Szczegóły publikacji
Opis bibliograficzny
Cost optimization of execution of multi-level deadline-constrained scientific workflows on clouds / Maciej MALAWSKI, Kamil FIGIELA, Marian BUBAK, Ewa Deelman, Jarek Nabrzyski // W: Parallel Processing and Applied Mathematics : 10th international conference, PPAM 2013 : Warsaw, Poland, September 8–11, 2013 : revised selected papers, Pt. 1 / eds. Roman Wyrzykowski, [et al.]. — Berlin ; Heidelberg : Springer-Verlag, cop. 2014. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; 8384). — ISBN: 978-3-642-55223-6; e-ISBN: 978-3-642-55224-3. — S. 251–260. — Bibliogr. s. 259–260, Abstr.
Autorzy (5)
- AGHMalawski Maciej
- AGHFigiela Kamil
- AGHBubak Marian
- Deelman Ewa
- Nabrzyski Jarek
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 84684 |
|---|---|
| Data dodania do BaDAP | 2014-10-07 |
| DOI | 10.1007/978-3-642-55224-3_24 |
| Rok publikacji | 2014 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | Parallel Processing and Applied Mathematics : 10th international conference |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
This paper introduces a cost optimization model for scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous VM instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a Cloud Object Store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified in AMPL modeling language and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications such as Montage, Epigenomics, LIGO. We indicate how this model can be used for scenarios that require resource planning for scientific workflows and their ensembles.