Improving Real-Time Data Dissemination Performance by Multi Path Data Scheduling in Data Grids

The performance of data grids for data intensive, real-time applications is highly dependent on the data dissemination algorithm employed in the system. Motivated by this fact, this study first formally defines the real-time splittable data dissemination problem (RTS/DDP) where data transfer requests can be routed over multiple paths to maximize the number of data transfers to be completed before their deadlines. Since RTS/DDP is proved to be NP-hard, four different heuristic algorithms, namely kSP/ESMP, kSP/BSMP, kDP/ESMP, and kDP/BSMP are proposed. The performance of these heuristic algorithms is analyzed through an extensive set of data grid system simulation scenarios. The simulation results reveal that a performance increase up to 8 % as compared to a very competitive single path data dissemination algorithm is possible

New Challenges in Quality of Services Control Architectures in Next Generation Networks

A mesura que Internet i les xarxes IP s'han anat integrant dins la societat i les corporacions, han anat creixent les expectatives de nous serveis convergents així com les expectatives de qualitat en les comunicacions. Les Next Generation Networks (NGN) donen resposta a les noves necessitats i representen el nou paradigma d'Internet a partir de la convergència IP. Un dels aspectes menys desenvolupats de les NGN és el control de la Qualitat del Servei (QoS), especialment crític en les comunicacions multimèdia a través de xarxes heterogènies i/o de diferents operadors. A més a més, les NGN incorporen nativament el protocol IPv6 que, malgrat les deficiències i esgotament d'adreces IPv4, encara no ha tingut l'impuls definitiu.Aquesta tesi està enfocada des d'un punt de vista pràctic. Així doncs, per tal de poder fer recerca sobre xarxes de proves (o testbeds) que suportin IPv6 amb garanties de funcionament, es fa un estudi en profunditat del protocol IPv6, del seu grau d'implementació i dels tests de conformància i interoperabilitat existents que avaluen la qualitat d'aquestes implementacions. A continuació s'avalua la qualitat de cinc sistemes operatius que suporten IPv6 mitjançant un test de conformància i s'implementa el testbed IPv6 bàsic, a partir del qual es farà la recerca, amb la implementació que ofereix més garanties.El QoS Broker és l'aportació principal d'aquesta tesi: un marc integrat que inclou un sistema automatitzat per gestionar el control de la QoS a través de sistemes multi-domini/multi-operador seguint les recomanacions de les NGN. El sistema automatitza els mecanismes associats a la configuració de la QoS dins d'un mateix domini (sistema autònom) mitjançant la gestió basada en polítiques de QoS i automatitza la negociació dinàmica de QoS entre QoS Brokers de diferents dominis, de forma que permet garantir QoS extrem-extrem sense fissures. Aquesta arquitectura es valida sobre un testbed de proves multi-domini que utilitza el mecanisme DiffServ de QoS i suporta IPv6.L'arquitectura definida en les NGN permet gestionar la QoS tant a nivell 3 (IP) com a nivell 2 (Ethernet, WiFi, etc.) de forma que permet gestionar també xarxes PLC. Aquesta tesi proposa una aproximació teòrica per aplicar aquesta arquitectura de control, mitjançant un QoS Broker, a les noves xarxes PLC que s'estan acabant d'estandarditzar, i discuteix les possibilitats d'aplicació sobre les futures xarxes de comunicació de les Smart Grids.Finalment, s'integra en el QoS Broker un mòdul per gestionar l'enginyeria del tràfic optimitzant els dominis mitjançant tècniques de intel·ligència artificial. La validació en simulacions i sobre un testbed amb routers Cisco demostra que els algorismes genètics híbrids són una opció eficaç en aquest camp.En general, les observacions i avenços assolits en aquesta tesi contribueixen a augmentar la comprensió del funcionament de la QoS en les NGN i a preparar aquests sistemes per afrontar problemes del món real de gran complexitat.A medida que Internet y las redes IP se han ido integrando dentro de la sociedad y las corporaciones, han ido creciendo las expectativas de nuevos servicios convergentes así como las expectativas de calidad en las comunicaciones. Las Next Generation Networks (NGN) dan respuesta a las nuevas necesidades y representan el nuevo paradigma de Internet a partir de la convergencia IP. Uno de los aspectos menos desarrollados de las NGN es el control de la Calidad del Servicio (QoS), especialmente crítico en las comunicaciones multimedia a través de redes heterogéneas y/o de diferentes operadores. Además, las NGN incorporan nativamente el protocolo IPv6 que, a pesar de las deficiencias y agotamiento de direcciones IPv4, aún no ha tenido el impulso definitivo.Esta tesis está enfocada desde un punto de vista práctico. Así pues, con tal de poder hacer investigación sobre redes de prueba (o testbeds) que suporten IPv6 con garantías de funcionamiento, se hace un estudio en profundidad del protocolo IPv6, de su grado de implementación y de