329 research outputs found

    Resource allocation in mobile edge cloud computing for data-intensive applications

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    Rapid advancement in the mobile telecommunications industry has motivated the development of mobile applications in a wide range of social and scientific domains. However, mobile computing (MC) platforms still have several constraints, such as limited computation resources, short battery life and high sensitivity to network capabilities. In order to overcome the limitations of mobile computing and benefit from the huge advancement in mobile telecommunications and the rapid revolution of distributed resources, mobile-aware computing models, such as mobile cloud computing (MCC) and mobile edge computing (MEC) have been proposed. The main problem is to decide on an application execution plan while satisfying quality of service (QoS) requirements and the current status of system networking and device energy. However, the role of application data in offloading optimisation has not been studied thoroughly, particularly with respect to how data size and distribution impact application offloading. This problem can be referred to as data-intensive mobile application offloading optimisation. To address this problem, this thesis presents novel optimisation frameworks, techniques and algorithms for mobile application resource allocation in mobile-aware computing environments. These frameworks and techniques are proposed to provide optimised solutions to schedule data intensive mobile applications. Experimental results show the ability of the proposed tools in optimising the scheduling and the execution of data intensive applications on various computing environments to meet application QoS requirements. Furthermore, the results clearly stated the significant contribution of the data size parameter on scheduling the execution of mobile applications. In addition, the thesis provides an analytical investigation of mobile-aware computing environments for a certain mobile application type. The investigation provides performance analysis to help users decide on target computation resources based on application structure, input data, and mobile network status

    Satellite integration in 5G : contribution on network architectures and traffic engineering solutions for hybrid satellite-terrestrial mobile backhauling

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    The recent technological advances in the satellite domain such as the use of High Throughput Satellites (HTS) with throughput rates that are magnitudes higher than with previous ones, or the use of large non- Geostationary Earth Orbit (GEO) satellites constellations, etc, are reducing the price per bit and enhancing the Quality of Service (QoS) metrics such as latency, etc., changing the way that the capacity is being brought to the market and making it more attractive for other services such as satellite broadband communications. These new capabilities coupled with the advantages offered by satellite communications such as the unique wide-scale geographical coverage, inherent broadcast/multicast capabilities and highly reliable connectivity, anticipate new opportunities for the integration of the satellite component into the 5G ecosystem. One of the most compelling scenarios is mobile backhauling, where satellite capacity can be used to complement the terrestrial backhauling infrastructure, not only in hard to reach areas, but also for more efficient traffic delivery to Radio Access Network (RAN) nodes, increased resiliency and better support for fast, temporary cell deployments and moving cells. In this context, this thesis work focuses on achieving better satellite-terrestrial backhaul network integration through the development of Traffic Engineering (TE) strategies to manage in a better way the dynamically steerable satellite provisioned capacity. To do this, this thesis work first takes the steps in the definition of an architectural framework that enables a better satellite-terrestrial mobile backhaul network integration, managing the satellite capacity as a constituent part of a Software Defined Networking (SDN) -based TE for mobile backhaul network. Under this basis, this thesis work first proposes and assesses a model for the analysis of capacity and traffic management strategies for hybrid satellite-terrestrial mobile backhauling networks that rely on SDN for fine-grained traffic steering. The performance analysis is carried out in terms of capacity gains that can be achieved when the satellite backhaul capacity is used for traffic overflow, taking into account the placement of the satellite capacity at different traffic aggregation levels and considering a spatial correlation of the traffic demand. Later, the thesis work presents the development of SDN-based TE strategies and algorithms that exploits the dynamically steerable satellite capacity provisioned for resilience purposes to better utilize the satellite capacity by maximizing the network utility under both failure and non-failure conditions in some terrestrial links, under the consideration of elastic, inelastic and unicast and multicast traffic. The performance analysis is carried out in terms of global network utility, fairness and connexion rejection rates compared to non SDN-based TE applications. Finally, sustained in the defined architectural framework designs, the thesis work presents an experimental Proof of Concept (PoC) and validation of a satellite-terrestrial backhaul links integration solution that builts upon SDN technologies for the realization of End-to-End (E2E) TE applications in mobile backhauling networks with a satellite component, assessing the feasibility of the proposed SDN-based integration solution under a practical laboratory setting that combines the use of commercial, experimentation-oriented and emulation equipment and software.Los recientes avances tecnológicos en el dominio de los satélites, como el uso de satélites de alto rendimiento (HTS) con tasas de rendimiento que son magnitudes más altas que los anteriores, o el uso de grandes constelaciones de satélites de órbita no geoestacionaria (GEO), etc. están reduciendo el precio por bit y mejorando las métricas de Calidad de Servicio (QoS) como la latencia, etc., cambiando la forma en que la capacidad se está llevando al mercado, y haciéndola más atractiva para otros servicios como las comunicaciones de banda ancha por satélite. Estas nuevas capacidades, junto con las ventajas ofrecidas por las comunicaciones por satélite, como la cobertura geográfica a gran escala, las inherentes capacidades de difusión / multidifusión y la conectividad altamente confiable, anticipan nuevas oportunidades para la integración de la componente satelital al ecosistema 5G. Uno de los escenarios más atractivos es el backhauling móvil, donde la capacidad del satélite se puede usar para complementar la infraestructura de backhauling terrestre, no solo en áreas de difícil acceso, sino también para la entrega de tráfico de manera más eficiente a los nodos de la Red de Acceso (RAN), una mayor resiliencia y mejor soporte para implementaciones rápidas y temporales de células, así como células en movimiento. En este contexto, este trabajo de tesis se centra en lograr una mejor integración de la red híbrida de backhaul satélital-terrestre, a través del desarrollo de estrategias de ingeniería de tráfico (TE) para gestionar de una mejor manera la capacidad dinámicamente orientable del satélite. Para hacer esto, este trabajo de tesis primero toma los pasos en la definición de un marco de arquitectura que permite una mejor integración de una red híbrida satelital-terrestre de backhaul móvil, gestionando la capacidad del satélite como parte constitutiva de un TE basado en Software Defined Networking (SDN). Bajo esta base, este trabajo de tesis primero propone y evalúa un modelo para el análisis de la capacidad y las estrategias de gestión del tráfico para redes híbridas satelital-terrestre de backhaul móvil basadas en SDN para la dirección de tráfico. El análisis de rendimiento se lleva a cabo en términos de aumento de capacidad que se puede lograr cuando la capacidad de la red de backhaul por satélite se utiliza para el desborde de tráfico, teniendo en cuenta la ubicación de la capacidad del satélite en diferentes niveles de agregación de tráfico y considerando una correlación espacial de la demanda de tráfico. Posteriormente, el trabajo de tesis presenta el desarrollo de estrategias y algoritmos de TE basados en SDN que explotan la capacidad dinámicamente orientable del satelite, provista con fines de resiliencia para utilizar de mejor manera la capacidad satelital al maximizar la utilidad de red en condiciones de falla y no falla en algunos enlaces terrestres, y bajo la consideración de tráfico elástico, inelástico y de unidifusión y multidifusión. El análisis de rendimiento se lleva a cabo en términos de tasas de rechazo, de utilidad, y equidad en comparación con las aplicaciones de TE no basadas en SDN. Finalmente, basado en la definición del diseño de marco de arquitectura, el trabajo de tesis presenta una Prueba de concepto (PoC) experimental y la validación de una solución de integración de enlaces de backhaul satelital-terrestre que se basa en las tecnologías SDN para la realización de aplicaciones de TE de extremo a extremo (E2E) en redes de backhaul móviles, evaluando la viabilidad de la solución propuesta de integración basada en SDN en un entorno práctico de laboratorio que combina el uso de equipos y software comerciales, orientados a la experimentación y emulación.Postprint (published version

