Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

Data centers are easily found in every sector of the worldwide economy. They are composed of thousands of servers, serving millions of users globally and 24-7. In the last years, e-Science applications such e-Health or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of Data Centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data center electricity represented 1.3% of all the electricity use in the world. In year 2012 alone, global data center power demand grep 63% to 38GW. A further rise of 17% to 43GW was estimated in 2013. Moreover, Data Centers are responsible for more than 2% of total carbon dioxide emissions

Affective computing for smart operations: a survey and comparative analysis of the available tools, libraries and web services

In this paper, we make a deep search of the available tools in the market, at the current state of the art of Sentiment Analysis. Our aim is to optimize the human response in Datacenter Operations, using a combination of research tools, that allow us to decrease human error in general operations, managing Complex Infrastructures. The use of Sentiment Analysis tools is the first step for extending our capabilities for optimizing the human interface. Using different data collections from a variety of data sources, our research provides a very interesting outcome. In our final testing, we have found that the three main commercial platforms (IBM Watson, Google Cloud and Microsoft Azure) get the same accuracy (89-90%). for the different datasets tested, based on Artificial Neural Network and Deep Learning techniques. The other stand-alone Applications or APIs, like Vader or MeaninCloud, get a similar accuracy level in some of the datasets, using a different approach, semantic Networks, such as Concepnet1, but the model can easily be optimized above 90% of accuracy, just adjusting some parameter of the semantic model. This paper points to future directions for optimizing DataCenter Operations Management and decreasing human error in complex environments

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research

Analysis of digital competence of educators (DigCompEdu) in teacher trainees: the context of Melilla, Spain

This study was partly funded by the autonomous city of Melilla, Spain, through PROMESA and the UGR-Enterprise Foundation.The Spanish autonomous city of Melilla, located in northwest Africa, has one of the highest academic failure and abandonment rates in Europe. An effective way to improve this situation would be to improve students’ digital competence. In order to do so, teachers must have competent digital skills themselves and also be able to teach them. To determine teachers’ level of digital competence, the Spanish adaptation of the European Framework for Digital Competence of Educators was used to analyse the self-assessment responses of teachers in training at the Faculty of Education and Sport Sciences in Melilla, Spain. Several quantitative techniques were used to analyse data collected from a questionnaire based on the items in the framework. Indicators were given to each competence using a factor analysis to contrast differences between undergraduate and postgraduate students. Correlations between some of the students’ characteristics and the competences were estimated using OLS. The results show students’ self-assessment level of digital competence in different areas and differences between the bachelor’s and master’s programmes. Digital competence gaps were also detected in teacher training, especially in security. The conclusions highlight the need to improve digital security and facilitate a higher level of digital skills in line with the framework. Indeed, more hours of training in digital competence are required while taking into account the educational context and the technological, pedagogical and content knowledge needed to teach. Equally, the same skills must be developed by educators in order for them to transmit digital competence to their students and support them in educational centres.autonomous city of Melilla, Spain, through PROMESAUGR-Enterprise Foundatio

Preseason training: the effects of a 17-day high-intensity shock microcycle in elite tennis players

Preseasons in tennis are normally reduced to 5 to 7 weeks duration, and coaches should use an integrated approach to conditioning and skill-based work. The aim of the present study was to investigate the effects of adding a high-intensity training (HIT) shock microcycle to the normal training content in several physical performance indicators in the preseason training of high-level male tennis players. Over 17 days, 12 male tennis players performed 13 HIT sessions in addition to their usual training. Physical performance tests (30:15 intermittent fitness test [VIFT], 20 m sprint, countermovement jump [CMJ], repeated sprint ability [RSA]) were conducted before (pre-test) and 5 days after the intervention (post-test). After the shock microcycle, results showed a significant increase in the VIFT (p < 0.001; Large ES) and a significant decrease in the mean RSA time (RSAm) (p = 0.002; Small ES), while there were no significant changes in the other parameters analysed (e.g., 20 m, CMJ, best RSA time [RSAb]; percentage of decrement in the RSA [%Dec]). Moreover, the training load (TL) during tennis sessions was significantly higher (p < 0.01; Large ES) than the TL during the integrated sessions, except during the first training session. A 17-day shock microcycle (i.e., 13 HIT sessions) in addition to the regular tennis training significantly improved parameters that can impact physical performance in tennis. Moreover, additional sessions, including running exercises based on the 30:15ITF and on-court specific exercises, were characterised by significantly lower TL than tennis-training sessions

Detecting false testimonies in reputation systems using self-organizing maps

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios

AMISEC: Leveraging Redundancy and Adaptability to Secure AmI Applications

Security in Ambient Intelligence (AmI) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in AmI applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability

Self-organizing maps versus growing neural Gas in detecting anomalies in data centers

Reliability is one of the key performance factors in data centres. The out-of-scale energy costs of these facilities lead data centre operators to increase the ambient temperature of the data room to decrease cooling costs. However, increasing ambient temperature reduces the safety margins and can result in a higher number of anomalous events. Anomalies in the data centre need to be detected as soon as possible to optimize cooling efficiency and mitigate the harmful effects over servers. This article proposes the usage of clustering-based outlier detection techniques coupled with a trust and reputation system engine to detect anomalies in data centres. We show how self-organizing maps or growing neural gas can be applied to detect cooling and workload anomalies, respectively, in a real data centre scenario with very good detection and isolation rates, in a way that is robust to the malfunction of the sensors that gather server and environmental information

A novel energy-driven computing paradigm for e-health scenarios

A first-rate e-Health system saves lives, provides better patient care, allows complex but useful epidemiologic analysis and saves money. However, there may also be concerns about the costs and complexities associated with e-health implementation, and the need to solve issues about the energy footprint of the high-demanding computing facilities. This paper proposes a novel and evolved computing paradigm that: (i) provides the required computing and sensing resources; (ii) allows the population-wide diffusion; (iii) exploits the storage, communication and computing services provided by the Cloud; (iv) tackles the energy-optimization issue as a first-class requirement, taking it into account during the whole development cycle. The novel computing concept and the multi-layer top-down energy-optimization methodology obtain promising results in a realistic scenario for cardiovascular tracking and analysis, making the Home Assisted Living a reality

On the leakage-power modeling for optimal server operation

Leakage power consumption is a com- ponent of the total power consumption in data cen- ters that is not traditionally considered in the set- point temperature of the room. However, the effect of this power component, increased with temperature, can determine the savings associated with the careful management of the cooling system, as well as the re- liability of the system. The work presented in this paper detects the need of addressing leakage power in order to achieve substantial savings in the energy consumption of servers. In particular, our work shows that, by a careful detection and management of two working regions (low and high impact of thermal- dependent leakage), energy consumption of the data- center can be optimized by a reduction of the cooling budget