Evaluation and optimization of Big Data Processing on High Performance Computing Systems

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] Hoxe en día, moitas organizacións empregan tecnoloxías Big Data para extraer información de grandes volumes de datos. A medida que o tamaño destes volumes crece, satisfacer as demandas de rendemento das aplicacións de procesamento de datos masivos faise máis difícil. Esta Tese céntrase en avaliar e optimizar estas aplicacións, presentando dúas novas ferramentas chamadas BDEv e Flame-MR. Por unha banda, BDEv analiza o comportamento de frameworks de procesamento Big Data como Hadoop, Spark e Flink, moi populares na actualidade. BDEv xestiona a súa configuración e despregamento, xerando os conxuntos de datos de entrada e executando cargas de traballo previamente elixidas polo usuario. Durante cada execución, BDEv extrae diversas métricas de avaliación que inclúen rendemento, uso de recursos, eficiencia enerxética e comportamento a nivel de microarquitectura. Doutra banda, Flame-MR permite optimizar o rendemento de aplicacións Hadoop MapReduce. En xeral, o seu deseño baséase nunha arquitectura dirixida por eventos capaz de mellorar a eficiencia dos recursos do sistema mediante o solapamento da computación coas comunicacións. Ademais de reducir o número de copias en memoria que presenta Hadoop, emprega algoritmos eficientes para ordenar e mesturar os datos. Flame-MR substitúe o motor de procesamento de datos MapReduce de xeito totalmente transparente, polo que non é necesario modificar o código de aplicacións xa existentes. A mellora de rendemento de Flame-MR foi avaliada de maneira exhaustiva en sistemas clúster e cloud, executando tanto benchmarks estándar coma aplicacións pertencentes a casos de uso reais. Os resultados amosan unha redución de entre un 40% e un 90% do tempo de execución das aplicacións. Esta Tese proporciona aos usuarios e desenvolvedores de Big Data dúas potentes ferramentas para analizar e comprender o comportamento de frameworks de procesamento de datos e reducir o tempo de execución das aplicacións sen necesidade de contar con coñecemento experto para elo.[Resumen] Hoy en día, muchas organizaciones utilizan tecnologías Big Data para extraer información de grandes volúmenes de datos. A medida que el tamaño de estos volúmenes crece, satisfacer las demandas de rendimiento de las aplicaciones de procesamiento de datos masivos se vuelve más difícil. Esta Tesis se centra en evaluar y optimizar estas aplicaciones, presentando dos nuevas herramientas llamadas BDEv y Flame-MR. Por un lado, BDEv analiza el comportamiento de frameworks de procesamiento Big Data como Hadoop, Spark y Flink, muy populares en la actualidad. BDEv gestiona su configuración y despliegue, generando los conjuntos de datos de entrada y ejecutando cargas de trabajo previamente elegidas por el usuario. Durante cada ejecución, BDEv extrae diversas métricas de evaluación que incluyen rendimiento, uso de recursos, eficiencia energética y comportamiento a nivel de microarquitectura. Por otro lado, Flame-MR permite optimizar el rendimiento de aplicaciones Hadoop MapReduce. En general, su diseño se basa en una arquitectura dirigida por eventos capaz de mejorar la eficiencia de los recursos del sistema mediante el solapamiento de la computación con las comunicaciones. Además de reducir el número de copias en memoria que presenta Hadoop, utiliza algoritmos eficientes para ordenar y mezclar los datos. Flame-MR reemplaza el motor de procesamiento de datos MapReduce de manera totalmente transparente, por lo que no se necesita modificar el código de aplicaciones ya existentes. La mejora de rendimiento de Flame-MR ha sido evaluada de manera exhaustiva en sistemas clúster y cloud, ejecutando tanto benchmarks estándar como aplicaciones pertenecientes a casos de uso reales. Los resultados muestran una reducción de entre un 40% y un 90% del tiempo de ejecución de las aplicaciones. Esta Tesis proporciona a los usuarios y desarrolladores de Big Data dos potentes herramientas para analizar y comprender el comportamiento de frameworks de procesamiento de datos y reducir el tiempo de ejecución de las aplicaciones sin necesidad de contar con conocimiento experto para ello.[Abstract] Nowadays, Big Data technologies are used by many organizations to extract valuable information from large-scale datasets. As the size of these datasets increases, meeting the huge performance requirements of data processing applications becomes more challenging. This Thesis focuses on evaluating and optimizing these applications by proposing two new tools, namely BDEv and Flame-MR. On the one hand, BDEv allows to thoroughly assess the behavior of widespread Big Data processing frameworks such as Hadoop, Spark and Flink. It manages the configuration and deployment of the frameworks, generating the input datasets and launching the workloads specified by the user. During each workload, it automatically extracts several evaluation metrics that include performance, resource utilization, energy efficiency and microarchitectural behavior. On the other hand, Flame-MR optimizes the performance of existing Hadoop MapReduce applications. Its overall design is based on an event-driven architecture that improves the efficiency of the system resources by pipelining data movements and computation. Moreover, it avoids redundant memory copies present in Hadoop, while also using efficient sort and merge algorithms for data processing. Flame-MR replaces the underlying MapReduce data processing engine in a transparent way and thus the source code of existing applications does not require to be modified. The performance benefits provided by Flame- MR have been thoroughly evaluated on cluster and cloud systems by using both standard benchmarks and real-world applications, showing reductions in execution time that range from 40% to 90%. This Thesis provides Big Data users with powerful tools to analyze and understand the behavior of data processing frameworks and reduce the execution time of the applications without requiring expert knowledge

Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Design and Evaluation of Low-Latency Communication Middleware on High Performance Computing Systems

[Resumen]El interés en Java para computación paralela está motivado por sus interesantes características, tales como su soporte multithread, portabilidad, facilidad de aprendizaje,alta productividad y el aumento significativo en su rendimiento omputacional. No obstante, las aplicaciones paralelas en Java carecen generalmente de mecanismos de comunicación eficientes, los cuales utilizan a menudo protocolos basados en sockets incapaces de obtener el máximo provecho de las redes de baja latencia, obstaculizando la adopción de Java en computación de altas prestaciones (High Per- formance Computing, HPC). Esta Tesis Doctoral presenta el diseño, implementación y evaluación de soluciones de comunicación en Java que superan esta limitación. En consecuencia, se desarrollaron múltiples dispositivos de comunicación a bajo nivel para paso de mensajes en Java (Message-Passing in Java, MPJ) que aprovechan al máximo el hardware de red subyacente mediante operaciones de acceso directo a memoria remota que proporcionan comunicaciones de baja latencia. También se incluye una biblioteca de paso de mensajes en Java totalmente funcional, FastMPJ, en la cual se integraron los dispositivos de comunicación. La evaluación experimental ha mostrado que las primitivas de comunicación de FastMPJ son competitivas en comparación con bibliotecas nativas, aumentando significativamente la escalabilidad de aplicaciones MPJ. Por otro lado, esta Tesis analiza el potencial de la computación en la nube (cloud computing) para HPC, donde el modelo de distribución de infraestructura como servicio (Infrastructure as a Service, IaaS) emerge como una alternativa viable a los sistemas HPC tradicionales. La evaluación del rendimiento de recursos cloud específicos para HPC del proveedor líder, Amazon EC2, ha puesto de manifiesto el impacto significativo que la virtualización impone en la red, impidiendo mover las aplicaciones intensivas en comunicaciones a la nube. La clave reside en un soporte de virtualización apropiado, como el acceso directo al hardware de red, junto con las directrices para la optimización del rendimiento sugeridas en esta Tesis.[Resumo]O interese en Java para computación paralela está motivado polas súas interesantes características, tales como o seu apoio multithread, portabilidade, facilidade de aprendizaxe, alta produtividade e o aumento signi cativo no seu rendemento computacional. No entanto, as aplicacións paralelas en Java carecen xeralmente de mecanismos de comunicación e cientes, os cales adoitan usar protocolos baseados en sockets que son incapaces de obter o máximo proveito das redes de baixa latencia, obstaculizando a adopción de Java na computación de altas prestacións (High Performance Computing, HPC). Esta Tese de Doutoramento presenta o deseño, implementaci ón e avaliación de solucións de comunicación en Java que superan esta limitación. En consecuencia, desenvolvéronse múltiples dispositivos de comunicación a baixo nivel para paso de mensaxes en Java (Message-Passing in Java, MPJ) que aproveitan ao máaximo o hardware de rede subxacente mediante operacións de acceso directo a memoria remota que proporcionan comunicacións de baixa latencia. Tamén se inclúe unha biblioteca de paso de mensaxes en Java totalmente funcional, FastMPJ, na cal foron integrados os dispositivos de comunicación. A avaliación experimental amosou que as primitivas de comunicación de FastMPJ son competitivas en comparación con bibliotecas nativas, aumentando signi cativamente a escalabilidade de aplicacións MPJ. Por outra banda, esta Tese analiza o potencial da computación na nube (cloud computing) para HPC, onde o modelo de distribución de infraestrutura como servizo (Infrastructure as a Service, IaaS) xorde como unha alternativa viable aos sistemas HPC tradicionais. A ampla avaliación do rendemento de recursos cloud específi cos para HPC do proveedor líder, Amazon EC2, puxo de manifesto o impacto signi ficativo que a virtualización impón na rede, impedindo mover as aplicacións intensivas en comunicacións á nube. A clave atópase no soporte de virtualización apropiado, como o acceso directo ao hardware de rede, xunto coas directrices para a optimización do rendemento suxeridas nesta Tese.[Abstract]The use of Java for parallel computing is becoming more promising owing to its appealing features, particularly its multithreading support, portability, easy-tolearn properties, high programming productivity and the noticeable improvement in its computational performance. However, parallel Java applications generally su er from inefficient communication middleware, most of which use socket-based protocols that are unable to take full advantage of high-speed networks, hindering the adoption of Java in the High Performance Computing (HPC) area. This PhD Thesis presents the design, development and evaluation of scalable Java communication solutions that overcome these constraints. Hence, we have implemented several lowlevel message-passing devices that fully exploit the underlying network hardware while taking advantage of Remote Direct Memory Access (RDMA) operations to provide low-latency communications. Moreover, we have developed a productionquality Java message-passing middleware, FastMPJ, in which the devices have been integrated seamlessly, thus allowing the productive development of Message-Passing in Java (MPJ) applications. The performance evaluation has shown that FastMPJ communication primitives are competitive with native message-passing libraries, improving signi cantly the scalability of MPJ applications. Furthermore, this Thesis has analyzed the potential of cloud computing towards spreading the outreach of HPC, where Infrastructure as a Service (IaaS) o erings have emerged as a feasible alternative to traditional HPC systems. Several cloud resources from the leading IaaS provider, Amazon EC2, which speci cally target HPC workloads, have been thoroughly assessed. The experimental results have shown the signi cant impact that virtualized environments still have on network performance, which hampers porting communication-intensive codes to the cloud. The key is the availability of the proper virtualization support, such as the direct access to the network hardware, along with the guidelines for performance optimization suggested in this Thesis

