Enactment of Adaptation in Data Stream Processing with Latency Implications
| dc.contributor.author | Qin, Cui | |
| dc.contributor.author | Eichelberger, Holger | |
| dc.contributor.author | Schmid, Klaus | |
| dc.contributor.editor | Felderer, Michael | |
| dc.contributor.editor | Hasselbring, Wilhelm | |
| dc.contributor.editor | Rabiser, Rick | |
| dc.contributor.editor | Jung, Reiner | |
| dc.date.accessioned | 2020-02-03T13:03:58Z | |
| dc.date.available | 2020-02-03T13:03:58Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | This summary refers to the paper Enactment of adaptation in data stream processing with latency implications – A systematic literature review. This paper is a journal paper published in Information and Software Technology (IST) in July 2019. Runtime adaptation in stream processing plays a significant role in supporting the optimization of data processing tasks. In recent years, runtime adaptation, particularly its enactment, has received significant interest in scientific literature. However, so far no categorization of the enactment approaches for runtime adaptation in stream processing has been established. This paper presents a systematic literature review (SLR), where we identify and characterize different approaches towards the enactment of runtime adaptation in stream processing with a main focus on latency as quality dimension. We discovered 75 relevant papers out of 244 papers from the search. We identified 17 different enactment categories and developed a taxonomy to characterize all possible enactment approaches. We extracted the realization techniques of each identified enactment approach and classified them into categories. Furthermore, we identified 9 categories of processing problems, 6 adaptation goals, 9 evaluation metrics and 12 evaluation parameters from the identified enactment approaches. The research interest on enactment approaches has significantly increased in recent years. The most commonly applied enactment approaches are parameter adaptation to tune parameters or settings of the processing, load balancing used to re-distribute workloads, and processing scaling to dynamically scale up and down the processing. | en |
| dc.identifier.doi | 10.18420/SE2020_09 | |
| dc.identifier.isbn | 978-3-88579-694-7 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/31754 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Software Engineering 2020 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-300 | |
| dc.subject | Stream processing | |
| dc.subject | Big Data | |
| dc.subject | Runtime Adaptation | |
| dc.subject | Enactment | |
| dc.subject | Latency | |
| dc.subject | Systematic Literature | |
| dc.subject | Review | |
| dc.title | Enactment of Adaptation in Data Stream Processing with Latency Implications | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | ||
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 41 | |
| gi.conference.date | 24.-28. Feburar 2020 | |
| gi.conference.location | Innsbruck, Austria | |
| gi.conference.sessiontitle | Domänen-spezifische Softwareentwicklung |
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