2 research outputs found
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 tecnolog铆as de la informaci贸n (IT) se encuentra en transici贸n
a una nueva generaci贸n de aplicaciones que requieren sistemas de adquisici贸n de datos,
procesamiento y almacenamiento cada vez m谩s intensivo. Como resultado de ese cambio
hacia la computaci贸n intensiva de datos, existe una superposici贸n, cada vez mayor, entre
la computaci贸n de alto rendimiento (HPC) y las t茅cnicas Big Data en las aplicaciones,
pues muchas aplicaciones HPC producen grandes vol煤menes de datos, y Big Data necesita
capacidades HPC.
La hip贸tesis 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
unificaci贸n de ambos para hacer frente a un amplio espectro de problemas de investigaci贸n.
Por lo tanto, el objetivo principal de esta tesis es la propuesta y desarrollo de un sistema
de monitorizaci贸n que facilite la convergencia de los paradigmas HPC y Big Data gracias
a la provisi贸n de datos sobre el comportamiento de las aplicaciones en un entorno en
el que se pueden ejecutar aplicaciones de ambos mundos, ofreciendo informaci贸n 煤til
para mejorar la escalabilidad, la explotaci贸n 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 dise帽o de mecanismos de monitorizaci贸n y localizaci贸n de recursos para explotar el
paralelismo en todos los niveles de la pila del software. El resultado es un framework
de monitorizaci贸n en dos niveles (tanto a nivel de nodo como de aplicaci贸n) con una
baja carga computacional, escalable, y que se puede comunicar con distintos m贸dulos
gracias a una API proporcionada para tal objetivo. Todos datos recolectados se difunden
para facilitar la realizaci贸n de mejoras de manera global en todo el sistema, y as铆 evitar
desajustes entre capas, lo que combinado con las t茅cnicas 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 migraci贸n de las mismas entre nodos, adem谩s
de la posibilidad de incrementar o disminuir su n煤mero de procesos de forma din谩mica.
Todo ello gracias a la cooperaci贸n con otros m贸dulos que se integran en LIMITLESS, y
cuyo objetivo es optimizar la ejecuci贸n de una pila de aplicaciones en base a pol铆ticas
multicriterio. Esta funcionalidad se llama planificaci贸n de grano grueso.
Para un mejor desempe帽o y con el objetivo de reducir m谩s a煤n la carga durante la
ejecuci贸n, 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 p茅rdida
de informaci贸n. Para lograr este objetivo se ha hecho uso de t茅cnicas de filtrado de datos,
algoritmos de Machine Learning (ML), y Redes Neuronales (NN).
Finalmente, para obtener mejores resultados en la planificaci贸n de aplicaciones y
para dise帽ar nuevas pol铆ticas de planificaci贸n multi-criterio, los datos de monitorizaci贸n recolectados han sido combinados con nuevos algoritmos de ML para identificar (por
medio de algoritmos de clasificaci贸n) aplicaciones y sus fases de ejecuci贸n. Todo ello
realizando tareas de profiling offline. Gracias a estas t茅cnicas, LIMITLESS puede detectar
en qu茅 fase de su ejecuci贸n se encuentra una determinada aplicaci贸n e intentar compartir los
recursos de computacionales con otras aplicaciones que sean compatibles (no se produce
una degradaci贸n del rendimiento entre ellas cuando ambas se ejecutan a la vez en el mismo
nodo). Esta funcionalidad se llama planificaci贸n de grano fino y puede reducir el tiempo
total de ejecuci贸n de la pila de aplicaciones en los casos de uso porque realiza un uso m谩s
eficiente de los recursos de las m谩quinas.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 Tecnolog铆a Inform谩tica por la Universidad Carlos III de MadridPresidente: F茅lix Garc铆a Carballeira.- Secretario: Pedro 脕ngel Cuenca Castillo.- Vocal: Mar铆a Cristina V. Marinesc