TOP-Storm: A topology-based resource-aware scheduler for Stream Processing Engine-Reference-Cited by-同舟云学术

TOP-Storm: A topology-based resource-aware scheduler for Stream Processing Engine

Published:2020-05-06 Issue:1 Volume:24 Page:417-431
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Muhammad Asif,Aleem Muhammad^ORCID,Islam Muhammad Arshad

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

https://link.springer.com/content/pdf/10.1007/s10586-020-03117-y.pdf

Reference31 articles.

1. A. S. Foundation, Apache Storm Documentation [Online]. Available: https://storm.incubator.apache.org/documentation/Home.html. (2014) Accessed 13 Nov2017

2. Apache Software Foundation, S4 Incubation Status—Apache Incubator [Online]. Available: https://incubator.apache.org/projects/s4.html. (2014) Accessed 23 Aug 2019

3. The Apache Software Foundation Apache SparkTM is a unified analytics engine for large-scale data processing, Apache Spark, [Online]. Available: https://spark.apache.org/. (2018) Accessed 23 Aug 2019

4. SQLstream | Streaming SQL Analytics for Kafka & Kinesis—SQLstream provides the power to create streaming Kafka & Kinesis applications with continuous SQL queries to discover, analyze and act on data in real time. [Online]. Available: https://sqlstream.com/. Accessed 06 Sep 2018

5. Illecker, M.: Real-time twitter sentiment classification based on Apache Storm (2015)

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