NGS: Performance Mining of Large-Scale Data-Intensive Distributed Object Applications

EIA-0103708 Mohammed J. ZakiRensselaer Polytechnic InstituteNGS Performance Mining of large-scale Data-Intensive Distributed Object Applications该提案的目标是开发一个基于性能测量的运行时环境，用于支持大型数据密集型分布式对象应用程序。 该系统将通过性能数据挖掘、关键路径发现和推测执行的结合提供连续的、自适应的性能优化，为了解决下一代软件系统面临的这些挑战，我们提出开发性能挖掘引擎PERFMINER。 PERFMINER是一个通过挖掘进行持续性能优化的系统，它将使分布式对象系统能够：1）发现自己的关键路径，2）检测推测处理的新机会，以及3）促进修改对象的行为（即，方法）在运行时响应于新获得的知识。

EIA-0103708Mohammed J. ZakiRensselaer Polytechnic InstituteNGS Performance Mining of large-scale Data-Intensive Distributed Object ApplicationsThe objective of the proposal is to develop a performance measurements-based run-time environment for supporting large data-intensive distributed object applications. The system will provide continuous and adaptive performance optimization via a combination of performance data mining, critical path discovery and speculative execution.To address these challenges for next generation software systems we propose to develop the PERFMINER engine for the performance mining. PERFMINER, a system for continuous performance optimization via mining, will enable a distributed object system to: 1) discover its own critical path, 2) detect new opportunities for speculative processing, and 3) to facilitate modifying an object's behaviors (i.e., methods) at run-time in response to newly acquired knowledge.