41 research outputs found
New cross-layer techniques for multi-criteria scheduling in large-scale systems
The global ecosystem of information technology (IT) is in transition to a new generation
of applications that require more and more intensive data acquisition, processing and
storage systems. As a result of that change towards data intensive computing, there is a
growing overlap between high performance computing (HPC) and Big Data techniques in
applications, since many HPC applications produce large volumes of data, and Big Data
needs HPC capabilities.
The hypothesis of this PhD. thesis is that the potential interoperability and convergence
of the HPC and Big Data systems are crucial for the future, being essential the unification
of both paradigms to address a broad spectrum of research domains. For this reason, the
main objective of this Phd. thesis is purposing and developing a monitoring system to
allow the HPC and Big Data convergence, thanks to giving information about behaviors of
applications in a system which execute both kind of them, giving information to improve
scalability, data locality, and to allow adaptability to large scale computers. To achieve
this goal, this work is focused on the design of resource monitoring and discovery to
exploit parallelism at all levels. These collected data are disseminated to facilitate global
improvements at the whole system, and, thus, avoid mismatches between layers. The
result is a two-level monitoring framework (both at node and application level) with
a low computational load, scalable, and that can communicate with different modules
thanks to an API provided for this purpose. All data collected is disseminated to facilitate
the implementation of improvements globally throughout the system, and thus avoid
mismatches between layers, which combined with the techniques applied to deal with fault
tolerance, makes the system robust and with high availability.
On the other hand, the developed framework includes a task scheduler capable of managing
the launch of applications, their migration between nodes, as well as the possibility
of dynamically increasing or decreasing the number of processes. All these thanks to the
cooperation with other modules that are integrated into LIMITLESS, and whose objective
is to optimize the execution of a stack of applications based on multi-criteria policies. This
scheduling mode is called coarse-grain scheduling based on monitoring.
For better performance and in order to further reduce the overhead during the monitorization,
different optimizations have been applied at different levels to try to reduce
communications between components, while trying to avoid the loss of information. To
achieve this objective, data filtering techniques, Machine Learning (ML) algorithms, and
Neural Networks (NN) have been used.
In order to improve the scheduling process and to design new multi-criteria scheduling
policies, the monitoring information has been combined with other ML algorithms to
identify (through classification algorithms) the applications and their execution phases,
doing offline profiling. Thanks to this feature, LIMITLESS can detect which phase is executing an application and tries to share the computational resources with other applications
that are compatible (there is no performance degradation between them when both are
running at the same time). This feature is called fine-grain scheduling, and can reduce the
makespan of the use cases while makes efficient use of the computational resources that
other applications do not use.El ecosistema global de las tecnologías de la información (IT) se encuentra en transición
a una nueva generación de aplicaciones que requieren sistemas de adquisición de datos,
procesamiento y almacenamiento cada vez más intensivo. Como resultado de ese cambio
hacia la computación intensiva de datos, existe una superposición, cada vez mayor, entre
la computación de alto rendimiento (HPC) y las técnicas Big Data en las aplicaciones,
pues muchas aplicaciones HPC producen grandes volúmenes de datos, y Big Data necesita
capacidades HPC.
La hipótesis de esta tesis es que hay un gran potencial en la interoperabilidad y
convergencia de los sistemas HPC y Big Data, siendo crucial para el futuro tratar una
unificación de ambos para hacer frente a un amplio espectro de problemas de investigación.
Por lo tanto, el objetivo principal de esta tesis es la propuesta y desarrollo de un sistema
de monitorización que facilite la convergencia de los paradigmas HPC y Big Data gracias
a la provisión de datos sobre el comportamiento de las aplicaciones en un entorno en
el que se pueden ejecutar aplicaciones de ambos mundos, ofreciendo información útil
para mejorar la escalabilidad, la explotación de la localidad de datos y la adaptabilidad
en los computadores de gran escala. Para lograr este objetivo, el foco se ha centrado en
el diseño de mecanismos de monitorización y localización de recursos para explotar el
paralelismo en todos los niveles de la pila del software. El resultado es un framework
de monitorización en dos niveles (tanto a nivel de nodo como de aplicación) con una
baja carga computacional, escalable, y que se puede comunicar con distintos módulos
gracias a una API proporcionada para tal objetivo. Todos datos recolectados se difunden
para facilitar la realización de mejoras de manera global en todo el sistema, y así evitar
desajustes entre capas, lo que combinado con las técnicas aplicadas para lidiar con la
tolerancia a fallos, hace que el sistema sea robusto y con una alta disponibilidad.
