252 research outputs found
Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service
An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1
BDEv 3.0: energy efficiency and microarchitectural characterization of Big Data processing frameworks
This is a post-peer-review, pre-copyedit version of an article published in Future Generation Computer Systems. The final authenticated version is available online at: https://doi.org/10.1016/j.future.2018.04.030[Abstract] As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metrics is key to fully understanding the behavior of these frameworks. For example, microarchitecture-level events can bring meaningful insights to characterize the interaction between frameworks and hardware. Moreover, energy consumption is also gaining increasing attention as systems scale to thousands of cores. This work discusses the current state of the art in evaluating distributed processing frameworks, while extending our Big Data Evaluator tool (BDEv) to extract energy efficiency and microarchitecture-level metrics from the execution of representative Big Data workloads. An experimental evaluation using BDEv demonstrates its usefulness to bring meaningful information from popular frameworks such as Hadoop, Spark and Flink.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-PMinisterio de Educación; FPU14/02805Ministerio de Educación; FPU15/0338
ShenZhen transportation system (SZTS): a novel big data benchmark suite
Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets
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
Architecting Data Centers for High Efficiency and Low Latency
Modern data centers, housing remarkably powerful computational capacity, are built in massive scales and consume a huge amount of energy. The energy consumption of data centers has mushroomed from virtually nothing to about three percent of the global electricity supply in the last decade, and will continuously grow. Unfortunately, a significant fraction of this energy consumption is wasted due to the inefficiency of current data center architectures, and one of the key reasons behind this inefficiency is the stringent response latency requirements of the user-facing services hosted in these data centers such as web search and social networks. To deliver such low response latency, data center operators often have to overprovision resources to handle high peaks in user load and unexpected load spikes, resulting in low efficiency.
This dissertation investigates data center architecture designs that reconcile high system efficiency and low response latency. To increase the efficiency, we propose techniques that understand both microarchitectural-level resource sharing and system-level resource usage dynamics to enable highly efficient co-locations of latency-critical services and low-priority batch workloads. We investigate the resource sharing on real-system simultaneous multithreading (SMT) processors to enable SMT co-locations by precisely predicting the performance interference. We then leverage historical resource usage patterns to further optimize the task scheduling algorithm and data placement policy to improve the efficiency of workload co-locations. Moreover, we introduce methodologies to better manage the response latency by automatically attributing the source of tail latency to low-level architectural and system configurations in both offline load testing environment and online production environment. We design and develop a response latency evaluation framework at microsecond-level precision for data center applications, with which we construct statistical inference procedures to attribute the source of tail latency. Finally, we present an approach that proactively enacts carefully designed causal inference micro-experiments to diagnose the root causes of response latency anomalies, and automatically correct them to reduce the response latency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144144/1/yunqi_1.pd
Hadoop-Oriented SVM-LRU (H-SVM-LRU): An Intelligent Cache Replacement Algorithm to Improve MapReduce Performance
Modern applications can generate a large amount of data from different
sources with high velocity, a combination that is difficult to store and
process via traditional tools. Hadoop is one framework that is used for the
parallel processing of a large amount of data in a distributed environment,
however, various challenges can lead to poor performance. Two particular issues
that can limit performance are the high access time for I/O operations and the
recomputation of intermediate data. The combination of these two issues can
result in resource wastage. In recent years, there have been attempts to
overcome these problems by using caching mechanisms. Due to cache space
limitations, it is crucial to use this space efficiently and avoid cache
pollution (the cache contains data that is not used in the future). We propose
Hadoop-oriented SVM-LRU (HSVM- LRU) to improve Hadoop performance. For this
purpose, we use an intelligent cache replacement algorithm, SVM-LRU, that
combines the well-known LRU mechanism with a machine learning algorithm, SVM,
to classify cached data into two groups based on their future usage.
Experimental results show a significant decrease in execution time as a result
of an increased cache hit ratio, leading to a positive impact on Hadoop
performance
Optimizing Virtual Machine I/O Performance in Virtualized Cloud by Differenciated-frequency Scheduling and Functionality Offloading
Many enterprises are increasingly moving their applications to private cloud environments or public cloud platforms. A key technology driving cloud computing is virtualization which can serve multiple VMs in one physical machine hence providing better management flexibility and significant savings in operational costs. However, one important consequence of virtualized hosts in the cloud is the negative impact it has on the I/O performance of the applications running in the VMs
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