los tests de conformancia e interoperabilidad existentes que evalúan la calidad de estas implementaciones. A continuación se evalua la calidad de cinco sistemas operativos que soportan IPv6 mediante un test de conformancia y se implementa el testbed IPv6 básico, a partir del cual se realizará la investigación, con la implementación que ofrece más garantías.El QoS Broker es la aportación principal de esta tesis: un marco integrado que incluye un sistema automatitzado para gestionar el control de la QoS a través de sistemas multi-dominio/multi-operador siguiendo las recomendaciones de las NGN. El sistema automatiza los mecanismos asociados a la configuración de la QoS dentro de un mismo dominio (sistema autónomo) mediante la gestión basada en políticas de QoS y automatiza la negociación dinámica de QoS entre QoS brokers de diferentes dominios, de forma que permite garantizar QoS extremo-extremo sin fisuras. Esta arquitectura se valida sobre un testbed de pruebas multi-dominio que utiliza el mecanismo DiffServ de QoS y soporta IPv6. La arquitectura definida en las NGN permite gestionar la QoS tanto a nivel 3 (IP) o como a nivel 2 (Ethernet, WiFi, etc.) de forma que permite gestionar también redes PLC. Esta tesis propone una aproximación teórica para aplicar esta arquitectura de control, mediante un QoS Broker, a las noves redes PLC que se están acabando de estandardizar, y discute las posibilidades de aplicación sobre las futuras redes de comunicación de las Smart Grids.Finalmente, se integra en el QoS Broker un módulo para gestionar la ingeniería del tráfico optimizando los dominios mediante técnicas de inteligencia artificial. La validación en simulaciones y sobre un testbed con routers Cisco demuestra que los algoritmos genéticos híbridos son una opción eficaz en este campo.En general, las observaciones y avances i avances alcanzados en esta tesis contribuyen a augmentar la comprensión del funcionamiento de la QoS en las NGN y en preparar estos sistemas para afrontar problemas del mundo real de gran complejidad.The steady growth of Internet along with the IP networks and their integration into society and corporations has brought with it increased expectations of new converged services as well as greater demands on quality in communications. The Next Generation Networks (NGNs) respond to these new needs and represent the new Internet paradigm from the IP convergence. One of the least developed aspects in the NGNs is the Quality of Service (QoS) control, which is especially critical in the multimedia communication through heterogeneous networks and/or different operators. Furthermore, the NGNs natively incorporate the IPv6 protocol which, despite its shortcomings and the depletion of IPv4 addresses has not been boosted yet.This thesis has been developed with a practical focus. Therefore, with the aim of carrying out research over testbeds supporting the IPv6 with performance guarantees, an in-depth study of the IPv6 protocol development has been conducted and its degree of implementation and the existing conformance and interoperability tests that evaluate these implementations have been studied. Next, the quality of five implementations has been evaluated through a conformance test and the basic IPv6 testbed has been implemented, from which the research will be carried out. The QoS Broker is the main contribution to this thesis: an integrated framework including an automated system for QoS control management through multi-domain/multi-operator systems according to NGN recommendations. The system automates the mechanisms associated to the QoS configuration inside the same domain (autonomous system) through policy-based management and automates the QoS dynamic negotiation between peer QoS Brokers belonging to different domains, so it allows the guarantee of seamless end-to-end QoS. This architecture is validated over a multi-domain testbed which uses the QoS DiffServ mechanism and supports IPv6.The architecture defined in the NGN allows QoS management at level 3 (IP) as well as at level 2 (e.g. Ethernet, WiFi) so it also facilitates the management of PLC networks. Through the use of a QoS Broker, this thesis proposes a theoretical approach for applying this control architecture to the newly standardized PLC networks, and discusses the possibilities of applying it over the future communication networks of the Smart Grids.Finally, a module for managing traffic engineering which optimizes the network domains through artificial intelligence techniques is integrated in the QoS Broker. The validations by simulations and over a Cisco router testbed demonstrate that hybrid genetic algorithms are an effective option in this area.Overall, the advances and key insights provided in this thesis help advance our understanding of QoS functioning in the NGNs and prepare these systems to face increasingly complex problems, which abound in current industrial and scientific applications

Performance Optimization and Dynamics Control for Large-scale Data Transfer in Wide-area Networks

