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Hour minimization based resource allocation for deadline constrained scientific workflow application in cloud computing

机译：基于小时最小化的资源分配，用于期限受限的科学工作流在云计算中的应用

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Nowadays, the demands for Workflow applications are blooming increasingly to address the resources and VM instance hour minimization issues. Some provoking constraints acting as a breakthrough has steered us towards investigating the secret's of cloud dominance such as (1) to achieve end-to-end deadlines; (2) Minimized VM instance allotment to a particular application; (3) Minimizing the application's makespan by establishing application performance schedule; (4) Determining a VM process schedule. Initially, it is necessary to determine the lower and upper bounds for end-to-end deadliness. In response to that, we develop a heuristic Slack-Distance Minimization (SDM) algorithm for ensuring the first two conditions. After determining the application performance schedule and appropriate VM instances, it is necessary to minimize the instance hours by using our proposed Resource Hour Minimization (RHM) algorithm. The investigational outcome proves that the proposed SDM algorithm is an outstanding one when compared to other algorithms like HEFT and MOHEFT. Furthermore, the comparisons were made in the presence and absence of the RHM algorithm for ensuring the minimized execution instance hours.
机译：如今，对工作流应用程序的需求日益增长，以解决资源和VM实例小时最小化问题。某些突破性的限制条件使我们转向调查云优势的秘密，例如（1）达到端到端的期限; （2）最小化分配给特定应用程序的VM实例; （3）通过建立应用程序性能进度表来最小化应用程序的制造时间; （4）确定VM进程计划。最初，有必要确定端到端期限的下限和上限。响应于此，我们开发了一种启发式的松弛距离最小化（SDM）算法，以确保满足前两个条件。确定应用程序性能计划和适当的VM实例后，有必要使用我们建议的资源小时最小化（RHM）算法来最小化实例时间。研究结果证明，与其他算法（例如HEFT和MOHEFT）相比，所提出的SDM算法是一种出色的算法。此外，在有和没有RHM算法的情况下进行了比较，以确保执行实例时间最小化。

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