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检索条件"主题词=scalable data analytics"

共 18 条记录，以下是1-10 订阅

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Boosting scalable data analytics with Modern Programmable Networks 14

Boosting Scalable Data Analytics with Modern Programmable Ne...

引用

14th International Workshop on data Management on New Hardware (DaMoN)

作者： Blocher, Marcel Ziegler, Tobias Binnig, Carsten Eugster, Patrick Tech Univ Darmstadt Darmstadt Germany Brown Univ Providence RI 02912 USA Univ Lugano USI Lugano Switzerland Purdue Univ W Lafayette IN 47907 USA

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C3O: Collaborative Cluster Configuration Optimization for Distributed data Processing in Public Clouds 9

C3O: Collaborative Cluster Configuration Optimization for Di...

引用

9th IEEE International Conference on Cloud Engineering (IC2E)

作者： Will, Jonathan Thamsen, Lauritz Scheinert, Dominik Bader, Jonathan Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781665449700

Distributed dataflow systems enable data-parallel processing of large datasets on clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. Yet, selecting appropriate cloud resources for dataflow jobs - that neither lead to bottlenecks nor to low resource utilization - is often challenging, even for expert users such as data engineers. We present C3O, a collaborative system for optimizing data processing cluster configurations in public clouds based on shared historical runtime data. The shared data is utilized for predicting the runtimes of data processing jobs on different possible cluster configurations, using specialized regression models. These models take the diverse execution contexts of different users into account and exhibit mean absolute errors below 3% in our experimental evaluation with 930 unique Spark jobs.

关键词： scalable data analytics Distributed dataflows Runtime Prediction Resource Allocation Cluster Management

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Scheduling Recurring Distributed dataflow Jobs Based on Resource Utilization and Interference 6

Scheduling Recurring Distributed Dataflow Jobs Based on Reso...

引用

IEEE 6th International Congress on Big data (Bigdata Congress)

作者： Thamsen, Lauritz Rabier, Benjamin Schmidt, Florian Renner, Thomas Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781538619964

Resource management systems like YARN or Mesos enable users to share cluster infrastructures by running analytics jobs in temporarily reserved containers. These containers are typically not isolated to achieve high degrees of overall resource utilizations despite the often fluctuating resource usage of single analytic jobs. However, some combinations of jobs utilize the resources better and interfere less with each others when running on the same nodes than others. This paper presents an approach for improving the resource utilization and job throughput when scheduling recurring data analysis jobs in shared cluster environments. Using a reinforcement learning algorithm, the scheduler continuously learns which jobs are best executed simultaneously on the cluster. Our evaluation of an implementation built on Hadoop YARN shows that this approach can increase resource utilization and decrease job runtimes. While interference between jobs can be avoided, co-locations of jobs with complementary resource usage are not yet always fully recognized. However, with a better measure of co-location goodness, our solution can be used to automatically adapt the scheduling to workloads with recurring batch jobs.

关键词： scalable data analytics Distributed dataflows Resource Management Cluster Scheduling Job Interference

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Toward Large-Scale Image Segmentation on Summit 20

Toward Large-Scale Image Segmentation on Summit

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49th International Conference on Parallel Processing (ICPP)

作者： Seal, Sudip K. Lim, Seung-Hwan Wang, Dali Hinkle, Jacob Lunga, Dalton Tsaris, Aristeidis Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37830 USA Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN USA Oak Ridge Natl Lab Natl Secur Emerging Technol Oak Ridge TN USA Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN USA

ISBN: (纸本)9781450388160

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (similar to 10(2) x 10(2)), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (similar to 10(4) x 10(4)). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver similar to 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

关键词： applied machine learning scalable data analytics deep neural networks model parallel image segmentation U-Net pipeline

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Training data Reduction for Performance Models of data analytics Jobs in the Cloud

Training Data Reduction for Performance Models of Data Analy...

