Elasticity is considered to be the most beneficial characteristic of cloud environments, which distinguishes the cloud from clusters and grids. Whereas elasticity has become mainstream for web-based, interactive applications, it is still a major research challenge how to leverage elasticity for applications from the high-performance computing (HPC) domain, which heavily rely on efficient parallel processing techniques. In this work, we specifically address the challenges of elasticity for parallel tree search applications. Well-known meta-algorithms based on this parallel processing technique include branch-and-bound and backtracking search. We show that their characteristics render static resource provisioning inappropriate and the capability of elastic scaling desirable. Moreover, we discuss how to construct an elasticity controller that reasons about the scaling behavior of a parallel system at runtime and dynamically adapts the number of processing units according to user-defined cost and efficiency thresholds. We evaluate a prototypical elasticity controller based on our findings by employing several benchmarks for parallel tree search and discuss the applicability of the proposed approach. Our experimental results show that, by means of elastic scaling, the performance can be controlled according to user-defined thresholds, which cannot be achieved with static resource provisioning.

中文翻译：

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Equilibrium：云中并行树搜索的弹性控制器

弹性被认为是云环境最有利的特性，它将云与集群和网格区分开来。尽管弹性已成为基于 Web 的交互式应用程序的主流，但如何为高度依赖高效并行处理技术的高性能计算 (HPC) 领域的应用程序利用弹性仍然是一个主要的研究挑战。在这项工作中，我们专门解决了并行树搜索应用程序的弹性挑战。基于这种并行处理技术的著名元算法包括分支定界和回溯搜索。我们展示了它们的特性使得静态资源供应不合适，并且弹性扩展的能力是可取的。而且，我们讨论了如何构建一个弹性控制器，该控制器在运行时对并行系统的扩展行为进行推理，并根据用户定义的成本和效率阈值动态调整处理单元的数量。我们通过采用并行树搜索的几个基准来评估基于我们的发现的原型弹性控制器，并讨论所提出方法的适用性。我们的实验结果表明，通过弹性扩展，可以根据用户定义的阈值控制性能，这是静态资源配置无法实现的。我们通过采用并行树搜索的几个基准来评估基于我们的发现的原型弹性控制器，并讨论所提出方法的适用性。我们的实验结果表明，通过弹性扩展，可以根据用户定义的阈值控制性能，这是静态资源配置无法实现的。我们通过采用并行树搜索的几个基准来评估基于我们的发现的原型弹性控制器，并讨论所提出方法的适用性。我们的实验结果表明，通过弹性扩展，可以根据用户定义的阈值控制性能，这是静态资源配置无法实现的。