Cost-efficient enactment of stream processing topologies
The continuous increase of unbound streaming data poses several challenges to established data stream processing engines. One of the most important challenges is the cost-efficient enactment of stream processing topologies under changing data volume. These data volume pose different loads to stream...
| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Makale |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
PeerJ Inc.
2017-12-01
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| Seri Bilgileri: | PeerJ Computer Science |
| Konular: | |
| Online Erişim: | https://peerj.com/articles/cs-141.pdf |
| _version_ | 1828328774452641792 |
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| author | Christoph Hochreiner Michael Vögler Stefan Schulte Schahram Dustdar |
| author_facet | Christoph Hochreiner Michael Vögler Stefan Schulte Schahram Dustdar |
| author_sort | Christoph Hochreiner |
| collection | DOAJ |
| description | The continuous increase of unbound streaming data poses several challenges to established data stream processing engines. One of the most important challenges is the cost-efficient enactment of stream processing topologies under changing data volume. These data volume pose different loads to stream processing systems whose resource provisioning needs to be continuously updated at runtime. First approaches already allow for resource provisioning on the level of virtual machines (VMs), but this only allows for coarse resource provisioning strategies. Based on current advances and benefits for containerized software systems, we have designed a cost-efficient resource provisioning approach and integrated it into the runtime of the Vienna ecosystem for elastic stream processing. Our resource provisioning approach aims to maximize the resource usage for VMs obtained from cloud providers. This strategy only releases processing capabilities at the end of the VMs minimal leasing duration instead of releasing them eagerly as soon as possible as it is the case for threshold-based approaches. This strategy allows us to improve the service level agreement compliance by up to 25% and a reduction for the operational cost of up to 36%. |
| first_indexed | 2024-04-13T20:14:44Z |
| format | Article |
| id | doaj.art-acab2f5f95f14b4fa46fa3e48c15564b |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-13T20:14:44Z |
| publishDate | 2017-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-acab2f5f95f14b4fa46fa3e48c15564b2022-12-22T02:31:44ZengPeerJ Inc.PeerJ Computer Science2376-59922017-12-013e14110.7717/peerj-cs.141Cost-efficient enactment of stream processing topologiesChristoph Hochreiner0Michael Vögler1Stefan Schulte2Schahram Dustdar3Distributed Systems Group, TU Wien, Vienna, AustriaTU Wien, Vienna, AustriaDistributed Systems Group, TU Wien, Vienna, AustriaDistributed Systems Group, TU Wien, Vienna, AustriaThe continuous increase of unbound streaming data poses several challenges to established data stream processing engines. One of the most important challenges is the cost-efficient enactment of stream processing topologies under changing data volume. These data volume pose different loads to stream processing systems whose resource provisioning needs to be continuously updated at runtime. First approaches already allow for resource provisioning on the level of virtual machines (VMs), but this only allows for coarse resource provisioning strategies. Based on current advances and benefits for containerized software systems, we have designed a cost-efficient resource provisioning approach and integrated it into the runtime of the Vienna ecosystem for elastic stream processing. Our resource provisioning approach aims to maximize the resource usage for VMs obtained from cloud providers. This strategy only releases processing capabilities at the end of the VMs minimal leasing duration instead of releasing them eagerly as soon as possible as it is the case for threshold-based approaches. This strategy allows us to improve the service level agreement compliance by up to 25% and a reduction for the operational cost of up to 36%.https://peerj.com/articles/cs-141.pdfData stream processingCloud computingResource elasticityResource optimization |
| spellingShingle | Christoph Hochreiner Michael Vögler Stefan Schulte Schahram Dustdar Cost-efficient enactment of stream processing topologies PeerJ Computer Science Data stream processing Cloud computing Resource elasticity Resource optimization |
| title | Cost-efficient enactment of stream processing topologies |
| title_full | Cost-efficient enactment of stream processing topologies |
| title_fullStr | Cost-efficient enactment of stream processing topologies |
| title_full_unstemmed | Cost-efficient enactment of stream processing topologies |
| title_short | Cost-efficient enactment of stream processing topologies |
| title_sort | cost efficient enactment of stream processing topologies |
| topic | Data stream processing Cloud computing Resource elasticity Resource optimization |
| url | https://peerj.com/articles/cs-141.pdf |
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