引用

9th IEEE International Conference on Big data (IEEE Bigdata)

作者： Will, Jonathan Arslan, Onur Bader, Jonathan Scheinert, Dominik Thamsen, Lauritz Tech Univ Berlin Berlin Germany Humboldt Univ Berlin Germany

ISBN: (纸本)9781665439022

Distributed dataflow systems like Apache Flink and Apache Spark simplify processing large amounts of data on clusters in a data-parallel manner. However, choosing suitable cluster resources for distributed dataflow jobs in both type and number is difficult, especially for users who do not have access to previous performance metrics. One approach to overcoming this issue is to have users share runtime metrics to train context-aware performance models that help find a suitable configuration for the job at hand. A problem when sharing runtime data instead of trained models or model parameters is that the data size can grow substantially over time. This paper examines several clustering techniques to minimize training data size while keeping the associated performance models accurate. Our results indicate that efficiency gains in data transfer, storage, and model training can be achieved through training data reduction. In the evaluation of our solution on a dataset of runtime data from 930 unique distributed dataflow jobs, we observed that, on average, a 75% data reduction only increases prediction errors by one percentage point.

关键词： scalable data analytics Distributed dataflows Resource Allocation Machine Learning data Reduction

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Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big data analytics

Karasu: A Collaborative Approach to Efficient Cluster Config...

引用

IEEE International Performance, Computing, and Communications Conference (IPCCC)

作者： Scheinert, Dominik Wiesner, Philipp Wittkopp, Thorsten Thamsen, Lauritz Will, Jonathan Kao, Odej Tech Univ Berlin Berlin Germany Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9798350302936

Selecting the right resources for big data analytics jobs is hard because of the wide variety of configuration options like machine type and cluster size. As poor choices can have a significant impact on resource efficiency, cost, and energy usage, automated approaches are gaining popularity. Most existing methods rely on profiling recurring workloads to find near-optimal solutions over time. Due to the cold-start problem, this often leads to lengthy and costly profiling phases. However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks. The potential in sharing aggregated profiling runs to collaboratively address the cold start problem is largely unexplored. We present Karasu, an approach to more efficient resource configuration profiling that promotes data sharing among users working with similar infrastructures, frameworks, algorithms, or datasets. Karasu trains lightweight performance models using aggregated runtime information of collaborators and combines them into an ensemble method to exploit inherent knowledge of the configuration search space. Moreover, Karasu allows the optimization of multiple objectives simultaneously. Our evaluation is based on performance data from diverse workload executions in a public cloud environment. We show that Karasu is able to significantly boost existing methods in terms of performance, search time, and cost, even when few comparable profiling runs are available that share only partial common characteristics with the target job.

关键词： scalable data analytics Distributed data Processing Performance Modeling Resource Management

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Extending the visualization capabilities of a genome browser

Extending the visualization capabilities of a genome browser

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4th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Lamfalusi, Csaba Girus, David Kruppa, Kinga Toth, Janos Pocsai, Eniko Hajdune Kunkli, Roland Hajdu, Andras Balint, Balint L. Univ Debrecen Fac Informat POB 12 H-4010 Debrecen Hungary Univ Debrecen Med & Hlth Sci Ctr Dept Biochem & Mol Biol H-4032 Debrecen Hungary

ISBN: (纸本)9781479915439;9781479915460

scalable visualization of big data is highly desired in several fields. A demonstrative example is annotated genetic data, where the DNA sequence can be better visualized using space-filling fractal curves. Though such approaches are already available in the literature, they are not applied routinely in the clinical practice. The reason of this undesired fact is that clinicians use well-established, but rather old gene browsers in their work. Naturally, these browsers lack state-of-the-art programming support and also visualization features. To help with this problem, in this paper, we propose a completion of a very widely used genome browser with scalable visualization techniques. The motivation of the work came from the clinical partner, since a good overview of the whole annotated DNA segment can lead to the recognition of currently unknown relations between genes. We explain what steps were needed to add the new visualization feature to the browser in terms of graphical elements, data extraction, and a scalable user interface.

