Performance Evaluation and Analysis of Multiple Scenarios of Big Data Stream Computing on Storm Platform

doi:10.3837/tiis.2018.07.002

Cited by 2 publications

(1 citation statement)

References 22 publications

(77 reference statements)

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“…Artificial Intelligence (AI) technology is generally focusing on collecting and analyzing vast amounts of data [1]. However, if a problem occurs in collecting some data, it is complicated to find an abnormal point because the data volume is infinitely large [2].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Success Rate-based Sensor Relocation for IoT Applications

2021

KSII TIIS

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Small-sized IoT wireless sensing devices can be deployed with small aircraft such as drones, and the deployment of mobile IoT devices can be relocated to suit data collection with efficient relocation algorithms. However, the terrain may not be able to predict its shape. Mobile IoT devices suitable for these terrains are hopping devices that can move with jumps. So far, most hopping sensor relocation studies have made the unrealistic assumption that all hopping devices know the overall state of the entire network and each device's current state. Recent work has proposed the most realistic distributed network environment-based relocation algorithms that do not require sharing all information simultaneously. However, since the shortest path-based algorithm performs communication and movement requests with terminals, it is not suitable for an area where the distribution of obstacles is uneven. The proposed scheme applies a simple Monte Carlo method based on relay nodes selection random variables that reflect the obstacle distribution's characteristics to choose the best relay node as reinforcement learning, not specific relay nodes. Using the relay node selection random variable could significantly reduce the generation of additional messages that occur to select the shortest path. This paper's additional contribution is that the world's first distributed environment-based relocation protocol is proposed reflecting real-world physical devices' characteristics through the OMNeT++ simulator. We also reconstruct the three days-long disaster environment, and performance evaluation has been performed by applying the proposed protocol to the simulated real-world environment.

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Section: Introductionmentioning

confidence: 99%

Adaptive Success Rate-based Sensor Relocation for IoT Applications

2021

KSII TIIS

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Performance Prediction Method for Stream Computing Platform Based on Time Series

Lü

2021

IEEE Access

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As one of the most popular high-performance data processing technology, existing task and resource scheduling strategies for stream computing platforms are suffering from the problem of triggering hysteresis, which seriously affects the cluster performance. To address this problem, the idea of performance prediction based on timeline series data is proposed. Firstly, the performance variation rule of the stream processing platform is analyzed, which provides a basis for proposing the performance prediction model. Secondly, the basic topology paradigm, periodic load prediction model, and real-time throughput prediction model are proposed as the theoretical foundation for performance prediction. Thirdly, the performance prediction algorithm is proposed to predict the variation trend of the processing load and throughput of the cluster. The processing load is predicted periodically while the throughput of the cluster is predicted in a real-time manner. Finally, the performance evaluation algorithm is proposed to evaluate the cluster performance based on the prediction results and trigger the corresponding scheduling strategies in advance. The experimental results showed that the prediction accuracy of the proposed algorithm meets the requirements in practical applications. Meanwhile, the proposed method improves the performance of stream computing performance by triggering the scheduling strategies in advance.INDEX TERMS Big data, stream computing, performance prediction, processing load, and throughput.

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Performance Evaluation and Analysis of Multiple Scenarios of Big Data Stream Computing on Storm Platform

Cited by 2 publications

References 22 publications

Adaptive Success Rate-based Sensor Relocation for IoT Applications

Adaptive Success Rate-based Sensor Relocation for IoT Applications

Performance Prediction Method for Stream Computing Platform Based on Time Series

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