Abstract
Large datasets and time-consuming processes have become the norm in scientific computing applications. The exploration phase in the development of scientific workflows involves trial-and-error with workflow components, which can take a lot of time given the time-consuming nature of the workflow tasks. These facts suggest the possibility of reducing the development time by reusing intermediate data whenever possible. However the storage space is always limited. This introduces a problem: which intermediate datasets from one workflow should be kept to be reused in another workflow, with a limited amount of storage. For the general class of series parallel graphs, we model this problem using a non-linear integer programming formulation and show that it is NP-Hard. We provide a branch and bound optimal algorithm as well as efficient heuristics. We conducted experiments over a large set of randomly-generated workflows as well as a smaller set of synthetic workflows which are based on real-world workflows used by scientists in different disciplines. Our experiments show that the best solution produced by the heuristics only differs from the optimal value by less than 1% on average.
Original language | English (US) |
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Title of host publication | Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium, IPDPS 2013 |
Pages | 1051-1062 |
Number of pages | 12 |
DOIs | |
State | Published - 2013 |
Event | 27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013 - Boston, MA, United States Duration: May 20 2013 → May 24 2013 |
Other
Other | 27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013 |
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Country/Territory | United States |
City | Boston, MA |
Period | 5/20/13 → 5/24/13 |
Keywords
- Data Reuse
- Intermediate Data
- Scientific Workflows
- Series-Parallel
ASJC Scopus subject areas
- Software