Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (1693)
- Fachbereich Elektrotechnik und Informationstechnik (719)
- IfB - Institut für Bioengineering (624)
- Fachbereich Energietechnik (589)
- INB - Institut für Nano- und Biotechnologien (557)
- Fachbereich Chemie und Biotechnologie (552)
- Fachbereich Luft- und Raumfahrttechnik (497)
- Fachbereich Maschinenbau und Mechatronik (283)
- Fachbereich Wirtschaftswissenschaften (222)
- Solar-Institut Jülich (165)
Language
- English (4935) (remove)
Document Type
- Article (3285)
- Conference Proceeding (1170)
- Part of a Book (195)
- Book (146)
- Doctoral Thesis (32)
- Conference: Meeting Abstract (29)
- Patent (25)
- Other (10)
- Report (10)
- Conference Poster (6)
Keywords
- Biosensor (25)
- Finite-Elemente-Methode (12)
- Einspielen <Werkstoff> (10)
- CAD (8)
- civil engineering (8)
- Bauingenieurwesen (7)
- Blitzschutz (6)
- FEM (6)
- Gamification (6)
- Limit analysis (6)
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.