Fair, responsive scheduling of engineering workflows on computing grids

This thesis considers scheduling in the context of a grid computing system used in engineering design. Users desire responsiveness and fairness in the treatment of the workflows they submit. Submissions outstrip the available computing capacity during the work day, and the queue is only caught up on overnight and at weekends. The execution times observed span a wide range of 10^0 to 10^7 core-minutes. The Projected Schedule Length Ratio (P-SLR) list scheduling policy is designed to use execution time estimates and the structure of the dependency graph to improve on the existing industrial FairShare policy. P-SLR aims to minimise the worst-case SLR of jobs and keep SLR fair across the space of job execution times. P-SLR is shown to equal or surpass all other evaluated policies in responsiveness and fairness across the spectra of load and networking delays. P-SLR is also dominant where execution time estimates are within an order of magnitude of the real value. Such estimates are considered achievable using user knowledge or automated profiling. Outside this range, the Shortest Remaining Time First (SRTF) policy achieved better responsiveness and fairness. The Projected Value Remaining (PVR) policy considers the case where a curve specifying the value of a job over time is given. PVR aims to maximise total workload value, even under overload, by maximising the worst-case job value in a workload. PVR is shown to be dominant across the load and networking spectra. Where execution time estimates are coarser than the nearest power of 2, SRTF delivers higher value than PVR. SRTF is also shown to have responsiveness, fairness and value close behind P-SLR and PVR throughout the range of load and network delays considered. However, the kinds of starvation under overload incurred by SRTF would almost certainly be undesirable if implemented in a production system

Supercomputer Emulation For Evaluating Scheduling Algorithms

Scheduling algorithms have a significant impact on the optimal utilization of HPC facilities, yet the vast majority of the research in this area is done using simulations. In working with simulations, a great deal of factors that affect a real scheduler, such as its scheduling processing time, communication latencies and the scheduler intrinsic implementation complexity are not considered. As a result, despite theoretical improvements reported in several articles, practically no new algorithms proposed have been implemented in real schedulers, with HPC facilities still using the basic first-come-first-served (FCFS) with Backfill policy scheduling algorithm. A better approach could be, therefore, the use of real schedulers in an emulation environment to evaluate new algorithms. This thesis investigates two related challenges in emulations: computational cost and faithfulness of the results to real scheduling environments. It finds that the sampling, shrinking and shuffling of a trace must be done carefully to keep the classical metrics invariant or linear variant in relation to size and times of the original workload. This is accomplished by the careful control of the submission period and the consideration of drifts in the submission period and trace duration. This methodology can help researchers to better evaluate their scheduling algorithms and help HPC administrators to optimize the parameters of production schedulers. In order to assess the proposed methodology, we evaluated both the FCFS with Backfill and Suspend/Resume scheduling algorithms. The results strongly suggest that Suspend/Resume leads to a better utilization of a supercomputer when high priorities are given to big jobs

Cost-effective resource management for distributed computing

Current distributed computing and resource management infrastructures (e.g., Cluster and Grid) suffer from a wide variety of problems related to resource management, which include scalability bottleneck, resource allocation delay, limited quality-of-service (QoS) support, and lack of cost-aware and service level agreement (SLA) mechanisms. This thesis addresses these issues by presenting a cost-effective resource management solution which introduces the possibility of managing geographically distributed resources in resource units that are under the control of a Virtual Authority (VA). A VA is a collection of resources controlled, but not necessarily owned, by a group of users or an authority representing a group of users. It leverages the fact that different resources in disparate locations will have varying usage levels. By creating smaller divisions of resources called VAs, users would be given the opportunity to choose between a variety of cost models, and each VA could rent resources from resource providers when necessary, or could potentially rent out its own resources when underloaded. The resource management is simplified since the user and owner of a resource recognize only the VA because all permissions and charges are associated directly with the VA. The VA is controlled by a ’rental’ policy which is supported by a pool of resources that the system may rent from external resource providers. As far as scheduling is concerned, the VA is independent from competitors and can instead concentrate on managing its own resources. As a result, the VA offers scalable resource management with minimal infrastructure and operating costs. We demonstrate the feasibility of the VA through both a practical implementation of the prototype system and an illustration of its quantitative advantages through the use of extensive simulations. First, the VA concept is demonstrated through a practical implementation of the prototype system. Further, we perform a cost-benefit analysis of current distributed resource infrastructures to demonstrate the potential cost benefit of such a VA system. We then propose a costing model for evaluating the cost effectiveness of the VA approach by using an economic approach that captures revenues generated from applications and expenses incurred from renting resources. Based on our costing methodology, we present rental policies that can potentially offer effective mechanisms for running distributed and parallel applications without a heavy upfront investment and without the cost of maintaining idle resources. By using real workload trace data, we test the effectiveness of our proposed rental approaches. Finally, we propose an extension to the VA framework that promotes long-term negotiations and rentals based on service level agreements or long-term contracts. Based on the extended framework, we present new SLA-aware policies and evaluate them using real workload traces to demonstrate their effectiveness in improving rental decisions

