NGS: Grid Harvest Service (GHS): A Performance System for Grid Computing

网格计算带来了地理上分散的计算能力，以满足不断增长的需求。 网格是一个全局共享、异构、自主控制的计算平台，代表着未来的计算平台。 它比过去的计算平台更先进、更强大、更动态、更复杂。 这种复杂性需要新的系统软件技术来有效地利用其计算能力。 大多数现有的性能技术都针对专用平台。最近的性能工具，如NWS，只预测短期（少于五分钟）的资源可用性，这是不适合长期的应用程序。 为了减轻网格的复杂性，需要新的软件技术来进行长期的、应用级的性能预测和任务调度。初步结果表明，全球统一制度在长期应用方面从根本上优于现有制度，并可导致计算成本大幅降低。项目负责人将与美国能源部国家实验室和印度理工学院的研究人员合作，展示全球统一制度在重要国家利益应用方面的巨大潜力。研究方法是基于观察，网格环境没有中央控制和性能效率必须基于资源的可用性。GHS将利用这一观察结果。 特别是该项目：1）设计、实现和验证随机和分析模型，以预测计算和通信资源的可用性及其对用户应用程序的影响。 2)将开发，实施和验证实用和非侵入性的性能测量技术。 3)将设计、实现和验证任务调度和重新调度算法，以利用（1）中给出的预测来减少用户应用程序运行时间。

Grid computing brings geographically dispersed computing power to meet the ever-increasing demands. However, representing new and future computing platforms, Grid is a globally shared, heterogeneous, and autonomous controlled platform. It is far more advanced, powerful, dynamic and complex than computing platforms in he past. Such complexity requires new system software technology to efficiently utilize its computing capacity. Most existing performance technologies are targeted for dedicated platforms. Recent performance facilities, such as NWS, only predict short-term (less than five minutes) resource availability, which is not appropriate for long-term applications. New software technologies are needed for long-term, application-level performance predication and task scheduling to alleviate the complexity of Grid. Preliminary results show that GHS is fundamentally better than existing systems for long-term applications and can lead to substantial decrease in computing cost. The PIs will collaborate with researchers at DOE national laboratories and IIT to demonstrate the great potential of GHS with important national interest applications. The research approach is based on the observation that Grid environments do not have central control and performance efficiency has to be based on resource availability. GHS will exploit this observation. In particular the project: 1) will design, implement, and validate stochastic and analytical models to predict the computing and communication resource availability and their influence on user applications. 2) will develop, implement, and validate practical and non-intrusive performance measurement technologies. 3) will design, implement, and validate task scheduling and rescheduling algorithms to utilize the prediction given in (1) to reduce user application run-times.