading, as a set of nodes that provide certain hardware and software resources. This separation allows to reason about alternative offloading of tasks with different hardware and software resource requirements, in heterogeneous nodes and minimizing energy consumption. In addition, the offloading process considers alternative implementations of tasks to choose the one that best fits the hardware and software characteristics of available edge network infrastructure. The experimental results shows that our approach reduces the energy consumption in the user node by approximately 41%-62%, and the energy consumption of the devices involved in a task offloading solution by 34%-48%.
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Papers by Maria Mercedes Amor Pinilla