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检索条件"主题词=Data Stream Processing"

共 203 条记录，以下是31-40 订阅

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Key Technology of Online Auditing data stream processing 12

Key Technology of Online Auditing Data Stream Processing

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12th IEEE Int Conf Ubiquitous Intelligence & Comp/12th IEEE Int Conf Autonom & Trusted Comp/15th IEEE Int Conf Scalable Comp & Commun & Associated Workshops/IEEE Int Conf Cloud & Big data Comp/IEEE Int Conf Internet People

作者： Li, Tuo Wang, Lei Miami Univ Coll Engn & Comp Oxford OH 45056 USA Beijing Informat Sci & Technol Univ Sch Informat Management Beijing 100192 Peoples R China

ISBN: (纸本)9781467372114

At present, big data processing, especially the stream data processing has become one of the main problems that restrict the application of online auditing. Based on the online auditing cloud platform infrastructure which relies on data stream processing technology and dynamic elastic service resources combination model which satisfies the demands of online auditing application, this paper is going to study and design the intelligent outlier detection algorithm and correlation analysis algorithm for the data stream analysis in online auditing, and detect suspect points and clues to empirical application and analysis depending on the data resource from the national auditing data center.

关键词： online auditing data stream processing outlier analysis correlation analysis

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The 4th International Workshop on Autonomic Solutions for Parallel and Distributed data stream processing (Auto-DaSP 2021) 21

The 4th International Workshop on Autonomic Solutions for Pa...

引用

ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Cardellini, Valeria Mencagli, Gabriele Torquati, Massimo Univ Roma Tor Vergata Rome Italy Univ Pisa Pisa Italy

The organizers of the 4th International Workshop on Autonomic Solutions for Parallel and Distributed data stream processing (Auto-DaSP 2021) are delighted to welcome you to the workshop proceedings as part of the ICPE... 详细信息

ISBN: (纸本)9781450381949

关键词： run-time adaptation mechanisms and techniques data stream processing

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Toward data-centric software architecture for automotive systems - Embedded data stream processing approach - 11

Toward data-centric software architecture for automotive sys...

引用

IEEE Int Conf on Ubiquitous Intelligence and Computing (UIC) / 11th IEEE Int Conf on Autonomic and Trusted Computing (ATC) / 14th IEEE Int Conf on Scalable Computing and Communications (ScalCom)

作者： Nakamoto, Yukikazu Yamaguchi, Akihiro Sato, Kenya Honda, Shinya Takada, Hiroaki Univ Hyogo Grad Sch Appl Informat Kobe Hyogo Japan Nagoya Univ Ctr Embedded Comp Syst Nagoya Aichi Japan Doshisha Univ Mobil Res Ctr Kyoto Japan Nagoya Univ Grad Sch Informat Sci Nagoya Aichi Japan

ISBN: (纸本)9781479976461

In the automotive field, intelligent control systems and information services using vehicle data are rapidly expanding. However, there is no software architecture to efficiently handle sensor data in vehicles and their surroundings. In order to address these problems and develop automotive software with higher productivity and trustworthiness, we have researched and developed data-centric software architecture for a data stream management system for an automotive embedded system (eDSMS) and a stream local dynamic map (LDM) using eDSMS. An LDM is a collection of data that is intended to be used in cooperative intelligent transportation systems. In this paper, we present features of eDSMS and LDM using eDSMS, and demonstrate collision warning application software with eDSMS and a driver assistance system with a stream-based LDM.

关键词： automotive software data stream processing local dynamic map data flow processing Software architecture CYP51A1 gene LDM Driver Assistance Systems automotive engineering data Collection Methods Information Services Sensor data vehicle

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MEAD: Model-Based Vertical Auto-Scaling for data stream processing 21

MEAD: Model-Based Vertical Auto-Scaling for Data Stream Proc...

引用

21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Russo, Gabriele Russo Cardellini, Valeria Casale, Giuliano Lo Presti, Francesco Univ Roma Tor Vergata Rome Italy Imperial Coll London London England

ISBN: (纸本)9781728195865

The unpredictable variability of data stream processing (DSP) application workloads calls for advanced mechanisms and policies for elastically scaling the processing capacity of DSP operators. Whilst many different approaches have been used to devise policies, most of the solutions have focused on data arrival rate and operator resource utilization as key metrics for auto-scaling. We here show that, under burstiness in the data flows, overly simple characterizations of the input stream can yet lead to very inaccurate performance estimations that affect such policies, resulting in sub-optimal resource allocation. We then present MEAD, a vertical auto-scaling solution that relies on online state-based representation of burstiness to drive resource allocation. We use in particular Markovian Arrival Processes (MAPs), which are composable with analytical queueing models, allowing us to efficiently predict performance at run-time under burstiness. We integrate MEAD in Apache Flink, and evaluate its benefits over simpler yet popular auto-scaling solutions, using both synthetic and real-world workloads. Differently from existing approaches, MEAD satisfies response time requirements under burstiness, while saving up to 50% CPU resources with respect to a static allocation.

关键词： data stream processing auto-scaling workload characterization Markovian Arrival Processes

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An Optimal Model for Optimizing the Placement and Parallelism of data stream processing Applications on Cloud-Edge Computing 32

An Optimal Model for Optimizing the Placement and Parallelis...

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32nd IEEE International Symposium on Computer Architecture and High-Performance Computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core Architectures (WAMCA)

作者： de Souza, Felipe Rodrigo de Assuncao, Marcos Dias Caron, Eddy Veith, Alexandre da Silva Univ Lyon EnsL INRIA UCBLCNRSLIP Lyon 07 France Univ Toronto Toronto ON Canada

ISBN: (纸本)9781728199245

The Internet of Things has enabled many application scenarios where a large number of connected devices generate unbounded streams of data, often processed by data stream processing frameworks deployed in the cloud. Edge computing enables offloading processing from the cloud and placing it close to where the data is generated, thereby reducing the time to process data events and deployment costs. However, edge resources are more computationally constrained than their cloud counterparts, raising two interrelated issues, namely deciding on the parallelism of processing tasks (a.k.a. operators) and their mapping onto available resources. In this work, we formulate the scenario of operator placement and parallelism as an optimal mixed-integer linear programming problem. The proposed model is termed as Cloud-Edge data stream Placement (CESP). Experimental results using discrete-event simulation demonstrate that CESP can achieve an end-to-end latency at least similar or equal to 80% and monetary costs at least similar or equal to 30% better than traditional cloud deployment.

关键词： data stream processing Operator Placement Operator Parallelism End-to-end Latency Edge Computing

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Automatic Performance Tuning for Distributed data stream processing Systems 38

Automatic Performance Tuning for Distributed Data Stream Pro...

引用

38th IEEE International Conference on data Engineering (ICDE)

作者： Herodotou, Herodotos Odysseos, Lambros Chen, Yuxing Lu, Jiaheng Cyprus Univ Technol Limassol Cyprus Tencent Inc Shenzhen Peoples R China Univ Helsinki Helsinki Finland

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Distributed data stream processing systems (DSPSs) such as Storm, Flink, and Spark streaming are now routinely used to process continuous data streams in (near) real-time. However, achieving the low latency and high throughput demanded by today's streaming applications can be a daunting task, especially since the performance of DSPSs highly depends on a large number of system parameters that control load balancing, degree of parallelism, buffer sizes, and various other aspects of system execution. This tutorial offers a comprehensive review of the state-of-the-art automatic performance tuning approaches that have been proposed in recent years. The approaches are organized into five main categories based on their methodologies and features: cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. The categories of approaches will be analyzed in depth and compared to each other, exposing their various strengths and weaknesses. Finally, we will identify several open research problems and challenges related to automatic performance tuning for DSPSs.

关键词： Performance tuning data stream processing parameter tuning Storm Flink Spark streaming

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A Pareto-Efficient Algorithm for data stream processing at Network Edges 10

A Pareto-Efficient Algorithm for Data Stream Processing at N...