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Composable architecture for rack scale big data computing

The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and developed the implications for the network and resource provisioning and management for rack scale architecture

ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments

This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an open, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR1051.Peer ReviewedPostprint (published version

Performance Evaluation of Data-Intensive Computing Applications on a Public IaaS Cloud

[Abstract] The advent of cloud computing technologies, which dynamically provide on-demand access to computational resources over the Internet, is offering new possibilities to many scientists and researchers. Nowadays, Infrastructure as a Service (IaaS) cloud providers can offset the increasing processing requirements of data-intensive computing applications, becoming an emerging alternative to traditional servers and clusters. In this paper, a comprehensive study of the leading public IaaS cloud platform, Amazon EC2, has been conducted in order to assess its suitability for data-intensive computing. One of the key contributions of this work is the analysis of the storage-optimized family of EC2 instances. Furthermore, this study presents a detailed analysis of both performance and cost metrics. More specifically, multiple experiments have been carried out to analyze the full I/O software stack, ranging from the low-level storage devices and cluster file systems up to real-world applications using representative data-intensive parallel codes and MapReduce-based workloads. The analysis of the experimental results has shown that data-intensive applications can benefit from tailored EC2-based virtual clusters, enabling users to obtain the highest performance and cost-effectiveness in the cloud.Ministerio de Economía y Competitividad; TIN2013-42148-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/055Ministerio de Educación y Ciencia; AP2010-434

Ignis: An efficient and scalable multi-language Big Data framework

Most of the relevant Big Data processing frameworks (e.g., Apache Hadoop, Apache Spark) only support JVM (Java Virtual Machine) languages by default. In order to support non-JVM languages, subprocesses are created and connected to the framework using system pipes. With this technique, the impossibility of managing the data at thread level arises together with an important loss in the performance. To address this problem we introduce Ignis, a new Big Data framework that benefits from an elegant way to create multi-language executors managed through an RPC system. As a consequence, the new system is able to execute natively applications implemented using non-JVM languages. In addition, Ignis allows users to combine in the same application the benefits of implementing each computational task in the best suited programming language without additional overhead. The system runs completely inside Docker containers, isolating the execution environment from the physical machine. A comparison with Apache Spark shows the advantages of our proposal in terms of performance and scalabilityThis work has been supported by MICINN, Spain (RTI2018-093336-B-C21), Xunta de Galicia, Spain (ED431G/08 and ED431C-2018/19) and European Regional Development Fund (ERDF)S

Low‐latency Java communication devices on RDMA‐enabled networks

This is the peer reviewed version of the following article: Expósito, R. R., Taboada, G. L., Ramos, S., Touriño, J., & Doallo, R. (2015). Low‐latency Java communication devices on RDMA‐enabled networks. Concurrency and Computation: Practice and Experience, 27(17), 4852-4879., which has been published in final form at https://doi.org/10.1002/cpe.3473. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.[Abstract] Providing high‐performance inter‐node communication is a key capability for running high performance computing applications efficiently on parallel architectures. In fact, current systems deployments are aggregating a significant number of cores interconnected via advanced networking hardware with Remote Direct Memory Access (RDMA) mechanisms, that enable zero‐copy and kernel‐bypass features. The use of Java for parallel programming is becoming more promising thanks to some useful characteristics of this language, particularly its built‐in multithreading support, portability, easy‐to‐learn properties, and high productivity, along with the continuous increase in the performance of the Java virtual machine. However, current parallel Java applications generally suffer from inefficient communication middleware, mainly based on protocols with high communication overhead that do not take full advantage of RDMA‐enabled networks. This paper presents efficient low‐level Java communication devices that overcome these constraints by fully exploiting the underlying RDMA hardware, providing low‐latency and high‐bandwidth communications for parallel Java applications. The performance evaluation conducted on representative RDMA networks and parallel systems has shown significant point‐to‐point performance increases compared with previous Java communication middleware, allowing to obtain up to 40% improvement in application‐level performance on 4096 cores of a Cray XE6 supercomputer.Ministerio de Economía y Competitividad; TIN2013-42148-PXunta de Galicia; GRC2013/055Ministerio de Educación y Ciencia; AP2010-434