Performance-aware scheduling of parallel applications on non-dedicated clusters

This work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform's compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.This work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the grant TIN2016-79637-P "Towards Unification of HPC and Big Data Paradigms"; and the European Union's Horizon 2020 research and innovation program under Grant No. 801091, project "Exascale programming models for extreme data processing" (ASPIDE)

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

Performance modeling and optimization techniques for heterogeneous computing

Since Graphics Processing Units (CPUs) have increasingly gained popularity amoung non-graphic and computational applications, known as General-Purpose computation on GPU (GPGPU), CPUs have been deployed in many clusters, including the world\u27s fastest supercomputer. However, to make the most efficiency from a GPU system, one should consider both performance and reliability of the system. This dissertation makes four major contributions. First, the two-level checkpoint/restart protocol that aims to reduce the checkpoint and recovery costs with a latency hiding strategy in a system between a CPU (Central Processing Unit) and a GPU is proposed. The experimental results and analysis reveals some benefits, especially in a long-running application. Second, a performance model for estimating GPGPU execution time is proposed. This performance model improves operation cost estimation over existing ones by considering varied memory latencies. The proposed model also considers the effects of thread synchronization functions. In addition, the impacts of various issues in GPGPU programming such as bank conflicts in shared memory and branch divergence are also discussed. Third, the interplay between GPGPU application performance and system reliability of a large GPU system is explored. This includes a checkpoint scheduling model for a certain GPGPU application. The effects of a checkpoint/restart mechanism on the application performance is also discussed. Finally, optimization techniques to remedy uncoalesced memory access in GPU\u27s global memory are proposed. These techniques are memory rearrangement using 2-dimensional matrix transpose and 3-dimensional matrix permutation. The analytical results show that the proposed technique can reduce memory access time, especially when the transformed array/matrix is frequently accessed

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

Banking theory based distributed resource management and scheduling for hybrid cloud computing

Cloud computing is a computing model in which the network offers a dynamically scalable service based on virtualized resources. The resources in the cloud environment are heterogeneous and geographically distributed. The user does not need to know how to manage those who support the cloud computing infrastructure. From the view of cloud computing, all hardware, software and networks are resources. All of the resources are dynamically scalable on demand. It can offer a complete service for the user even when these service resources are geographically distributed. The user pays for only what they use (pay-per-use). Meanwhile, the transaction environment will decide how to manage resource usage and cost, because all of the transactions have to follow the rule of the market. How to manage and schedule resources effectively becomes a very important part of cloud computing, and how to setup a new framework to offer a reliable, safe and executable service are very important issues. The approach herein is a new contribution to cloud computing. It not only proposes a hybrid cloud computing model based on banking theory to manage transactions among all participants in the hybrid cloud computing environment, but also proposes a "Cloud Bank" framework to support all the related issues. There are some of technology and theory been used to offer contributions as below: 1. This thesis presents an Optimal Deposit-loan Ratio Theory to adjust the pricing between the resource provider and resource consumer to realize both benefit maximization and cloud service optimization for all participants. 2. It also offers a new pricing schema using Centralized Synchronous Algorithm and Distributed Price Adjustment Algorithm to control all lifecycles and dynamically price all resources. 3. Normally, commercial banks apply four factors mitigation and to predict the risk: Probability of Default, Loss Given Default, Exposure at Default and Maturity. This thesis applies Probability of Default model of credit risk to forecast the safety supply of the resource. The Logistic Regression Model been used to control some factors in resource allocation. At the same time, the thesis uses Multivariate Statistical analysis to predict risk. 4. The Cloud Bank model applies an improved Pareto Optimality Algorithm to build its own scheduling system. 5. In order to archive the above purpose, this thesis proposes a new QoS-based SLA-CBSAL to describe all the physical resource and the processing of thread. In order to support all the related algorithms and theories, the thesis uses the CloudSim simulation tools give a test result to support some of the Cloud Bank management strategies and algorithms. The experiment shows us that the Cloud Bank Model is a new possible solution for hybrid cloud computing. For future research direction, the author will focus on building real hybrid cloud computing and simulate actual user behaviour in a real environment, and continue to modify and improve the feasibility and effectiveness of the project. For the risk mitigation and prediction, the risks can be divided into the four categories: credit risk, liquidity risk, operational risk, and other risks. Although this thesis uses credit risk and liquidity risk research, in a real trading environment operational risks and other risks exist. Only through improvements to the designation of all risk types of analysis and strategy can our Cloud Bank be considered relatively complete

A Meta Analysis of Critical Success Factors for Computer Technology Projects

Project managers are continually in pursuit of a magic formula that will deliver a successful project. Companies arc interested in formulas for project success because billions of dollars arc wasted each year, in U.S.A. Information Technology projects alone. through poorly managed or failed projects. Failing projects arc not only bad for the individual project team members, but a failed project\u27s impacts emanate outward to cause companies to decline or die. In addition, the targeted customers who receive inferior products, late products, or no products at all (due to project failures) are significantly impacted as well. Because failed computer technology projects arc not isolated to the U.S.A., ultimately the entire world economy would be benefited by the identification of reliable critical success factors. This study investigates, through a meta analysis of current research and literature, the existence of Critical Success Factors (CSFs) that influence the ability to deliver successful projects. Twenty-nine literature sources, from three different categories of literature (Researchers, Orgarizations, and Experts) were used as input to the analysis. The results obtained from the analysis yielded seventeen CSFs which contribute to the success of a project. Project management Clear goals and objectives Project team competence Requirements management User & stakeholder involvement Senior management support Organization change management Architecture and design 360 degree communication Quality management Iterative & incremental development Product development life cycle Interdepartmental cooperation Expectation management Individual and team attitudes Risk management Vendor managemen

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

Big Data Processing with Peer-To-Peer Architectures

Ph.DDOCTOR OF PHILOSOPH