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|Year=2014
 
|Year=2014
 
|Month=Januar
 
|Month=Januar
|Booktitle=Proceedings of the 45rd Hawai'i International Conference on System Sciences (HICSS)
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|Booktitle=Proceedings of the 47th Hawai'i International Conference on System Sciences (HICSS)
|Pages=1-10
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|Pages=3818-3826
 
|Organization=IEEE
 
|Organization=IEEE
 
|Publisher=IEEE
 
|Publisher=IEEE
 
|Address=Waikoloa, HI
 
|Address=Waikoloa, HI
 
|Editor=IEEE
 
|Editor=IEEE
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|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.
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|Projekt=DAAD PPP Australia (Sydney)
 
|Forschungsgruppe=Ökonomie und Technologie der eOrganisation
 
|Forschungsgruppe=Ökonomie und Technologie der eOrganisation
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{{Forschungsgebiet Auswahl
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|Forschungsgebiet=Cloud Computing
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{{Forschungsgebiet Auswahl
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|Forschungsgebiet=Business Activity Management
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{{Forschungsgebiet Auswahl
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|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 VeröffentlichungNote: 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.


Projekt

DAAD PPP Australia (Sydney)



Forschungsgruppe

Ökonomie und Technologie der eOrganisation


Forschungsgebiet

Cloud Computing, Business Activity Management, Geschäftsprozessmanagement