Person:
Ayala Rodrigo, José Luis

First Name

José Luis

Last Name

Ayala Rodrigo

URI

https://hdl.handle.net/20.500.14352/78677

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Informática

Department

Arquitectura de Computadores y Automática

Area

Arquitectura y Tecnología de Computadores

Identifiers

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Search Results

Now showing 1 - 10 of 10

Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments
(Sensors, 2012) Zapater, Marina; Sanchez, Cesar; Ayala Rodrigo, José Luis; Moya, Jose M.; Risco Martín, José Luis
Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.
An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devices
(2017) Pagán Ortiz, Josué; Fallahzadeh, Ramin ; Ghasemzadeh, Hassan ; Moya, Jose M.; Risco Martín, José Luis; Ayala Rodrigo, José Luis
Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with limited battery autonomy. Several researchers focus their efforts in reducing the energy consumption of these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption of monitoring devices for the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of several sources of energy consumption: the running code for prediction, and the sensors for data acquisition. As a result of our methodology, we are able to improve the energy consumption of the computing process up to 90% with a minimal impact on accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.
Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities.
(Energy Procedia, 6th International conference on sustainability in energy and buildings, 2014) Arroba, Patricia; Risco Martín, José Luis; Zapater Sancho, Marina; Moya, José Manuel; Ayala Rodrigo, José Luis; Olcoz Herrero, Katzalin
As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simultaneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.
Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data
(Sensors, 2015) Pagán, Josué; De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco Martín, José Luis; Vivancos Mora, J.; Moya, José M.; Ayala Rodrigo, José Luis
Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.
Runtime data center temperature prediction using Grammatical Evolution techniques
(Applied soft computing, 2016) Zapater, Marina; Risco Martín, José Luis; Arroba, Patricia; Ayala Rodrigo, José Luis; Moya, José M.; Hermida Correa, Román
A Real-Time Framework for a DEVS-based MigrainePrediction Simulator System
(2016) Pagán Ortiz, Josué; Risco Martín, José Luis; Moya Fernández, José Manuel; Ayala Rodrigo, José Luis
The migraine disease is one of the most disabling neurological diseases that negatively impacts on the quality of life and on the cost of the public health services. The prediction of a migraine symptomatic event through monitorization of hemodynamic variables has been previously demonstrated in our previous works. In this paper, a first approach for the development of a simulator for a real time migraine prediction system is shown. The simulator has been implemented using a formal description language and validated using Grammatical Evolutionary models. The results encourage to develop real time techniques to trigger accurate alarms and real time repairing techniques of disrupted signals. All these problems will be faced in our future work by HW/SW co-simulation and including Hardware In the Loop components, in order to simulate failures in sensors or trigger alarms.
Grammatical Evolutionary Techniques for Prompt Migraine Prediction
(2016) Pagán Ortiz, Josué; Risco Martín, José Luis; José M. Moya; Ayala Rodrigo, José Luis
The migraine disease is a chronic headache presenting symptomatic crisis that causes high economic costs to the national health services, and impacts negatively on the quality of life of the patients. Even if some patients can feel unspecific symptoms before the onset of the migraine, these only happen randomly and cannot predict the crisis precisely. In our work, we have proved how migraine crisis can be predicted with high accuracy from the physiological variables of the patients, acquired by a non-intrusive Wireless Body Sensor Network. In this paper, we derive alternative models for migraine prediction using Grammatical Evolution techniques. We obtain prediction horizons around 20 minutes, which are sufficient to advance the drug intake and avoid the symptomatic crisis. The robustness of the models with respect to sensor failures has also been tackled to allow the practical implementation in the ambulatory monitoring platform. The achieved models are non linear mathematical expressions with low computing overhead during the run-time execution in the wearable devices.
Advanced migraine prediction simulation system
(2017) Pagán Ortiz, Josué; Moya Fernández, José Manuel; Risco Martín, José Luis; Ayala Rodrigo, José Luis
In the Internet of Things (IoT) era, there is growing interest in wireless monitoring sensors for detection, classification and prediction of health symptoms. The prediction of symptoms in chronic diseases such as migraines brings new hope to improve patients' lives. The prediction of a migraine symptomatic event through monitoring hemodynamic variables has been previously demonstrated in our earlier work. In this paper, a simulation-based approach for a real-time migraine prediction system is described. The simulation has been implemented using the specifications of the formal description language Discrete EVent Systems (DEVS). The simulation system is a proof of concept that is ready for testing in a real-world ambulatory monitoring environment. The results obtained encourage developing a hardware/software (HW/SW) co-simulation system that incorporates Hardware-in-the-Loop (HIL) components as prior step to the expensive and slow hardware implementation of a complete migraine prediction device. When such a system is used in a real-time setting, it can simulate failures in sensors and trigger alarms for active patient response.
Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases
(Journal Of Biomedical Informatics, 2016) Pagán Ortiz, Josué; Risco Martín, José Luis; Moya, José M. ; Ayala Rodrigo, José Luis
Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40 min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises.
Método para determinar el nivel de activación del sistema trigémino-vascular
(2018) Risco Martín, José Luis; Sobrado Sanz, Mónica; Vivancos Mora, José Aurelio; Ayala Rodrigo, José Luis; Gago Veiga, Ana Beatriz; Pagán Ortiz, Josué; Fundación para la Investigación Biomédica del Hospital Universitario La Princesa