FRP: An Experimental Comparative Study of N-Tier Application Performance in Computational Clouds

计算机系统实验研究中心提案#1127904本提案为佐治亚理工学院计算机系统实验研究中心寻求资助。基础研究的资金申请由NSF批准的征求授权，NSF 10-601。本次招标邀请I/ ucrc提交支持行业定义基础研究的提案。虽然云计算对许多应用程序的重要性正在上升，但仍然缺乏对不同配置可实现的性能的基本理解，特别是对于电子商务和社交网络等领域中常见的n层应用程序。拟议的研究将寻求系统地设计大规模实验，从中获得性能数据，并为n层应用建立性能指标。由此产生的大型数据集可以使研究人员探索实现特定应用的硬件和软件资源最佳分配的方法。提出的比较实验研究将使比较模型的开发成为可能，通过这些模型可以预测n层应用程序的性能，因此有机会在理解这类问题的云性能方面取得重大突破。拟议的研究有可能为开发工具提供资源，使云资源的行业提供商能够更好地管理其资源并以具有成本效益的方式提供服务。此外，这种优化可以应用于实现绿色IT目标。这项工作得到了中心个别行业成员的大力支持，并有可能扩展中心的投资组合，因为使用本研究生成的数据集可以进行许多研究和建模工作。在中心之外，如果设计得当，该数据集作为该领域研究的资源，在研究界具有广泛影响的潜力。该提案进一步为学生和UREP的参与提供了一个坚实的计划。

Center for Experimental Research in Computer Systems Proposal #1127904This proposal seeks funding for the Center for Experimental Research in Computer Systems at Georgia Institute of Technology. Funding Requests for Fundamental Research are authorized by an NSF approved solicitation, NSF 10-601. The solicitation invites I/UCRCs to submit proposals for support of industry-defined fundamental research. While cloud computing is rising in importance for numerous applications, there remains a fundamental lack of understanding of performance achievable for different configurations, especially for N-tier applications common in such areas as e-commerce and social networking. The proposed research will seek to systematically design large scale experiments from which performance data will be derived and performance metrics established for N-tier applications. The resulting large data sets can enable researchers to explore means to achieve optimal allocation of hardware and software resources for specific applications. The proposed comparative experimental study will enable development of comparative models through which N-tier application performance can be predicted and as such holds the opportunity for significant breakthroughs in understanding of cloud performance for this class of problems.The proposed research has the potential to source the development of tools from which industry providers of cloud resources can better manage their resources and offer services in a cost effective way. Additionally, this optimization can be applied to achievement of Green IT goals. The work is well supported by center individual industry members and has the potential to extend the portfolio of the center by virtue of the many studies and modeling efforts achievable using the dataset generated by this study. Beyond the center, the dataset, if properly designed, has the potential for broad impact in the research community as a resource for studies in this area. The proposal furthermore provides a solid plan for student and UREP involvement.