Towards a standard event processing benchmark

Mendes, Marcelo R.N.; Bizarro, Pedro; Marques, Paulo

doi:10.1145/2479871.2479913

Cited by 12 publications

(2 citation statements)

References 3 publications

(3 reference statements)

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“…-Benchmarks of CER engines, where there is a lack of industry-wide and well-accepted benchmarks 1 . For instance, efforts like BiCEP [124] were not completed. -Benchmarking combinations of the above based on criteria such as those presented in Table 2.…”

Section: Open Issues In Cer On Big Data Platformsmentioning

confidence: 99%

Complex event recognition in the Big Data era: a survey

et al. 2019

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The concept of event processing is established as a generic computational paradigm in various application fields. Events report on state changes of a system and its environment. Complex Event Recognition (CER) refers to the identification of composite events of interest, which are collections of simple, derived events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of anomalies in maritime surveillance, electronic fraud, cardiac arrhythmias and epidemic spread. This survey elaborates on the whole pipeline from the time CER queries are expressed in the most prominent languages, to algorithmic toolkits for scaling-out CER to clustered and geo-distributed architectural settings. We also highlight future research directions.

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Section: Open Issues In Cer On Big Data Platformsmentioning

confidence: 99%

Complex event recognition in the Big Data era: a survey

et al. 2019

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“…They extensively hand‐tuned the end‐to‐end data path of Borealis stream processing system. Another notable work of CEP benchmarking has been conducted by Mendes et al that is called BiCEP benchmark suite . They introduced Pairs benchmark that is aimed at evaluating the ability of CEP engines in processing progressively larger volumes of events and simultaneous queries within real‐time constraints.…”

Section: Related Workmentioning

confidence: 99%

Energy consumption analysis of data stream processing: a benchmarking approach

Dayarathna

Wen

et al. 2016

Softw Pract Exp

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Summary Energy efficiency of data analysis systems has become a very important issue in recent times because of the increasing costs of data center operations. Although distributed streaming workloads have increasingly been present in modern data centers, energy‐efficient scheduling of such applications remains as a significant challenge. In this paper, we conduct an energy consumption analysis of data stream processing systems in order to identify their energy consumption patterns. We follow stream system benchmarking approach to solve this issue. Specifically, we implement Linear Road benchmark on six stream processing environments (S4, Storm, ActiveMQ, Esper, Kafka, and Spark Streaming) and characterize these systems' performance on a real‐world data center. We study the energy consumption characteristics of each system with varying number of roads as well as with different types of component layouts. We also use a microbenchmark to capture raw energy consumption characteristics. We observed that S4, Esper, and Spark Streaming environments had highest average energy consumption efficiencies compared with the other systems. Using a neural networkbased technique with the power/performance information gathered from our experiments, we developed a model for the power consumption behavior of a streaming environment. We observed that energy‐efficient execution of streaming application cannot be specifically attributed to the system CPU usage. We observed that communication between compute nodes with moderate tuple sizes and scheduling plans with balanced system overhead produces better power consumption behaviors in the context of data stream processing systems. Copyright © 2016 John Wiley & Sons, Ltd.

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Complex Event Processing in ThingML

Lam

Haugen

2016

System Analysis and Modeling. Technology-Specific Aspects of Models

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Towards a standard event processing benchmark

Cited by 12 publications

References 3 publications

Complex event recognition in the Big Data era: a survey

Complex event recognition in the Big Data era: a survey

Energy consumption analysis of data stream processing: a benchmarking approach

Complex Event Processing in ThingML

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