Refine
Year of publication
Document Type
- Article (1578)
- Conference Proceeding (239)
- Book (96)
- Part of a Book (59)
- Doctoral Thesis (27)
- Patent (17)
- Report (15)
- Other (8)
- Habilitation (4)
- Lecture (3)
- Preprint (3)
- Course Material (1)
- Review (1)
- Talk (1)
Keywords
- Biosensor (25)
- Finite-Elemente-Methode (16)
- CAD (15)
- civil engineering (14)
- Bauingenieurwesen (13)
- Einspielen <Werkstoff> (13)
- shakedown analysis (9)
- FEM (6)
- Limit analysis (6)
- Shakedown analysis (6)
- limit analysis (6)
- Clusterion (5)
- Air purification (4)
- Einspielanalyse (4)
- Hämoglobin (4)
- LAPS (4)
- Lipopolysaccharide (4)
- Luftreiniger (4)
- Natural language processing (4)
- Plasmacluster ion technology (4)
Institute
- Fachbereich Medizintechnik und Technomathematik (2052) (remove)
HisT/PLIER : A Two-Fold Provenance Approach for Grid-Enabled Scientific Workflows Using WS-VLAM
(2011)
An increasing number of applications target their executions on specific hardware like general purpose Graphics Processing Units. Some Cloud Computing providers offer this specific hardware so that organizations can rent such resources. However, outsourcing the whole application to the Cloud causes avoidable costs if only some parts of the application benefit from the specific expensive hardware. A partial execution of applications in the Cloud is a tradeoff between costs and efficiency. This paper addresses the demand for a consistent framework that allows for a mixture of on- and off-premise calculations by migrating only specific parts to a Cloud. It uses the concept of workflows to present how individual workflow tasks can be migrated to the Cloud whereas the remaining tasks are executed on-premise.
Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.