Patricia Arroba

A Methodology for Developing Accessible Mobile Platforms over Leading Devices for Visually Impaired People

Mobile user interfaces are moving to new touchscreen technologies setting new barriers for the blind. Many solutions and designs have been proposed but none is complete for the vast heterogeneous variety of devices.

Dynamic Voltage and Frequency Scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers

Computational demand in data centers is increasing because of the growing popularity of Cloud applications. However, data centers are becoming unsustainable in terms of power consumption and growing energy costs so Cloud providers have to face the major challenge of placing them on a more scalable curve. Also, Cloud services are provided under strict Service Level Agreement conditions, so trade‐offs between energy and performance have to be taken into account. Techniques as Dynamic Voltage and Frequency Scaling (DVFS) and consolidation are commonly used to reduce the energy consumption in data centers, although they are applied independently and their effects on Quality of Service are not always considered. Thus, understanding the relationship between power, DVFS, consolidation, and performance is crucial to enable energy‐efficient management at the data center level. In this work, we propose a DVFS policy that reduces power consumption while preventing performance degradation, and a DVFS‐aware consolidation policy that optimizes consumption, considering the DVFS configuration that would be necessary when mapping Virtual Machines to maintain Quality of Service. We have performed an extensive evaluation on the CloudSim toolkit using real Cloud traces and an accurate power model based on data gathered from real servers. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 41.62% for scenarios under dynamic workload conditions. These outcomes outperforms previous approaches, that do not consider integrated use of DVFS and consolidation strategies.

Enhancing Regression Models for Complex Systems Using Evolutionary Techniques for Feature Engineering

This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer’s expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. 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. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98 %. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.

DVFS-Aware Consolidation for Energy-Efficient Clouds

Nowadays, data centers consume about 2% of the worldwide energy production, originating more than 43 million tons of CO2 per year. Cloud providers need to implement an energy-efficient management of physical resources in order to meet the growing demand for their services and ensure minimal costs. From the application-framework viewpoint, Cloud workloads present additional restrictions as 24/7 availability, and SLA constraints among others. Also, workload variation impacts on the performance of two of the main strategies for energy-efficiency in Cloud data centers: Dynamic Voltage and Frequency Scaling (DVFS) and Consolidation. Our work proposes two contributions: 1) a DVFS policy that takes into account the trade-offs between energy consumption and performance degradation; 2) a novel consolidation algorithm that is aware of the frequency that would be necessary when allocating a Cloud workload in order to maintain QoS. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 39.14% for scenarios under dynamic workload conditions.

A Trust and Reputation System for Energy Optimization in Cloud Data Centers

The increasing success of Cloud Computing applications and online services has contributed to the unsustainability of data center facilities in terms of energy consumption. Higher resource demand has increased the electricity required by computation and cooling resources, leading to power shortages and outages, specially in urban infrastructures. Current energy reduction strategies for Cloud facilities usually disregard the data center topology, the contribution of cooling consumption and the scalability of optimization strategies. Our work tackles the energy challenge by proposing a temperature-aware VM allocation policy based on a Trust-and-Reputation System (TRS). A TRS meets the requirements for inherently distributed environments such as data centers, and allows the implementation of autonomous and scalable VM allocation techniques. For this purpose, we model the relationships between the different computational entities, synthesizing this information in one single metric. This metric, called reputation, would be used to optimize the allocation of VMs in order to reduce energy consumption. We validate our approach with a state-of-the-art Cloud simulator using real Cloud traces. Our results show considerable reduction in energy consumption, reaching up to 46.16% savings in computing power and 17.38% savings in cooling, without QoS degradation while keeping servers below thermal redlining. Moreover, our results show the limitations of the PUE ratio as a metric for energy efficiency. To the best of our knowledge, this paper is the first approach in combining Trust-and-Reputation systems with Cloud Computing VM allocation.

Green Adaptation of Real-Time Web Services for Industrial CPS Within a Cloud Environment

Managing energy efficiency under timing constraints is an interesting and big challenge. This paper proposes an accurate power model in data centers for time-constrained servers in Cloud computing. This model, as opposed to previous approaches, does not only consider the workload assigned to the processing element, but also incorporates the need of considering the static power consumption and, even more interestingly, its dependency with temperature. The proposed model has been used in a multiobjective optimization environment in which the dynamic voltage and frequency scaling and workload assignment have been efficiently optimized.