Distributed Late-binding Micro-scheduling and Data Caching for Data-Intensive Workflows

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 06-07-2015El mundo de hoy en día se encuentra inundado por ingentes cantidades de información digital procedente de muy diversas fuentes. Todo apunta, además, a que esta tendencia se agudizará en el futuro. Ni la industria, ni la sociedad en general, ni, muy particularmente, la ciencia, permanecen indiferentes ante este hecho. Al contrario, se esfuerzan por obtener el máximo provecho de esta información, lo que significa que deben capturarla, transferirla, almacenarla y procesarla puntual y eficientemente, utilizando una amplia gama de recursos computacionales. Pero esta tarea no es siempre sencilla. Un ejemplo representativo de los desafíos que suponen el manejo y procesamiento de grandes cantidades de datos es el de los experimentos de física de partículas del Large Hadron Collider (LHC), en Ginebra, que cada año deben gestionar decenas de petabytes de información. Basándonos en la experiencia de una de estas colaboraciones, hemos estudiado los principales problemas relativos a la gestión de volúmenes de datos masivos y a la ejecución de vastos flujos de trabajo que necesitan consumirlos. En este contexto, hemos desarrollado una arquitectura de propósito general para la planificación y ejecución de flujos de trabajo con importantes requisitos de datos, que hemos llamado Task Queue. Este nuevo sistema aprovecha el modelo de asignación tardía basado en agentes que ha ayudado a los experimentos del LHC a superar los problemas asociados con la heterogeneidad y la complejidad de las grandes infraestructuras grid de computación. Nuestra propuesta presenta varias mejoras con respecto a los sistemas existentes. Los agentes de ejecución de la arquitectura Task Queue comparten una tabla hash distribuida (Distributed Hash Table, DHT) y realizan la asignación de tareas de una manera cooperativa. De esta forma, se evitan los problemas de escalabilidad de los algoritmos centralizados de asignación y se mejoran los tiempos de ejecución. Esta escalabilidad nos permite realizar una microplanificación de grano fino lo cual posibilita nuevas funcionalidades, como la implementación de una cache distribuida en los nodos de ejecución y el uso de la información de ubicación de los datos en las decisiones de asignación de tareas. Esto mejora la eficiencia del procesado de datos y ayuda a aliviar los habitualmente congestionados servicios de almacenamiento del grid. Además, nuestro sistema es más robusto frente a problemas en la interacción con la cola central de tareas y ofrece mejor comportamiento en situaciones con patrones de acceso a datos exigentes o en ausencia de servicios de almacenamiento locales. Todo esto ha sido demostrado en una amplia serie de pruebas de evaluación. Dado que nuestro procedimiento de planificación de tareas distribuido requiere el uso de mensajes de broadcast, también hemos realizado un profundo estudio de las posibles aproximaciones a la implementación de esta operación sobre el DHT Kademlia, el cual es utilizado para la cache de datos compartida. Kademlia ofrece enrutamiento a nodos individuales pero no incluye ninguna primitiva de broadcast. Nuestro trabajo expone las peculiaridades de este sistema, particularmente su métrica basada en la operación XOR, y estudia analíticamente qué técnicas de broadcast pueden ser usadas con él. También se ha desarrollado un modelo que estima la cobertura de nodos en función de la probabilidad que cada mensaje individual alcance su destino correctamente. Como validación, los algoritmos se han implementado y se han evaluado exhaustivamente. Además, proponemos varias técnicas para mejorar los protocolos en situaciones adversas, por ejemplo cuando el sistema presenta una alta rotación de nodos o la tasa de error en las entregas no es despreciable. Esta técnicas incluyen redundancia, reenvío e inundación (flooding), así como combinaciones de las mismas. Presentamos un análisis de las fortalezas y debilidades de los diferentes algoritmos y las mencionadas técnicas complementarias.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Intelligent Load Balancing in Cloud Computer Systems

Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments at the same time. Clouds are typically more cost-effective than single computers of comparable computing performance. The sheer physical size of the system itself means that thousands of machines may be involved. The focus of this research was to design a strategy to dynamically allocate tasks without overloading Cloud nodes which would result in system stability being maintained at minimum cost. This research has added the following new contributions to the state of knowledge: (i) a novel taxonomy and categorisation of three classes of schedulers, namely OS-level, Cluster and Big Data, which highlight their unique evolution and underline their different objectives; (ii) an abstract model of cloud resources utilisation is specified, including multiple types of resources and consideration of task migration costs; (iii) a virtual machine live migration was experimented with in order to create a formula which estimates the network traffic generated by this process; (iv) a high-fidelity Cloud workload simulator, based on a month-long workload traces from Google's computing cells, was created; (v) two possible approaches to resource management were proposed and examined in the practical part of the manuscript: the centralised metaheuristic load balancer and the decentralised agent-based system. The project involved extensive experiments run on the University of Westminster HPC cluster, and the promising results are presented together with detailed discussions and a conclusion

Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions

Gestão e engenharia de CAP na nuvem híbrida

Doutoramento em InformáticaThe evolution and maturation of Cloud Computing created an opportunity for the emergence of new Cloud applications. High-performance Computing, a complex problem solving class, arises as a new business consumer by taking advantage of the Cloud premises and leaving the expensive datacenter management and difficult grid development. Standing on an advanced maturing phase, today’s Cloud discarded many of its drawbacks, becoming more and more efficient and widespread. Performance enhancements, prices drops due to massification and customizable services on demand triggered an emphasized attention from other markets. HPC, regardless of being a very well established field, traditionally has a narrow frontier concerning its deployment and runs on dedicated datacenters or large grid computing. The problem with common placement is mainly the initial cost and the inability to fully use resources which not all research labs can afford. The main objective of this work was to investigate new technical solutions to allow the deployment of HPC applications on the Cloud, with particular emphasis on the private on-premise resources – the lower end of the chain which reduces costs. The work includes many experiments and analysis to identify obstacles and technology limitations. The feasibility of the objective was tested with new modeling, architecture and several applications migration. The final application integrates a simplified incorporation of both public and private Cloud resources, as well as HPC applications scheduling, deployment and management. It uses a well-defined user role strategy, based on federated authentication and a seamless procedure to daily usage with balanced low cost and performance.O desenvolvimento e maturação da Computação em Nuvem abriu a janela de oportunidade para o surgimento de novas aplicações na Nuvem. A Computação de Alta Performance, uma classe dedicada à resolução de problemas complexos, surge como um novo consumidor no Mercado ao aproveitar as vantagens inerentes à Nuvem e deixando o dispendioso centro de computação tradicional e o difícil desenvolvimento em grelha. Situando-se num avançado estado de maturação, a Nuvem de hoje deixou para trás muitas das suas limitações, tornando-se cada vez mais eficiente e disseminada. Melhoramentos de performance, baixa de preços devido à massificação e serviços personalizados a pedido despoletaram uma atenção inusitada de outros mercados. A CAP, independentemente de ser uma área extremamente bem estabelecida, tradicionalmente tem uma fronteira estreita em relação à sua implementação. É executada em centros de computação dedicados ou computação em grelha de larga escala. O maior problema com o tipo de instalação habitual é o custo inicial e o não aproveitamento dos recursos a tempo inteiro, fator que nem todos os laboratórios de investigação conseguem suportar. O objetivo principal deste trabalho foi investigar novas soluções técnicas para permitir o lançamento de aplicações CAP na Nuvem, com particular ênfase nos recursos privados existentes, a parte peculiar e final da cadeia onde se pode reduzir custos. O trabalho inclui várias experiências e análises para identificar obstáculos e limitações tecnológicas. A viabilidade e praticabilidade do objetivo foi testada com inovação em modelos, arquitetura e migração de várias aplicações. A aplicação final integra uma agregação de recursos de Nuvens, públicas e privadas, assim como escalonamento, lançamento e gestão de aplicações CAP. É usada uma estratégia de perfil de utilizador baseada em autenticação federada, assim como procedimentos transparentes para a utilização diária com um equilibrado custo e performance

Contributions to High-Throughput Computing Based on the Peer-to-Peer Paradigm

XII, 116 p.This dissertation focuses on High Throughput Computing (HTC) systems and how to build a working HTC system using Peer-to-Peer (P2P) technologies. The traditional HTC systems, designed to process the largest possible number of tasks per unit of time, revolve around a central node that implements a queue used to store and manage submitted tasks. This central node limits the scalability and fault tolerance of the HTC system. A usual solution involves the utilization of replicas of the master node that can replace it. This solution is, however, limited by the number of replicas used. In this thesis, we propose an alternative solution that follows the P2P philosophy: a completely distributed system in which all worker nodes participate in the scheduling tasks, and with a physically distributed task queue implemented on top of a P2P storage system. The fault tolerance and scalability of this proposal is, therefore, limited only by the number of nodes in the system. The proper operation and scalability of our proposal have been validated through experimentation with a real system. The data availability provided by Cassandra, the P2P data management framework used in our proposal, is analysed by means of several stochastic models. These models can be used to make predictions about the availability of any Cassandra deployment, as well as to select the best possible con guration of any Cassandra system. In order to validate the proposed models, an experimentation with real Cassandra clusters is made, showing that our models are good descriptors of Cassandra's availability. Finally, we propose a set of scheduling policies that try to solve a common problem of HTC systems: re-execution of tasks due to a failure in the node where the task was running, without additional resource misspending. In order to reduce the number of re-executions, our proposals try to nd good ts between the reliability of nodes and the estimated length of each task. An extensive simulation-based experimentation shows that our policies are capable of reducing the number of re-executions, improving system performance and utilization of nodes