Benchmarking Tool for Modern Distributed Stream Processing Engines
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hanif, Muhammad | - |
dc.contributor.author | Yoon, Hyeongdeok | - |
dc.contributor.author | Lee, Choonhwa | - |
dc.date.accessioned | 2022-07-10T14:53:46Z | - |
dc.date.available | 2022-07-10T14:53:46Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019-01 | - |
dc.identifier.issn | 1976-7684 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/148477 | - |
dc.description.abstract | There is an upsurge in the usage and adaptation of streaming applications in the recent years by both industry and academia. At the core of these applications is streaming data processing engines that perform resource management and allocation in order to support continuous track of queries over distributed data streams. Several stream processing engines exists to handle these distributed streaming applications. In this paper, we present different challenges of the stream processing systems, in particular to stateful operators and implement Linear Road benchmark to examine the characteristic and performance metrics of the streaming system, in particular Apache Flink. Furthermore, we examine that Apache Flink can be used as a core for an efficient Linear Road application implementation for distributed environments without breaching the SLA requirements of the application. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Benchmarking Tool for Modern Distributed Stream Processing Engines | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Choonhwa | - |
dc.identifier.doi | 10.1109/ICOIN.2019.8718106 | - |
dc.identifier.scopusid | 2-s2.0-85066761823 | - |
dc.identifier.wosid | 000475888000074 | - |
dc.identifier.bibliographicCitation | International Conference on Information Networking, v.2019-January, pp.393 - 395 | - |
dc.relation.isPartOf | International Conference on Information Networking | - |
dc.citation.title | International Conference on Information Networking | - |
dc.citation.volume | 2019-January | - |
dc.citation.startPage | 393 | - |
dc.citation.endPage | 395 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Acoustic streaming | - |
dc.subject.keywordPlus | Cloud computing | - |
dc.subject.keywordPlus | Data handling | - |
dc.subject.keywordPlus | Distributed computer systems | - |
dc.subject.keywordPlus | Distributed parameter control systems | - |
dc.subject.keywordPlus | Engines | - |
dc.subject.keywordPlus | nformation management | - |
dc.subject.keywordPlus | Roads and streets | - |
dc.subject.keywordPlus | Distributed data streams | - |
dc.subject.keywordPlus | Distributed environments | - |
dc.subject.keywordPlus | Distributed stream processing | - |
dc.subject.keywordPlus | Distributed streaming | - |
dc.subject.keywordPlus | Stream processing engines | - |
dc.subject.keywordPlus | Stream processing systems | - |
dc.subject.keywordPlus | Streaming applications | - |
dc.subject.keywordPlus | Streaming data processing | - |
dc.subject.keywordPlus | Benchmarking | - |
dc.subject.keywordAuthor | Benchmarking | - |
dc.subject.keywordAuthor | Cloud Computing | - |
dc.subject.keywordAuthor | Distributed Computing | - |
dc.subject.keywordAuthor | SLA | - |
dc.subject.keywordAuthor | Streaming | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8718106 | - |
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