    Energy-aware scheduling in distributed computing systems

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    Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto

    Advances in Grid Computing

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    This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

    AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

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    The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice Framework (AIMS). AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored for the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions of demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have a self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we've highlighted its prowess in three case studies from biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances

    A Model Driven Approach to Model Transformations

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    The OMG's Model Driven Architecture (MDA) initiative has been the focus of much attention in both academia and industry, due to its promise of more rapid and consistent software development through the increased use of models. In order for MDA to reach its full potential, the ability to manipulate and transform models { most obviously from the Platform Independent Model (PIM) to the Platform Specific Models (PSM) { is vital. Recognizing this need, the OMG issued a Request For Proposals (RFP) largely concerned with finding a suitable mechanism for trans- forming models. This paper outlines the relevant background material, summarizes the approach taken by the QVT-Partners (to whom the authors belong), presents a non-trivial example using the QVT-Partners approach, and finally sketches out what the future holds for model transformations

    Organic Service-Level Management in Service-Oriented Environments

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    Dynamic service-oriented environments (SOEs) are characterised by a large number of heterogeneous service components that are expected to support the business as a whole. The present work provides a negotiation-based approach to facilitate automated and multi-level service-level management in an SOE, where each component autonomously arranges its contribution to the whole operational goals. Evaluation experiments have shown an increased responsiveness and stability of an SOE in case of changes

    Integrating multiple clusters for compute-intensive applications

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    Multicluster grids provide one promising solution to satisfying the growing computational demands of compute-intensive applications. However, it is challenging to seamlessly integrate all participating clusters in different domains into a single virtual computational platform. In order to fully utilize the capabilities of multicluster grids, computer scientists need to deal with the issue of joining together participating autonomic systems practically and efficiently to execute grid-enabled applications. Driven by several compute-intensive applications, this theses develops a multicluster grid management toolkit called Pelecanus to bridge the gap between user\u27s needs and the system\u27s heterogeneity. Application scientists will be able to conduct very large-scale execution across multiclusters with transparent QoS assurance. A novel model called DA-TC (Dynamic Assignment with Task Containers) is developed and is integrated into Pelecanus. This model uses the concept of a task container that allows one to decouple resource allocation from resource binding. It employs static load balancing for task container distribution and dynamic load balancing for task assignment. The slowest resources become useful rather than be bottlenecks in this manner. A cluster abstraction is implemented, which not only provides various cluster information for the DA-TC execution model, but also can be used as a standalone toolkit to monitor and evaluate the clusters\u27 functionality and performance. The performance of the proposed DA-TC model is evaluated both theoretically and experimentally. Results demonstrate the importance of reducing queuing time in decreasing the total turnaround time for an application. Experiments were conducted to understand the performance of various aspects of the DA-TC model. Experiments showed that our model could significantly reduce turnaround time and increase resource utilization for our targeted application scenarios. Four applications are implemented as case studies to determine the applicability of the DA-TC model. In each case the turnaround time is greatly reduced, which demonstrates that the DA-TC model is efficient for assisting application scientists in conducting their research. In addition, virtual resources were integrated into the DA-TC model for application execution. Experiments show that the execution model proposed in this thesis can work seamlessly with multiple hybrid grid/cloud resources to achieve reduced turnaround time
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