Transport control plays an important role in the performance of large-scale scientific and media streaming applications involving transfer of large data sets, media streaming, online computational steering, interactive visualization, and remote instrument control. In general, these applications have two distinctive classes of transport requirements: large-scale scientific applications require high bandwidths to move bulk data across wide-area networks, while media streaming applications require stable bandwidths to ensure smooth media playback. Unfortunately, the widely deployed Transmission Control Protocol is inadequate for such tasks due to its performance limitations. The purpose of this dissertation is to conduct rigorous analytical study of the design and performance of transport solutions, and develop an integrated transport solution in a systematical way to overcome the limitations of current transport methods. One of the primary challenges is to explore and compose a set of feasible route options with multiple constraints. Another challenge essentially arises from the randomness inherent in wide-area networks, particularly the Internet. This randomness must be explicitly accounted for to achieve both goodput maximization and stabilization over the constructed routes by suitably adjusting the source rate in response to both network and host dynamics.The superior and robust performance of the proposed transport solution is extensively evaluated in a simulated environment and further verified through real-life implementations and deployments over both Internet and dedicated connections under disparate network conditions in comparison with existing transport methods

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

An integrated transport solution to big data movement in high-performance networks

Extreme-scale e-Science applications in various domains such as earth science and high energy physics among multiple national institutions within the U.S. are generating colossal amounts of data, now frequently termed as “big data”. The big data must be stored, managed and moved to different geographical locations for distributed data processing and analysis. Such big data transfers require stable and high-speed network connections, which are not readily available in traditional shared IP networks such as the Internet. High-performance networking technologies and services featuring high bandwidth and advance reservation are being rapidly developed and deployed across the nation and around the globe to support such scientific applications. However, these networking technologies and services have not been fully utilized, mainly because: i) the use of these technologies and services often requires considerable domain knowledge and many application users are even not aware of their existence; and ii) the end-to-end data transfer performance largely depends on the transport protocol being used on the end hosts. The high-speed network path with reserved bandwidth in High-performance Networks has shifted the data transfer bottleneck from network segments in traditional IP networks to end hosts, which most existing transport protocols are not well suited to handle. In this dissertation, an integrated transport solution is proposed in support of data- and network-intensive applications in various science domains. This solution integrates three major components, i.e., i) transport-support workflow optimization, ii) transport profile generation, and iii) transport protocol design, into a unified framework. Firstly, a class of transport-support workflow optimization problems are formulated, where an appropriate set of resources and services are selected to compose the best transport-support workflow to meet user’s data transfer request in terms of various performance requirements. Secondly, a transport profiler named Transport Profile Generator (TPG) and its extended and accelerated version named FastProf are designed and implemented to characterize and enhance the end-to-end data transfer performance of a selected transport method over an established network path. Finally, several approaches based on rate and error threshold control are proposed to design a suite of data transfer protocols specifically tailored for big data transfer over dedicated connections. The proposed integrated transport solution is implemented and evaluated in: i) a local testbed with a single 10 Gb/s back-to-back connection and dual 10 Gb/s NIC-to-NIC connections; and ii) several wide-area networks with 10 Gb/s long-haul connections at collaborative sites including Oak Ridge National Laboratory, Argonne National Laboratory, and University of Chicago

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Autonomous grid scheduling using probabilistic job runtime scheduling

Computational Grids are evolving into a global, service-oriented architecture – a universal platform for delivering future computational services to a range of applications of varying complexity and resource requirements. The thesis focuses on developing a new scheduling model for general-purpose, utility clusters based on the concept of user requested job completion deadlines. In such a system, a user would be able to request each job to finish by a certain deadline, and possibly to a certain monetary cost. Implementing deadline scheduling is dependent on the ability to predict the execution time of each queued job, and on an adaptive scheduling algorithm able to use those predictions to maximise deadline adherence. The thesis proposes novel solutions to these two problems and documents their implementation in a largely autonomous and self-managing way. The starting point of the work is an extensive analysis of a representative Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected by the Grid middleware for accounting purposes. An automated approach is proposed to identify these dependencies and use them to partition the highly variable workload into subsets of more consistent and predictable behaviour. A range of time-series forecasting models, applied in this context for the first time, were used to model the job execution times as a function of their historical behaviour and associated properties. Based on the resulting predictions of job runtimes a novel scheduling algorithm is able to estimate the latest job start time necessary to meet the requested deadline and sort the queue accordingly to minimise the amount of deadline overrun. The testing of the proposed approach was done using the actual job trace collected from a production Grid facility. The best performing execution time predictor (the auto-regressive moving average method) coupled to workload partitioning based on three simultaneous job properties returned the median absolute percentage error centroid of only 4.75%. This level of prediction accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler. Overall, the thesis demonstrates that deadline scheduling of computational jobs on the Grid is achievable using statistical forecasting of job execution times based on historical information. The proposed approach is easily implementable, substantially self-managing and better matched to the human workflow making it well suited for implementation in the utility Grids of the future

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi