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检索条件"主题词=data-stream processing"

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Generic Checkpointing Support for stream-based State-Machine Replication 10

Generic Checkpointing Support for Stream-based State-Machine...

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10th Workshop on Principles and Practice of Consistency for Distributed data (PaPoC)

作者： Lawniczak, Laura Ammon, Marco Distler, Tobias Friedrich Alexander Univ Erlangen Nurnberg FAU Erlangen Germany

ISBN: (纸本)9798400700866

stream-based replication facilitates the deployment and operation of state-machine replication protocols by running them as applications on top of data-stream processing frameworks. Taking advantage of platform-provided features, this approach makes it possible to significantly minimize implementation complexity at the protocol level. To further extend the associated benefits, in this paper we examine how the concept can be used to provide generic support for creating, storing, and applying checkpoints of replica states, both in the use case for catch up and garbage collection as well as to recover failed replicas. Specifically, we present three checkpointing-mechanism designs with different degrees of platform involvement and evaluate them in the context of Twitter's stream-processing engine Heron.

关键词： State-machine replication checkpointing recovery data-stream processing

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pi-Lisco: Parallel and Incremental stream-Based Point-Cloud Clustering 22

pi-Lisco: Parallel and Incremental Stream-Based Point-Cloud ...

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37th Annual ACM Symposium on Applied Computing

作者： Najdataei, Hannaneh Gulisano, Vincenzo Tsigas, Philippas Papatriantafilou, Marina Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450387132

Point-cloud clustering is a key task in applications like autonomous vehicles and digital twins, where rotating LiDAR sensors commonly generate point-cloud measurements in data streams. The state-ofthe-art algorithms, Lisco and its parallel equivalent P-Lisco, define a single-pass distance-based clustering. However, while outperforming other batch-based techniques, they cannot incrementally cluster point-clouds from consecutive LiDAR rotations, as they cannot exploit result-similarity between rotations. The simplicity of Lisco, along with the potential of improvements through utilization of computational overlaps, form the motivation of a more challenging objective studied here. We propose Parallel and Incremental Lisco (pi-Lisco), which, with a simple yet efficient approach, clusters LiDAR data in streaming sliding windows, reusing the results from overlapping portions of the data, thus, enabling single-window (i.e., in-place) processing. Moreover, pi-Lisco employs efficient work-sharing among threads, facilitated by the ScaleGate data structure, and embeds a customised version of the STINGER concurrent data structure. Through an orchestration of these key ideas, pi-Lisco is able to lead to significant performance improvements. We complement with an evaluation of pi-Lisco, using the Ford Campus real-world extensive data-set, showing (i) the computational benefits from incrementally processing the consecutive point-clouds;and (ii) the fact that pi-Lisco' parallelization leads to continuously increasing sustainable rates with increasing number of threads, shifting the saturation point of the baseline.

关键词： Clustering data-stream processing Point-cloud analysis

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stream-based State-Machine Replication 17

Stream-based State-Machine Replication

引用

17th European Dependable Computing Conference (EDCC)

作者： Lawniczak, Laura Distler, Tobias Friedrich Alexander Univ Erlangen Nurnberg FAU Erlangen Germany

ISBN: (纸本)9781665436717

Developing state-machine replication protocols for practical use is a complex and labor-intensive process because of the myriad of essential tasks (e.g., deployment, communication, recovery) that need to be taken into account in an implementation. In this paper, we show how this problem can be addressed with stream-based replication, a novel approach that implements a replication protocol as application on top of a data-stream processing framework. With such framework already handling most essential tasks and furthermore providing means for debugging and monitoring, this technique has the key benefit of significantly minimizing overhead for both programmers as well as system operators. Our first stream-based protocol TARA tolerates crashes and comprises full-fledged mechanisms for request handling, checkpointing, and view changes. Still, TARA's prototype implementation, which is based on Twitter's Heron framework, consists of fewer than 1,500 lines of application-level code.

关键词： State-machine replication fault tolerance consensus scalability data-stream processing

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Personalized Management of Semantic, Dynamic data in Pervasive Systems: Context-ADDICT Revisited

Personalized Management of Semantic, Dynamic Data in Pervasi...

引用

International Conference on High Performance Computing & Simulation (HPCS)

作者： Panigati, Emanuele Politecn Milan Dipartimento Elettron Informaz & Biongegneria I-20133 Milan Italy

ISBN: (纸本)9781479953134

Due to the high information load to which everyone is exposed in her everyday life, the rise of new, systems fully supporting pervasive information distribution, analysis and sharing becomes a key factor to allow a correct and useful interaction among humans and computer systems. This kind of systems must allow to manage, integrate, analyze, and possibly reason on, a large and heterogeneous set of data. The SuNDroPS system, briefly described in this paper, applies context-aware techniques to data gathering, shared services, and information distribution;the system is based on a context-aware approach that, applied to these tasks, leads to the reduction of the so-called information noise, delivering to the users only the portion of information that is useful in their current context.

关键词： Context-Aware data Management Complex Event processing data-stream processing Map-Reduce-based data Mining Algorithms

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data stream Partitioning Re-Optimization Based on Runtime Dependency Mining

Data Stream Partitioning Re-Optimization Based on Runtime De...

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IEEE 30th International Conference on data Engineering (ICDE)

作者： Viel, Emeric Ueda, Haruyasu Fujitsu Labs Ltd Syst Software Labs Kawasaki Kanagawa 211 Japan

ISBN: (纸本)9781479934812

In distributed data stream processing, a program made of multiple queries can be parallelized by partitioning input streams according to the values of specific attributes, or partitioning keys. Applying different partitioning keys to different queries requires re-partitioning intermediary streams, causing extra communication and reduced throughput. Repartitionings can be avoided by detecting dependencies between the partitioning keys applicable to each query. Existing partitioning optimization methods analyze query syntax at compile-time to detect inter-key dependencies and avoid repartitionings. This paper extends those compile-time methods by adding a runtime re-optimization step based on the mining of temporal approximate dependencies (TADs) between partitioning keys. A TAD is defined in this paper as a type of dependency that can be approximately valid over a moving time window. Our evaluation, based on a simulation of the Linear Road Benchmark, showed a 94.5% reduction of the extra communication cost.

关键词： data-stream processing DSMS distributed processing partitioning optimization temporal approximate dependencies dependency mining

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