A PVSS Application for Monitoring the Start-up of the Super Proton Synchrotron after Major Breakdowns

Supervisory Control and Data Acquisition (SCADA) systems are widely employed in monitoring and controlling technical facilities at the European Organization for Nuclear Research (CERN). Various kinds of SCADA systems are used for the supervision of electricity, cooling, cryogenics and other systems as wells as for the control of the laboratory's particle accelerators and high-energy physics (HEP) experiments. This thesis is concerned with the development of a software application for two of CERN's main control rooms, for monitoring the start-up of the Super Proton Synchrotron (SPS), the laboratory's second largest particle accelerator. Following a CERN recommendation, the application is based on PVSS II, a commercial off-the-shell SCADA product that will replace the heterogeneous component architecture currently used for monitoring SPS equipment. The set-up of the SCADA system in a redundant, distributed and scattered manner in order to guarantee high dependability and the possibility of doing data exchange with external PVSS II systems is a central issue of this work. A PVSS Driver Manager to SL-Equip, a middleware allowing data exchange with heterogeneous remote devices located around the accelerator, is developed in order to communicate with SPS hardware. The object-oriented design and the C++ implementation of this driver are discussed in a detailed manner. Special attention is paid to the design and implementation of the application's user interface, for this is the part of the system that the control rooms' operators will be confronted with on a daily basis. A first prototype of this interface, consisting of a series of PVSS panels, is developed in close co-operation with the operators concerned. In addition to the application itself, an off-line database system for managing static PVSS configuration information is created. An integration strategy for existing configuration data is developed, a relational database structure for storing the information is designed and Perl scripts and PL/SQL procedures for data import and export are implemented

HARD: Hybrid Adaptive Resource Discovery for Jungle Computing

In recent years, Jungle Computing has emerged as a distributed computing paradigm based on simultaneous combination of various hierarchical and distributed computing environments which are composed by large number of heterogeneous resources. In such a computing environment, the resources and the underlying computation and communication infrastructures are highly-hierarchical and heterogeneous. This creates a lot of difficulty and complexity for finding the proper resources in a precise way in order to run a particular job on the system efficiently. This paper proposes Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. The proposed approach supports distributed query processing within and across hierarchical layers by deploying various distributed resource discovery services and functionalities in the system which are implemented using different adapted algorithms and mechanisms in each level of hierarchy. The proposed approach addresses the requirements for resource discovery in Jungle Computing environments such as high-hierarchy, high-heterogeneity, high-scalability and dynamicity. Simulation results show significant scalability and efficiency of the proposed approach over highly heterogeneous, hierarchical and dynamic computing environments

Contributions to energy-aware demand-response systems using SDN and NFV for fog computing

