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المؤلفAurangzaib, Rana
المؤلفIqbal, Waheed
المؤلفAbdullah, Muhammad
المؤلفBukhari, Faisal
المؤلفUllah, Faheem
المؤلفErradi, Abdelkarim
تاريخ الإتاحة2023-04-10T09:10:04Z
تاريخ النشر2022
اسم المنشورProceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/CloudCom55334.2022.00014
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41803
الملخصWith the widespread usage of IoT, processing data streams in real-time have become very important. The traditional data-stream processing systems are inefficient in processing big data for detecting anomalies, classifications, clustering, and prediction in real-time using minimal resources. In this paper, we address this limitation by proposing a scalable pipeline for real-time processing of big data streams. Our proposed solution is capable of dynamically managing resources for different components of the pipeline using automatic scaling. The pipeline is containerized and deployed on a Kubernetes cluster. The proposed scalable pipeline is evaluated using a case study of anomaly detection in IoT data. The proposed solution yields a x 1.31 to x 2.4 increase in throughput, and x 32 to x 80 decreased latency compared to the commonly used static resource allocation strategy for data pipelines. 2022 IEEE.
اللغةen
الناشرIEEE Computer Society
الموضوعAnomaly detection
Auto-scaling
Big Data Analytics
Data Pipeline
IoT
Real-time
العنوانScalable Containerized Pipeline for Real-time Big Data Analytics
النوعConference Paper
الصفحات25-32
رقم المجلد2022-December
dc.accessType Abstract Only


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