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|Year=2014 | |Year=2014 | ||
|Month=Januar | |Month=Januar | ||
| − | |Booktitle=Proceedings of the | + | |Booktitle=Proceedings of the 47th Hawai'i International Conference on System Sciences (HICSS) |
| − | |Pages= | + | |Pages=3818-3826 |
|Organization=IEEE | |Organization=IEEE | ||
|Publisher=IEEE | |Publisher=IEEE | ||
|Address=Waikoloa, HI | |Address=Waikoloa, HI | ||
|Editor=IEEE | |Editor=IEEE | ||
| + | |Note=conditionally accepted | ||
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{{Publikation Details | {{Publikation Details | ||
|Abstract=With few exceptions, the opportunities cloud com-puting offers to business process management (BPM) technologies have been neglected so far. We investi-gate opportunities and challenges of implementing a BPM-aware cloud architecture for the benefit of pro-cess runtime optimization. Processes with predomi-nantly automated tasks such as data transformation processes are key targets for this runtime optimization. In theory, off-the-shelf mechanisms offered by cloud providers, such as horizontal scaling, should already provide as much computational resources as necessary for a process to execute in a timely fashion. However, we show that making process data available to scaling decisions can significantly improve process turnaround time and better cater for the needs of BPM. We present a model and method of cloud-aware business process optimization which provides computational resources based on process knowledge. We describe a performance measurement experiment and evaluate it against the performance of a standard automatic horizontal scaling controller to demonstrate its potential. | |Abstract=With few exceptions, the opportunities cloud com-puting offers to business process management (BPM) technologies have been neglected so far. We investi-gate opportunities and challenges of implementing a BPM-aware cloud architecture for the benefit of pro-cess runtime optimization. Processes with predomi-nantly automated tasks such as data transformation processes are key targets for this runtime optimization. In theory, off-the-shelf mechanisms offered by cloud providers, such as horizontal scaling, should already provide as much computational resources as necessary for a process to execute in a timely fashion. However, we show that making process data available to scaling decisions can significantly improve process turnaround time and better cater for the needs of BPM. We present a model and method of cloud-aware business process optimization which provides computational resources based on process knowledge. We describe a performance measurement experiment and evaluate it against the performance of a standard automatic horizontal scaling controller to demonstrate its potential. | ||
| + | |Projekt=DAAD PPP Australia (Sydney) | ||
|Forschungsgruppe=Ökonomie und Technologie der eOrganisation | |Forschungsgruppe=Ökonomie und Technologie der eOrganisation | ||
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| + | {{Forschungsgebiet Auswahl | ||
| + | |Forschungsgebiet=Cloud Computing | ||
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| + | {{Forschungsgebiet Auswahl | ||
| + | |Forschungsgebiet=Business Activity Management | ||
| + | }} | ||
| + | {{Forschungsgebiet Auswahl | ||
| + | |Forschungsgebiet=Geschäftsprozessmanagement | ||
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Aktuelle Version vom 15. Januar 2014, 10:51 Uhr
Optimizing the Performance of Automated Business Processes Executed on Virtualized Infrastructure
Optimizing the Performance of Automated Business Processes Executed on Virtualized Infrastructure
Published: 2014
Januar
Herausgeber: IEEE
Buchtitel: Proceedings of the 47th Hawai'i International Conference on System Sciences (HICSS)
Seiten: 3818-3826
Verlag: IEEE
Erscheinungsort: Waikoloa, HI
Organisation: IEEE
Referierte Veröffentlichung
Note: conditionally accepted
BibTeX
Kurzfassung
With few exceptions, the opportunities cloud com-puting offers to business process management (BPM) technologies have been neglected so far. We investi-gate opportunities and challenges of implementing a BPM-aware cloud architecture for the benefit of pro-cess runtime optimization. Processes with predomi-nantly automated tasks such as data transformation processes are key targets for this runtime optimization. In theory, off-the-shelf mechanisms offered by cloud providers, such as horizontal scaling, should already provide as much computational resources as necessary for a process to execute in a timely fashion. However, we show that making process data available to scaling decisions can significantly improve process turnaround time and better cater for the needs of BPM. We present a model and method of cloud-aware business process optimization which provides computational resources based on process knowledge. We describe a performance measurement experiment and evaluate it against the performance of a standard automatic horizontal scaling controller to demonstrate its potential.
Ökonomie und Technologie der eOrganisation
Cloud Computing, Business Activity Management, Geschäftsprozessmanagement