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns worldwide that drive the urgent creation of new energy management and consumption schemes. In this regard, by leveraging the massive connectivity provided by emerging communications such as the 5G systems, this thesis proposes a long-term sustainable Demand-Response solution for the adaptive and efficient management of available energy consumption for Internet of Things (IoT) infrastructures, in which energy utilization is optimized based on the available supply. In the proposed approach, energy management focuses on consumer devices (e.g., appliances such as a light bulb or a screen). In this regard, by proposing that each consumer device be part of an IoT infrastructure, it is feasible to control its respective consumption. The proposal includes an architecture that uses Network Functions Virtualization (NFV) and Software Defined Networking technologies as enablers to promote the primary use of energy from renewable sources. Associated with architecture, this thesis presents a novel consumption model conditioned on availability in which consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as the prioritization of the energy supply, workload scheduling using time-shifting capabilities, and quality degradation to decrease- the power demanded by consumers if needed. The adaptive energy management solution is modeled as an Integer Linear Programming, and its complexity has been identified to be NP-Hard. To verify the improvements in energy utilization, an optimal algorithmic solution based on a brute force search has been implemented and evaluated. Because the hardness of the adaptive energy management problem and the non-polynomial growth of its optimal solution, which is limited to energy management for a small number of energy demands (e.g., 10 energy demands) and small values of management mechanisms, several faster suboptimal algorithmic strategies have been proposed and implemented. In this context, at the first stage, we implemented three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs). Then, we incorporated into both the optimal and heuristic strategies a prepartitioning method in which the total set of analyzed services is divided into subsets of smaller size and complexity that are solved iteratively. As a result of the adaptive energy management in this thesis, we present eight strategies, one timal and seven heuristic, that when deployed in communications infrastructures such as the NFV domain, seek the best possible scheduling of demands, which lead to efficient energy utilization. The performance of the algorithmic strategies has been validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and the processing of energy demands. Additionally, the simulation results revealed that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands. This thesis also explores possible application scenarios of both the proposed architecture for adaptive energy management and algorithmic strategies. In this regard, we present some examples, including adaptive energy management in-home systems and 5G networks slicing, energy-aware management solutions for unmanned aerial vehicles, also known as drones, and applicability for the efficient allocation of spectrum in flex-grid optical networks. Finally, this thesis presents open research problems and discusses other application scenarios and future work.El constante aumento del consumo de energía, el agotamiento de los recursos no renovables, el impacto climático asociado con la generación de energía y la capacidad finita de producción de energía son preocupaciones importantes en todo el mundo que impulsan la creación urgente de nuevos esquemas de consumo y gestión de energía. Al aprovechar la conectividad masiva que brindan las comunicaciones emergentes como los sistemas 5G, esta tesis propone una solución de Respuesta a la Demanda sostenible a largo plazo para la gestión adaptativa y eficiente del consumo de energía disponible para las infraestructuras de Internet of Things (IoT), en el que se optimiza la utilización de la energía en función del suministro disponible. En el enfoque propuesto, la gestión de la energía se centra en los dispositivos de consumo (por ejemplo, electrodomésticos). En este sentido, al proponer que cada dispositivo de consumo sea parte de una infraestructura IoT, es factible controlar su respectivo consumo. La propuesta incluye una arquitectura que utiliza tecnologías de Network Functions Virtualization (NFV) y Software Defined Networking como habilitadores para promover el uso principal de energía de fuentes renovables. Asociada a la arquitectura, esta tesis presenta un modelo de consumo condicionado a la disponibilidad en el que los consumidores son parte del proceso de gestión. Para utilizar eficientemente la energía de fuentes renovables y no renovables, se proponen varias estrategias de gestión, como la priorización del suministro de energía, la programación de la carga de trabajo utilizando capacidades de cambio de tiempo y la degradación de la calidad para disminuir la potencia demandada. La solución de gestión de energía adaptativa se modela como un problema de programación lineal entera con complejidad NP-Hard. Para verificar las mejoras en la utilización de energía, se ha implementado y evaluado una solución algorítmica óptima basada en una búsqueda de fuerza bruta. Debido a la dureza del problema de gestión de energía adaptativa y el crecimiento no polinomial de su solución óptima, que se limita a la gestión de energía para un pequeño número de demandas de energía (por ejemplo, 10 demandas) y pequeños valores de los mecanismos de gestión, varias estrategias algorítmicas subóptimos más rápidos se han propuesto. En este contexto, en la primera etapa, implementamos tres estrategias heurísticas: una estrategia codiciosa (GreedyTs), una solución basada en algoritmos genéticos (GATs) y un enfoque de programación dinámica (DPTs). Luego, incorporamos tanto en la estrategia óptima como en la- heurística un método de prepartición en el que el conjunto total de servicios analizados se divide en subconjuntos de menor tamaño y complejidad que se resuelven iterativamente. Como resultado de la gestión adaptativa de la energía en esta tesis, presentamos ocho estrategias, una óptima y siete heurísticas, que cuando se despliegan en infraestructuras de comunicaciones como el dominio NFV, buscan la mejor programación posible de las demandas, que conduzcan a un uso eficiente de la energía. El desempeño de las estrategias algorítmicas ha sido validado a través de extensas simulaciones en varios escenarios, demostrando mejoras en el consumo de energía y el procesamiento de las demandas de energía. Los resultados de la simulación revelaron que los enfoques heurísticos producen soluciones de alta calidad cercanas a las óptimas mientras se ejecutan entre dos y siete órdenes de magnitud más rápido y con aplicabilidad a escenarios con miles y cientos de miles de demandas de energía. Esta tesis también explora posibles escenarios de aplicación tanto de la arquitectura propuesta para la gestión adaptativa de la energía como de las estrategias algorítmicas. En este sentido, presentamos algunos ejemplos, que incluyen sistemas de gestión de energía adaptativa en el hogar, en 5G networkPostprint (published version

