GEM: An analytic geometrical approach to fast event matching for multi-dimensional content-based publish/subscribe services

Fan, Wenhao; Liu, Yuanan; Tang, Bihua

doi:10.1109/infocom.2016.7524338

Cited by 19 publications

(18 citation statements)

References 15 publications

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“…GEM [15] and REIN [30] are different from the count-based and tree-based methods presented above. The idea behind these two algorithms is to filter out the unmatching subscriptions one by one for each event.…”

Section: Related Workmentioning

confidence: 91%

“…We concentrate on pub/sub matching since other contexts either use different languages or cannot scale to thousands of dimensions and millions of expressions. Existing solutions in pub/sub matching include BE-Tree [33,34,35], OpIndex [39], Propagation [14], k-index [38], SIFT [3], Gryphon [3], H-Tree [31], TAMA [40], REIN [30], GEM [15], and SCAN [3]. These solutions can be roughly classified into two classes: count-based methods and tree-based methods.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to a set of PS-Trees, PSTHash utilizes two more data structures: a set of 2-dim predicate space IDs and a hash p [7] 7 p [15] Assume that an event E { attr1, 1 , attr2, −1 , attr3, 2 } is received. The event is matched against these three PS-Trees.…”

Section: Psthash Structurementioning

confidence: 99%

“…A large number of Boolean expression matching algorithms exist [3,14,38,33,34,35,6,39,31,30,15,28]. However, these solutions continue to suffer from limitations that affect performance and applicability.…”

Section: Introductionmentioning

confidence: 99%

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PS-tree-based efficient boolean expression matching for high-dimensional and dense workloads

Jacobsen

2018

Proc. VLDB Endow.

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Boolean expression matching is an important function for many applications. However, existing solutions still suffer from limitations when applied to high-dimensional and dense workloads. To overcome these limitations, in this paper, we design a data structure called PS-Tree that can efficiently index subscriptions in one dimension. By dividing predicates into disjoint predicate spaces, PS-Tree achieves high matching performance and good expressiveness. Based on PS-Tree, we first propose a Boolean expression matching algorithm PSTBloom. By efficiently filtering out a large proportion of unmatching subscriptions, PSTBloom achieves high matching performance, especially for high-dimensional workloads. PSTBloom also achieves fast index construction and a small memory footprint. Compared with state-of-theart methods, comprehensive experiments show that PSTBloom reduces matching time, index construction time and memory usage by up to 84%, 78% and 94%, respectively. Although PSTBloom is effective for many workload distributions, dense workloads represent new challenges to PSTBloom and other algorithms. To effectively handle dense workloads, we further propose the PSTHash algorithm, which divides subscriptions into disjoint multidimensional predicate spaces. This organization prunes partially matching subscriptions efficiently. Comprehensive experiments on both synthetic and real-world datasets show that PSTHash improves the matching performance by up to 92% for dense workloads.

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Section: Related Workmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Section: Psthash Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PS-tree-based efficient boolean expression matching for high-dimensional and dense workloads

Jacobsen

2018

Proc. VLDB Endow.

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“…For example, the discretization lever of Tama can be configured to tradeoff between matching speed and matching precision [6]. The cache size of Gen is adjustable to balance between matching speed and storage cost [7]. Nevertheless, most existing matching algorithms lack the adaptability to dynamically respond to changing workloads.…”

Section: Introductionmentioning

confidence: 99%

Adjusting Matching Algorithm to Adapt to Dynamic Subscriptions in Content-Based Publish/Subscribe Systems

Mao

Cao

Xue

et al. 2018

2018 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Ubiquitous Computing &Amp; Communications, Big

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When facing fluctuating workloads, can the performance of matching algorithms in a content-based publish/subscribe system be adjusted to adapt to the workloads? In this paper, we explore the idea of endowing matching algorithms with adaptability. The prerequisite for adaptability is to enable the matching algorithm to possess the ability to dynamically and quantitatively adjust its performance. We propose PSAM, a Predicate-Skipping Adjustment Mechanism that realizes dynamic performance adjustment by smoothly switching between exact matching and approximate matching, following the strategy of trading off matching precision in favor of matching speed. The PSAM mechanism is integrated into an existing matching algorithm, resulting in a performance-adjustable matching algorithm called Ada-Rein. To collaborate with Ada-Rein, we design PADA, a Performance Adjustment Decision Algorithm that is able to make proper performance adjustment plans in the presence of fluctuating workloads. The effectiveness of Ada-Rein and PADA is evaluated through a series of experiments based on both synthetic data and real-world stock traces. Experiment results show that adjusting the performance of Ada-Rein at the price of a small false positive rate, less than 0.1%, can shorten event latency by almost 2.1 times, which well demonstrates the feasibility of our exploratory idea.

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Lap: A latency‐aware parallelism framework for content‐based publish/subscribe systems

Zhu

Qian

et al. 2021

Concurrency and Computation

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Summary When large‐scale content‐based publish/subscribe systems face dynamic workloads, it is challenging to stabilize event delivery latency. In this article, we propose a latency‐aware parallelism framework (Lap) to address this challenge. Specifically, we propose a lightweight parallelism method called PhSIH for event matching algorithms. In addition, we design a reactive parallelism degree adjustment (RPDA) mechanism in the backpressure way to determine the parallelism degree. We implement Lap in Apache Kafka and evaluate the parallelism effect of PhSIH and the adaptability of RPDA on trace data. The experiment results demonstrate that PhSIH achieves linear speedup on three existing algorithms and RPDA possesses a desirable adaptability to the dynamic workloads.

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GEM: An analytic geometrical approach to fast event matching for multi-dimensional content-based publish/subscribe services

Cited by 19 publications

References 15 publications

PS-tree-based efficient boolean expression matching for high-dimensional and dense workloads

PS-tree-based efficient boolean expression matching for high-dimensional and dense workloads

Adjusting Matching Algorithm to Adapt to Dynamic Subscriptions in Content-Based Publish/Subscribe Systems

Lap: A latency‐aware parallelism framework for content‐based publish/subscribe systems

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