Por otro lado, el framework desarrollado incluye un planificador de tareas capaz de
gestionar el lanzamiento de aplicaciones, la migración de las mismas entre nodos, además
de la posibilidad de incrementar o disminuir su número de procesos de forma dinámica.
Todo ello gracias a la cooperación con otros módulos que se integran en LIMITLESS, y
cuyo objetivo es optimizar la ejecución de una pila de aplicaciones en base a políticas
multicriterio. Esta funcionalidad se llama planificación de grano grueso.
Para un mejor desempeño y con el objetivo de reducir más aún la carga durante la
ejecución, se han aplicado distintas optimizaciones en distintos niveles para tratar de
reducir las comunicaciones entre componentes, a la vez que se trata de evitar la pérdida
de información. Para lograr este objetivo se ha hecho uso de técnicas de filtrado de datos,
algoritmos de Machine Learning (ML), y Redes Neuronales (NN).
Finalmente, para obtener mejores resultados en la planificación de aplicaciones y
para diseñar nuevas políticas de planificación multi-criterio, los datos de monitorización recolectados han sido combinados con nuevos algoritmos de ML para identificar (por
medio de algoritmos de clasificación) aplicaciones y sus fases de ejecución. Todo ello
realizando tareas de profiling offline. Gracias a estas técnicas, LIMITLESS puede detectar
en qué fase de su ejecución se encuentra una determinada aplicación e intentar compartir los
recursos de computacionales con otras aplicaciones que sean compatibles (no se produce
una degradación del rendimiento entre ellas cuando ambas se ejecutan a la vez en el mismo
nodo). Esta funcionalidad se llama planificación de grano fino y puede reducir el tiempo
total de ejecución de la pila de aplicaciones en los casos de uso porque realiza un uso más
eficiente de los recursos de las máquinas.This PhD dissertation has been partially supported by the Spanish Ministry of Science and Innovation under an FPI fellowship associated to a National Project with reference TIN2016-79637-P (from July 1,
2018 to October 10, 2021)Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Félix García Carballeira.- Secretario: Pedro Ángel Cuenca Castillo.- Vocal: María Cristina V. Marinesc
DynamicISP: Dynamically Controlled Image Signal Processor for Image Recognition
Image signal processor (ISP) plays an important role not only for human
perceptual quality but also for computer vision. In most cases, experts resort
to manual tuning of many parameters in the ISPs for perceptual quality. It
failed in sub-optimal, especially for computer vision. Aiming to improve ISPs,
two approaches have been actively proposed; tuning the parameters with machine
learning, or constructing an ISP with DNN. The former is lightweight but lacks
expressive powers. The latter has expressive powers but it was too heavy to
calculate on edge devices. To this end, we propose DynamicISP, which consists
of traditional simple ISP functions but their parameters are controlled
dynamically per image according to what the downstream image recognition model
felt to the previous frame. Our proposed method successfully controlled
parameters of multiple ISP functions and got state-of-the-art accuracy with a
small computational cost
Doctor of Philosophy
dissertationA modern software system is a composition of parts that are themselves highly complex: operating systems, middleware, libraries, servers, and so on. In principle, compositionality of interfaces means that we can understand any given module independently of the internal workings of other parts. In practice, however, abstractions are leaky, and with every generation, modern software systems grow in complexity. Traditional ways of understanding failures, explaining anomalous executions, and analyzing performance are reaching their limits in the face of emergent behavior, unrepeatability, cross-component execution, software aging, and adversarial changes to the system at run time. Deterministic systems analysis has a potential to change the way we analyze and debug software systems. Recorded once, the execution of the system becomes an independent artifact, which can be analyzed offline. The availability of the complete system state, the guaranteed behavior of re-execution, and the absence of limitations on the run-time complexity of analysis collectively enable the deep, iterative, and automatic exploration of the dynamic properties of the system. This work creates a foundation for making deterministic replay a ubiquitous system analysis tool. It defines design and engineering principles for building fast and practical replay machines capable of capturing complete execution of the entire operating system with an overhead of several percents, on a realistic workload, and with minimal installation costs. To enable an intuitive interface of constructing replay analysis tools, this work implements a powerful virtual machine introspection layer that enables an analysis algorithm to be programmed against the state of the recorded system through familiar terms of source-level variable and type names. To support performance analysis, the replay engine provides a faithful performance model of the original execution during replay
