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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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Bellamy: Reusing Performance Models for Distributed dataflow Jobs Across Contexts

Bellamy: Reusing Performance Models for Distributed Dataflow...

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IEEE International Conference on Cluster Computing (Cluster)

作者： Scheinert, Dominik Thamsen, Lauritz Zhu, Houkun Will, Jonathan Acker, Alexander Wittkopp, Thorsten Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781728196664

Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforward. Performance models trained on historical executions of a concrete job are helpful in such situations, yet they are usually bound to a specific job execution context (e.g. node type, software versions, job parameters) due to the few considered input parameters. Even in case of slight context changes, such supportive models need to be retrained and cannot benefit from historical execution data from related contexts. This paper presents Bellamy, a novel modeling approach that combines scale-outs, dataset sizes, and runtimes with additional descriptive properties of a dataflow job. It is thereby able to capture the context of a job execution. Moreover, Bellamy is realizing a two-step modeling approach. First, a general model is trained on all the available data for a specific scalable analytics algorithm, hereby incorporating data from different contexts. Subsequently, the general model is optimized for the specific situation at hand, based on the available data for the concrete context. We evaluate our approach on two publicly available datasets consisting of execution data from various dataflow jobs carried out in different environments, showing that Bellamy outperforms state-of-the-art methods.

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

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Bolt-on Differential Privacy for scalable Stochastic Gradient Descent-based analytics 17

Bolt-on Differential Privacy for Scalable Stochastic Gradien...

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ACM International Conference on Management of data

作者： Wu, Xi Li, Fengan Kumar, Arun Chaudhuri, Kamalika Jha, Somesh Naughton, Jeffrey Google Mountain View CA 94043 USA Univ Calif San Diego San Diego CA 92103 USA Univ Wisconsin Madison Madison WI USA

ISBN: (纸本)9781450341974

While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address both issues in an integrated manner. In contrast to the white-box approach adopted by previous work, we revisit and use the classical technique of output perturbation to devise a novel "bolt-on" approach to private SGD. While our approach trivially addresses (2), it makes (1) even more challenging. We address this challenge by providing a novel analysis of the L-2-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We integrate our algorithm, as well as other state-of-the-art differentially private SGD, into Bismarck, a popular scalable SGD-based analytics system on top of an RDBMS. Extensive experiments show that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms on many real datasets.

关键词： differential privacy optimization scalable data analytics stochastic gradient descent

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Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale data Processing 10

Get Your Memory Right: The Crispy Resource Allocation Assist...

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10th IEEE International Conference on Cloud Engineering (IC2E)

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

ISBN: (数字)9781665491150

ISBN: (纸本)9781665491150

Distributed dataflow systems like Apache Spark and Apache Hadoop enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational 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. Further, existing automated approaches to resource selection rely on the assumption that a job is recurring to learn from previous runs or to warrant the cost of full test runs to learn from. However, this assumption often does not hold since many jobs are too unique. Therefore, we present Crispy, a method for optimizing data processing cluster configurations based on job profiling runs with small samples of the dataset on just a single machine. Crispy attempts to extrapolate the memory usage for the full dataset to then choose a cluster configuration with enough total memory. In our evaluation on a dataset with 1031 Spark and Hadoop jobs, we see a reduction of job execution costs by 56% compared to the baseline, while on average spending less than ten minutes on profiling runs per job on a consumer-grade laptop.

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

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

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

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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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Enel: Context-Aware Dynamic Scaling of Distributed dataflow Jobs using Graph Propagation

Enel: Context-Aware Dynamic Scaling of Distributed Dataflow ...

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40th IEEE International Performance, Computing, and Communications Conference (IPCCC)

作者： Scheinert, Dominik Zhu, Houkun Thamsen, Lauritz Geldenhuys, Morgan K. Will, Jonathan Acker, Alexander Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781665443319

Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics. While runtime prediction models can be used to initially select appropriate cluster resources given target runtimes, the actual runtime performance of dataflow jobs depends on several factors and varies over time. Yet, in many situations, dynamic scaling can be used to meet formulated runtime targets despite significant performance variance. This paper presents Enel, a novel dynamic scaling approach that uses message propagation on an attributed graph to model dataflow jobs and, thus, allows for deriving effective resealing decisions. For this, Enel incorporates descriptive properties that capture the respective execution context, considers statistics from individual dataflow tasks, and propagates predictions through the job graph to eventually find an optimized new scale-out. Our evaluation of Enel with four iterative Spark jobs shows that our approach is able to identify effective resealing actions, reacting for instance to node failures, and can be reused across different execution contexts.

关键词： scalable data analytics Distributed dataflows Dynamic Scaling Performance Modeling Runtime Prediction Resource Management Graph Neural Networks

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