关键词： Customizable content management Information visualization scalable data analytics Genome browsing Space-filling curves

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CoBell: Runtime Prediction for Distributed dataflow Jobs in Shared Clusters 10

CoBell: Runtime Prediction for Distributed Dataflow Jobs in ...

引用

10th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom)

作者： Verbitskiy, Ilya Thamsen, Lauritz Renner, Thomas Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781538678992

Distributed dataflow systems have been developed to help users analyze and process large datasets. While they make it easier for users to develop massively-parallel programs, users still have to choose the amount of resources for the execution of their jobs. Yet, users do not necessarily understand workload and system dynamics, while they often have constraints like runtime targets and budgets. Addressing this problem, systems have been developed that automatically select the required amount of resources to fulfill the users' constraints. However, interference with co-located workloads can introduce a significant variance into the runtimes of jobs and make accurate runtime prediction harder. This paper presents CoBell, a resource allocation system that incorporates information about co-located workloads to improve the runtime prediction for jobs in shared clusters. CoBell receives jobs from users with runtime and scale-out constraints and then reserves resources based on predicted runtimes. We implemented CoBell as a job submission tool for YARN. As such, it works with existing YARN cluster setups. The paper evaluates CoBell using five different distributed dataflow jobs, showing that using CoBell results in runtimes that do not violate the runtime constraints by more than 7.2%.

关键词： scalable data analytics Distributed dataflows Runtime Prediction Resource Allocation Cluster Management

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Ellis: Dynamically Scaling Distributed dataflows To Meet Runtime Targets 9

Ellis: Dynamically Scaling Distributed Dataflows To Meet Run...

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9th IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Thamsen, Lauritz Verbitskiy, Ilya Beilharz, Jossekin Renner, Thomas Polze, Andreas Kao, Odej Tech Univ Berlin Berlin Germany Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781538606926

Distributed dataflow systems like MapReduce, Spark, and Flink help users in analyzing large datasets with a set of cluster resources. Performance modeling and runtime prediction is then used for automatically allocating resources for specific performance goals. However, the actual performance of distributed dataflow jobs can vary significantly due to factors like interference with co-located workloads, varying degrees of data locality, and failures. We address this problem with Ellis, a system that allocates an initial set of resources for a specific runtime target, yet also continuously monitors a job's progress towards the target and if necessary dynamically adjusts the allocation. For this, Ellis models the scale-out behavior of individual stages of distributed dataflow jobs based on previous executions. Our evaluation of Ellis with iterative Spark jobs shows that dynamic adjustments can reduce the number of constraint violations by 30.7-75.0% and the magnitude of constraint violations by 70.6-94.5%.

关键词： scalable data analytics Distributed dataflows Dynamic Scaling Runtime Prediction Resource Management

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Towards Collaborative Optimization of Cluster Configurations for Distributed dataflow Jobs 8

Towards Collaborative Optimization of Cluster Configurations...

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8th IEEE International Conference on Big data (Big data)

作者： Will, Jonathan Bader, Jonathan Thamsen, Lauritz Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781728162515

Analyzing large datasets with distributed dataflow systems requires the use of clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. However, picking the appropriate resources in both type and number can often be challenging, as the selected configuration needs to match a distributed dataflow job's resource demands and access patterns. A good cluster configuration avoids hardware bottlenecks and maximizes resource utilization, avoiding costly overprovisioning. We propose a collaborative approach for finding optimal cluster configurations based on sharing and learning from historical runtime data of distributed dataflow jobs. Collaboratively shared data can be utilized to predict runtimes of future job executions through the use of specialized regression models. However, training prediction models on historical runtime data that were produced by different users and in diverse contexts requires the models to take these contexts into account.

关键词： scalable data analytics Distributed dataflows Runtime Prediction Resource Allocation Cluster Management

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