Reference : Benchmarking Synchronous and Asynchronous Stream Processing Systems

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/45325

Title :	Benchmarking Synchronous and Asynchronous Stream Processing Systems
Language :	English
Author, co-author :	Ellampallil Venugopal, Vinu [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Theobald, Martin [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Publication date :	2-Jan-2020
Main document title :	Benchmarking Synchronous and Asynchronous Stream Processing Systems
Author, co-author :	Ellampallil Venugopal, Vinu
	Theobald, Martin
Publisher :	ACM
Pages :	322-323
Peer reviewed :	Yes
On invitation :	Yes
Audience :	International
City :	Esch-sur-Alzette
Country :	Luxembourg
Event name :	7th ACM IKDD CoDS and 25th COMAD
Event date :	from 02-01-2020 to 04-01-2020
Event organizer :	ACM
Keywords :	[en] Stream data processing ; Big Data ; sustainable-throughput
Abstract :	[en] Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous progress in distributed stream processing systems in the past few years, the high-throughput and low-latency (a.k.a. high sustainable-throughput) requirement of modern applications is pushing the limits of traditional data processing infrastructures. To understand the upper bound of the maximum sustainable throughput that is possible for a given node configuration, we have designed multiple hard-coded multi-threaded processes (called ad-hoc dataflows) in C++ using Message Passing Interface (MPI) and Pthread libraries. Our preliminary results show that our ad-hoc design on average is 5.2 times better than Flink and 9.3 times better than Spark.
Funders :	University of Luxembourg - UL
Target :	Researchers ; General public
Permalink :	http://hdl.handle.net/10993/45325
DOI :	10.1145/3371158.3371206
Other URL :	https://dl.acm.org/doi/abs/10.1145/3371158.3371206

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