Back to Search Start Over

Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies

Authors :
Leonidas Theodorakopoulos
Aristeidis Karras
Alexandra Theodoropoulou
Georgios Kampiotis
Source :
Technologies, Vol 12, Iss 11, p 217 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. We demonstrate our results which show the capability of each system for real-world graph applications, and hence, providing a quantitative understanding to select the system for our purpose. GraphX’s strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. These performance characteristics emphasize how the capabilities of graph processing systems can match the requirements for the performance of different emerging technology applications. Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains.

Details

Language :
English
ISSN :
22277080
Volume :
12
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Technologies
Publication Type :
Academic Journal
Accession number :
edsdoj.ba61a30d0c1c4c03acc180e4034ca021
Document Type :
article
Full Text :
https://doi.org/10.3390/technologies12110217