Descoberta de recursos para sistemas de escala arbitrarias

Doutoramento em InformáticaTecnologias de Computação Distribuída em larga escala tais como Cloud, Grid, Cluster e Supercomputadores HPC estão a evoluir juntamente com a emergência revolucionária de modelos de múltiplos núcleos (por exemplo: GPU, CPUs num único die, Supercomputadores em single die, Supercomputadores em chip, etc) e avanços significativos em redes e soluções de interligação. No futuro, nós de computação com milhares de núcleos podem ser ligados entre si para formar uma única unidade de computação transparente que esconde das aplicações a complexidade e a natureza distribuída desses sistemas com múltiplos núcleos. A fim de beneficiar de forma eficiente de todos os potenciais recursos nesses ambientes de computação em grande escala com múltiplos núcleos ativos, a descoberta de recursos é um elemento crucial para explorar ao máximo as capacidade de todos os recursos heterogéneos distribuídos, através do reconhecimento preciso e localização desses recursos no sistema. A descoberta eficiente e escalável de recursos ´e um desafio para tais sistemas futuros, onde os recursos e as infira-estruturas de computação e comunicação subjacentes são altamente dinâmicas, hierarquizadas e heterogéneas. Nesta tese, investigamos o problema da descoberta de recursos no que diz respeito aos requisitos gerais da escalabilidade arbitrária de ambientes de computação futuros com múltiplos núcleos ativos. A principal contribuição desta tese ´e a proposta de uma entidade de descoberta de recursos adaptativa híbrida (Hybrid Adaptive Resource Discovery - HARD), uma abordagem de descoberta de recursos eficiente e altamente escalável, construída sobre uma sobreposição hierárquica virtual baseada na auto-organizaçãoo e auto-adaptação de recursos de processamento no sistema, onde os recursos computacionais são organizados em hierarquias distribuídas de acordo com uma proposta de modelo de descriçãoo de recursos multi-camadas hierárquicas. Operacionalmente, em cada camada, que consiste numa arquitetura ponto-a-ponto de módulos que, interagindo uns com os outros, fornecem uma visão global da disponibilidade de recursos num ambiente distribuído grande, dinâmico e heterogéneo. O modelo de descoberta de recursos proposto fornece a adaptabilidade e flexibilidade para executar consultas complexas através do apoio a um conjunto de características significativas (tais como multi-dimensional, variedade e consulta agregada) apoiadas por uma correspondência exata e parcial, tanto para o conteúdo de objetos estéticos e dinâmicos. Simulações mostram que o HARD pode ser aplicado a escalas arbitrárias de dinamismo, tanto em termos de complexidade como de escala, posicionando esta proposta como uma arquitetura adequada para sistemas futuros de múltiplos núcleos. Também contribuímos com a proposta de um regime de gestão eficiente dos recursos para sistemas futuros que podem utilizar recursos distribuíos de forma eficiente e de uma forma totalmente descentralizada. Além disso, aproveitando componentes de descoberta (RR-RPs) permite que a nossa plataforma de gestão de recursos encontre e aloque dinamicamente recursos disponíeis que garantam os parâmetros de QoS pedidos.Large scale distributed computing technologies such as Cloud, Grid, Cluster and HPC supercomputers are progressing along with the revolutionary emergence of many-core designs (e.g. GPU, CPUs on single die, supercomputers on chip, etc.) and significant advances in networking and interconnect solutions. In future, computing nodes with thousands of cores may be connected together to form a single transparent computing unit which hides from applications the complexity and distributed nature of these many core systems. In order to efficiently benefit from all the potential resources in such large scale many-core-enabled computing environments, resource discovery is the vital building block to maximally exploit the capabilities of all distributed heterogeneous resources through precisely recognizing and locating those resources in the system. The efficient and scalable resource discovery is challenging for such future systems where the resources and the underlying computation and communication infrastructures are highly-dynamic, highly-hierarchical and highly-heterogeneous. In this thesis, we investigate the problem of resource discovery with respect to the general requirements of arbitrary scale future many-core-enabled computing environments. The main contribution of this thesis is to propose Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. Operationally, at each layer, it consists of a peer-to-peer architecture of modules that, by interacting with each other, provide a global view of the resource availability in a large, dynamic and heterogeneous distributed environment. The proposed resource discovery model provides the adaptability and flexibility to perform complex querying by supporting a set of significant querying features (such as multi-dimensional, range and aggregate querying) while supporting exact and partial matching, both for static and dynamic object contents. The simulation shows that HARD can be applied to arbitrary scales of dynamicity, both in terms of complexity and of scale, positioning this proposal as a proper architecture for future many-core systems. We also contributed to propose a novel resource management scheme for future systems which efficiently can utilize distributed resources in a fully decentralized fashion. Moreover, leveraging discovery components (RR-RPs) enables our resource management platform to dynamically find and allocate available resources that guarantee the QoS parameters on demand

