RDMA-Based Apache Storm for High-Performance Stream Data Processing

2021 ◽  
Author(s):  
Ziyu Zhang ◽  
Zitan Liu ◽  
Qingcai Jiang ◽  
Junshi Chen ◽  
Hong An
Keyword(s):  
Data Processing ◽  
High Performance ◽  
Stream Data ◽  
Stream Data Processing ◽  
Apache Storm

RDMA-Based Apache Storm for High-Performance Stream Data Processing

2021 ◽  
pp. 276-287
Author(s):  
Ziyu Zhang ◽  
Zitan Liu ◽  
Qingcai Jiang ◽  
Zheng Wu ◽  
Junshi Chen ◽  
...  
Keyword(s):  
Data Processing ◽  
High Performance ◽  
Stream Data ◽  
Stream Data Processing ◽  
Apache Storm

scholarly journals Performance Assay of Big IoT Data Analytics Framework

2019 ◽  
Vol 8 (4) ◽  
pp. 8593-8596
Keyword(s):  
Internet Of Things ◽  
Data Processing ◽  
High Velocity ◽  
Data Analytics ◽  
Real Life ◽  
Global Environment ◽  
Stream Data ◽  
Processing Level ◽  
Stream Analytics ◽  
Apache Storm

Evaluation of Internet of Things (IoT) technologies in real life has scaled the enumeration of data in huge volumes and that too with high velocity, and thus a new issue has come into picture that is of management & analytics of this BIG IOT STREAM data. In order to optimize the performance of the IoT Machines and services provided by the vendors, industry is giving high priority to analyze this big IoT Stream Data for surviving in the competitive global environment. Thses analysis are done through number of applications using various Data Analytics Framework, which require obtaining the valuable information intelligently from a large amount of real-time produced data. This paper, discusses the challenges and issues faced by distributed stream analytics frameworks at the data processing level and tries to recommend a possible a Scalable Framework to adapt with the volume and velocity of Big IoT Stream Data. Experiments focus on evaluating the performance of three Distributed Stream Analytics Here Analytics frameworks, namely Apache Spark, Splunk and Apache Storm are being evaluated over large steam IoT data on latency & throughput as parameters in respect to concurrency. The outcome of the paper is to find the best possible existing framework and recommend a possible scalable framework.

Reliable stream data processing for elastic distributed stream processing systems

2019 ◽  
Vol 23 (2) ◽  
pp. 555-574 ◽  
Author(s):  
Xiaohui Wei ◽  
Yuan Zhuang ◽  
Hongliang Li ◽  
Zhiliang Liu
Keyword(s):  
Data Processing ◽  
Stream Processing ◽  
Stream Data ◽  
Stream Data Processing ◽  
Distributed Stream Processing
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