Complementary Layered Learning
Layered learning is a machine learning paradigm used to develop autonomous robotic-based agents by decomposing a complex task into simpler subtasks and learns each sequentially. Although the paradigm continues to have success in multiple domains, performance can be unexpectedly unsatisfactory. Using Boolean-logic problems and autonomous agent navigation, we show poor performance is due to the learner forgetting how to perform earlier learned subtasks too quickly (favoring plasticity) or having difficulty learning new things (favoring stability). We demonstrate that this imbalance can hinder learning so that task performance is no better than that of a suboptimal learning technique, monolithic learning, which does not use decomposition. Through the resulting analyses, we have identified factors that can lead to imbalance and their negative effects, providing a deeper understanding of stability and plasticity in decomposition-based approaches, such as layered learning. To combat the negative effects of the imbalance, a complementary learning system is applied to layered learning. The new technique augments the original learning approach with dual storage region policies to preserve useful information from being removed from an agent’s policy prematurely. Through multi-agent experiments, a 28% task performance increase is obtained with the proposed augmentations over the original technique
Energy-Efficient Technologies for High-Performance Manufacturing Industries
Ph.DDOCTOR OF PHILOSOPH
The Relationship between Nonprofit Organizations and Cloud Adoption Concerns
Many leaders of nonprofit organizations (NPOs) in the United States do not have plans to adopt cloud computing. However, the factors accounting for their decisions is not known. This correlational study used the extended unified theory of acceptance and use of technology (UTAUT2) to examine whether performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can predict behavioral intention (BI) and use behavior (UB) of NPO information technology (IT) managers towards adopting cloud computing within the Phoenix metropolitan area of Arizona of the U.S. An existing UTAUT2 survey instrument was used with a sample of IT managers (N = 106) from NPOs. A multiple regression analysis confirmed a positive statistically significant relationship between predictors and the dependent variables of BI and UB. The first model significantly predicted BI, F (7,99) =54.239, p -?¤ .001, R^2=.795. Performance expectancy (β = .295, p = .004), social influence (β = .148, p = .033), facilitating conditions (β = .246, p = .007), and habit (β = .245, p = .002) were statistically significant predictors of BI at the .05 level. The second model significantly predicted UB, F (3,103) = 37.845, p -?¤ .001, R^2 = .527. Habit (β = .430, p = .001) was a statistically significant predictor for UB at a .05 level. Using the study results, NPO IT managers may be able to develop strategies to improve the adoption of cloud computing within their organization. The implication for positive social change is that, by using the study results, NPO leaders may be able to improve their IT infrastructure and services for those in need, while also reducing their organization\u27s carbon footprint through use of shared data centers for processing
Satellite Networks: Architectures, Applications, and Technologies
Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled
NASA Aircraft Controls Research, 1983
The workshop consisted of 24 technical presentations on various aspects of aircraft controls, ranging from the theoretical development of control laws to the evaluation of new controls technology in flight test vehicles. A special report on the status of foreign aircraft technology and a panel session with seven representatives from organizations which use aircraft controls technology were also included. The controls research needs and opportunities for the future as well as the role envisioned for NASA in that research were addressed. Input from the panel and response to the workshop presentations will be used by NASA in developing future programs