Thermalization and Information Scrambling in Phases of Quantum Matter

Understanding how quantum matter behaves when driven out of equilibrium is one of the key focuses in quantum physics. Thanks to impressive progress in the control and precision achieved in quantum synthetic matter over the past decades, the nonequilibrium quantum many-body physics has become one of the most active research areas today, especially after the experimental realization of Bose-Einstein condensates and optical lattices, which allows us to directly observe and study nonequilibrium quantum matter with great accuracy and controllability. In this dissertation, I explore the rich landscape of nonequilibrium quantum many-body physics and how quantum phase transitions, both symmetry-breaking and topological, can be extended to the nonequilibrium setting. In the first part of the dissertation, I focus on spinor Bose-Einstein condensates as an isolated quantum many-body system, and reveal their various dynamical behaviors, including quantum collapse and revivals, thermalization and nonthermal equilibration with no revival even though the system has finite degrees of freedom. In contrast to typical integrable systems, which usually do not thermalize, we find that spinor condensates have a parameter range in which the system thermalizes via the Eigenstate Thermalization Hypothesis (ETH). We show that this observation is linked to the presence of rare nonthermal states whose fraction vanishes with system size, and contributes to the notion of thermalization via weak ETH. Next, I explore a dynamical process that is complementary to thermalization in isolated quantum systems: information scrambling, which could be probed via out-of-time-order correlators (OTOC). I propose a nonintegrable, disordered and quasi-1D spin model, the ladder-XX model, for a feasible detection of information scrambling in a cold atom simulator. This chapter poses a fundamental question: "What are the signatures of quantum phases and phase transitions in isolated interacting systems driven out-of-equilibrium?" I study the ladder-XX model in both clean and disordered potentials, and characterize different nonequilibrium phases, i.e., ergodic and many-body localized, of the model based on the decay properties of OTOCs. Emergent light cone shows sublinear behavior, while the butterfly cones drastically differ from the light cone by demonstrating superlinear spread of information with a velocity that is bounded by the light cone velocity. In the second part of the dissertation, I continue to search for answers to the question posed above, however this time with a particular focus on symmetry-breaking and topological quantum phase transitions. I pin down a universal mechanism underlying the relation between information scrambling at any temperature and quantum phases at low temperatures. Our method points to key ingredients to dynamically detect long-range order in gapped phases through OTOCs for symmetry-breaking quantum phase transitions and Z2 topological order associated with Majorana zero modes localized at the edges. Our results pave the way to an intriguing observation that phases of quantum matter could protect the information from scrambling and thermalization, even when the system is interacting and nonintegrable. Finally, I explore and propose utilizing short-time transient temporal regimes and single-site probes to detect the phases and phase transitions in quantum matter. These studies reveal a dynamical crossover and a dynamical phase transition, respectively for periodic and open-boundary chains. In both cases, a nonequilibrium scaling law appears in the vicinity of the crossover/transition with associated exponents that differ from the analytical predictions for long times. Feasibility of detecting such dynamical criticality in experimental systems are discussed.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170042/1/cbdag_1.pd

Non-Integrable Dynamics in a Trapped-Ion Quantum Simulator

From the first demonstration of a quantum logic gate in 1995 to the actualizationof a “quantum advantage” over classical technology a few years ago, the field of quantum information has made remarkable progress during my lifetime. Multiple quantum technology platforms have developed to the point that companies and governments are investing heavily in the industry. A primary focus is the development of fault-tolerant error correction, a technology expected to be necessary for large-scale digital quantum computers. Meanwhile analog quantum simulators, a subclass of quantum computers that apply unitary evolutions instead of digitized gates, are at the forefront of controllable quantum system sizes. In place of algorithms, analog quantum simulators are naturally suited to study many-body physics and model certain materials and transport phenomena. In this thesis I discuss an analog quantum simulator based on trapped +Yb171 ions and its use for studying dynamics and thermalizing properties of the non-integrable long-range Ising model with system sizes near the limit of classical tractability. In addition to the technical properties of the simulator, I present three select experiments that I worked on during my PhD. The first is an observation of a phenomenon in nonequilibrium physics, a dynamical phase transition (DPT). While equilibrium phase transitions follow robust universal principles, DPTs are challenging to describe with conventional thermodynamics. We present an experimental observation and characterization of a DPT with up to 53 qubits. We also explore the system’s ability to simulate physics beyond its own by implementing a quasiparticle confinement Hamiltonian. Here we see that the natural long-range interactions present in the simulator induce an effective confining potential on pairs of domain-wall quasiparticles, which behave similarly to quarks bound into mesons. We measure post-quench dynamics to identify how confinement introduces low-energy bound states and inhibits thermalization. Lastly, we use the individual-addressing capabilities of our simulator to implement Stark many-body localization (MBL) with a linear potential gradient. Stark MBL provides a novel, disorder-free method for localizing a quantum system that would otherwise thermalize under evolution. We explore how the localized phase depends on the gradient strength and uncover the presence of correlations using interferrometric double electron-electron resonance (DEER) measurements. These experiments show the capability of our experiment to study complex quantum dynamics in systems near 50 qubits and above