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

作者： Loukopoulos, Thanasis Tziritas, Nikos Koziri, Maria Stamoulis, Georgios Khan, Samee U. Univ Thessaly Dept Comp Sci & Biomed Informat Lamia Greece Univ Thessaly Dept Comp Sci Lamia Greece North Dakota State Univ Dept Elect & Comp Engn Fargo ND USA

ISBN: (纸本)9781538678992

data stream processing has received considerable attention from both research community and industry over the last years. Since latency is a key issue in data stream processing environments, the majority of the works existing in the literature focus on minimizing the latency experienced by the users. The aforementioned minimization takes place by assigning the data stream processing components close to data sources. Server consolidation is also a key issue for drastically reducing energy consumption in computing systems. Unfortunately, energy consumption and latency are two objective functions that may be in conflict with each other. Therefore, when the target function is to minimize energy consumption, the delay experienced by users may be considerable high, and the opposite. For the above reason there is a dire need to design strategies such that by targeting the minimization of energy consumption, there is a graceful degradation in latency, as well as the opposite. To achieve the above, we propose a Pareto-efficient algorithm that tackles the problem of data processing tasks placement simultaneously in both dimensions regarding the energy consumption and latency. The proposed algorithm outputs a set of solutions that are not dominated by any solution within the set regarding energy consumption and latency. The experimental results show that the proposed approach is superior against single-solution approaches because by targeting one objective function the other one can be gracefully degraded by choosing the appropriate solution.

关键词： data stream processing Pareto-efficient algorithms

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Spinstreams: a Static Optimization Tool for data stream processing Applications 18

SpinStreams: a Static Optimization Tool for Data Stream Proc...

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19th ACM/IFIP/USENIX Middleware Conference

作者： Mencagli, Gabriele Dazzi, Patrizio Tonci, Nicolo Univ Pisa Dept Comp Sci Pisa Italy ISTI CNR Pisa Italy

ISBN: (纸本)9781450357029

The ubiquity of data streams in different fields of computing has led to the emergence of stream processing Systems (SPSs) used to program applications that extract insights from unbounded sequences of data items. streaming applications demand various kinds of optimizations. Most of them are aimed at increasing throughput and reducing processing latency, and need cost models used to analyze the steady-state performance by capturing complex aspects like backpressure and bottleneck detection. In those systems, the tendency is to support dynamic optimizations of running applications which, although with a substantial run-time overhead, are unavoidable in case of unpredictable workloads. As an orthogonal direction, this paper proposes Spinstreams, a static optimization tool able to leverage cost models that programmers can use to detect and understand the inefficiencies of an initial application design. Spinstreams suggests optimizations for restructuring applications by generating code to be run on the SPS. We present the theory behind our optimizations, which cover more general classes of application structures than the ones studied in the literature so far. Then, we assess the accuracy of our models in Akka, an actor-based streaming framework providing a Java and Scala API.

关键词： data stream processing Operator Fission Operator Fusion Backpressure Akka

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Towards Elastic and Sustainable data stream processing on Edge Infrastructure 21

Towards Elastic and Sustainable Data Stream Processing on Ed...

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ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： de Assuncao, Marcos Dias ETS Montreal Montreal PQ Canada

ISBN: (纸本)9781450383318

Much of the data produced today is processed as it is generated by data stream processing systems. Although the cloud is often the target infrastructure for deploying data stream processing applications, resources located at the edges of the Internet have increasingly been used to offload some of the processing performed in the cloud and hence reduce the end-to-end latency when handling data events. In this work, I highlight some of the challenges in executing data stream processing applications on edge computing infrastructure and discuss directions for future research on making such applications more elastic and sustainable.

关键词： edge computing data stream processing cloud computing resource management

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Quantitative Impact Evaluation of an Abstraction Layer for data stream processing Systems 39

Quantitative Impact Evaluation of an Abstraction Layer for D...

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39th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Hesse, Guenter Matthies, Christoph Glass, Kelvin Huegle, Johannes Uflacker, Matthias Univ Potsdam Hasso Plattner Inst Potsdam Germany Free Univ Berlin Dept Math & Comp Sci Berlin Germany

ISBN: (纸本)9781728125190

With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution costs by providing an abstraction layer that enables executing programs on any of the supported streaming frameworks. In this paper, we present a novel benchmark architecture for comparing the performance impact of using Apache Beam on three streaming frameworks: Apache Spark streaming, Apache fink, and Apache Apex. We find significant performance penalties when using Apache Beam for application development in the surveyed systems. Overall, usage of Apache Beam for the examined streaming applications caused a high variance of query execution times with a slowdown of up to a factor of 58 compared to queries developed without the abstraction layer. All developed benchmark artifacts are publicly available to ensure reproducible results.

关键词： data stream processing Abstraction Layer Performance Benchmarking

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