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RWA-Einbau in Gebäudewänden
(1999)
Lufttechnische Prüfungen von Abzügen - Kritische Anmerkungen zur Prüfvorschrift DIN 12 924 Teil 1
(1994)
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.
The importance of validating and reproducing the outcome of computational processes is fundamental to many application domains. Assuring the provenance of workflows will likely become even more important with respect to the incorporation of human tasks to standard workflows by emerging standards such as WS-HumanTask. This paper addresses this trend by an actor-based workflow approach that actively support provenance. It proposes a framework to track and store provenance information automatically that applies for various workflow management systems. In particular, the introduced provenance framework supports the documentation of workflows in a legally binding way. The authors therefore use the concept of layered XML documents, i.e. history-tracing XML. Furthermore, the proposed provenance framework enables the executors (actors) of a particular workflow task to attest their operations and the associated results by integrating digital XML signatures.