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该提案旨在为格鲁吉亚理工学院计算机系统实验研究中心提供资金。基础研究的资金申请由NSF批准的招标（NSF 10 - 601）授权。 征集邀请I/UCRC提交支持行业定义的基础研究的提案。 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理解，特别是对于电子商务和社交网络等领域常见的N层应用程序。 拟议的研究将寻求系统地设计大规模的实验，从性能数据将得出和性能指标建立N层应用程序。 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的最佳分配的方法。 拟议的比较实验研究将使比较模型的发展，通过N-层应用程序的性能可以预测，因此，在理解这类问题的云性能方面有可能取得重大突破。拟议的研究有可能开发工具，使云资源的行业提供商能够更好地管理其资源并以成本提供服务有效的方式。 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，并有可能通过使用本研究生成的数据集进行的许多研究和建模工作来扩展中心的投资组合。除了中心之外，如果设计得当，数据集作为这一领域研究的资源，有可能在研究界产生广泛的影响。 该提案还为学生和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.