Browsing by Subject "Streaming applications"

Now showing 1 - 5 of 5

Open Access
Auto-parallelizing stateful distributed streaming applications
(2012) Schneider, S.; Hirzel, M.; Gedik, Buğra; Wu, K. -L.
Streaming applications transform possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. The streaming programming model naturally exposes task and pipeline parallelism, enabling it to exploit parallel systems of all kinds, including large clusters. However, it does not naturally expose data parallelism, which must instead be extracted from streaming applications. This paper presents a compiler and runtime system that automatically extract data parallelism for distributed stream processing. Our approach guarantees safety, even in the presence of stateful, selective, and userdefined operators. When constructing parallel regions, the compiler ensures safety by considering an operator's selectivity, state, partitioning, and dependencies on other operators in the graph. The distributed runtime system ensures that tuples always exit parallel regions in the same order they would without data parallelism, using the most efficient strategy as identified by the compiler. Our experiments using 100 cores across 14 machines show linear scalability for standard parallel regions, and near linear scalability when tuples are shuffled across parallel regions. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM).
Open Access
Big-data streaming applications scheduling based on staged multi-armed bandits
(Institute of Electrical and Electronics Engineers, 2016) Kanoun, K.; Tekin, C.; Atienza, D.; Van Der Schaar, M.
Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single stream input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new systematic and efficient methodology and associated algorithms for online learning and energy-efficient scheduling of Big-Data streaming applications with multiple streams on many core systems with resource constraints. We formalize the problem of multi-stream scheduling as a staged decision problem in which the performance obtained for various resource allocations is unknown. The proposed scheduling methodology uses a novel class of online adaptive learning techniques which we refer to as staged multi-armed bandits (S-MAB). Our scheduler is able to learn online which processing method to assign to each stream and how to allocate its resources over time in order to maximize the performance on the fly, at run-time, without having access to any offline information. The proposed scheduler, applied on a face detection streaming application and without using any offline information, is able to achieve similar performance compared to an optimal semi-online solution that has full knowledge of the input stream where the differences in throughput, observed quality, resource usage and energy efficiency are less than 1, 0.3, 0.2 and 4 percent respectively.
Open Access
Safe data parallelism for general streaming
(Institute of Electrical and Electronics Engineers, 2015) Schneider S.; Hirzel M.; Gedik, B.; Wu, Kun-Lung
Streaming applications process possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. General streaming applications use stateful, selective, and user-defined operators. The stream programming model naturally exposes task and pipeline parallelism, enabling it to exploit parallel systems of all kinds, including large clusters. However, data parallelism must either be manually introduced by programmers, or extracted as an optimization by compilers. Previous data parallel optimizations did not apply to selective, stateful and user-defined operators. This article presents a compiler and runtime system that automatically extracts data parallelism for general stream processing. Data-parallelization is safe if the transformed program has the same semantics as the original sequential version. The compiler forms parallel regions while considering operator selectivity, state, partitioning, and graph dependencies. The distributed runtime system ensures that tuples always exit parallel regions in the same order they would without data parallelism, using the most efficient strategy as identified by the compiler. Our experiments using 100 cores across 14 machines show linear scalability for parallel regions that are computation-bound, and near linear scalability when tuples are shuffled across parallel regions.
Open Access
SPL: an extensible language for distributed stream processing
(Association for Computing Machinery, 2017) Hirzel M.; Schneider S.; Gedik, B.
Big data is revolutionizing how all sectors of our economy do business, including telecommunication, transportation, medical, and finance. Big data comes in two flavors: data at rest and data in motion. Processing data in motion is stream processing. Stream processing for big data analytics often requires scale that can only be delivered by a distributed system, exploiting parallelism on many hosts and many cores. One such distributed stream processing system is IBM Streams. Early customer experience with IBM Streams uncovered that another core requirement is extensibility, since customers want to build high-performance domain-specific operators for use in their streaming applications. Based on these two core requirements of distribution and extensibility, we designed and implemented the Streams Processing Language (SPL). This article describes SPL with an emphasis on the language design, distributed runtime, and extensibility mechanism. SPL is now the gateway for the IBM Streams platform, used by our customers for stream processing in a broad range of application domains. © 2017 ACM.
Open Access
Tutorial: Stream processing optimizations
(ACM, 2013) Schneider, S.; Hirzel, M.; Gedik, Buğra
This tutorial starts with a survey of optimizations for streaming applications. The survey is organized as a catalog that introduces uniform terminology and a common categorization of optimizations across disciplines, such as data management, programming languages, and operating systems. After this survey, the tutorial continues with a deep-dive into the fission optimization, which automatically transforms streaming applications for data-parallelism. Fis-sion helps an application improve its throughput by taking advantage of multiple cores in a machine, or, in the case of a distributed streaming engine, multiple machines in a cluster. While the survey of optimizations covers a wide range of work from the literature, the in-depth discussion of ission relies more heavily on the presenters' own research and experience in the area. The tutorial concludes with a discussion of open research challenges in the field of stream processing optimizations. Copyright © 2013 ACM.