CSR: Medium: Collaborative Research: Architecting Performance Sensitive Applications for the Cloud

云计算为IT组织提供了部署地理分布和高度可扩展的应用程序的能力，同时提供了极具吸引力的成本节约优势。然而，构建、配置和调整云应用程序（延迟敏感的Web应用程序和批量数据处理应用程序）满足其严格的性能要求是一项挑战，因为云平台具有丰富的配置选项集、共享的多租户特性以及计划维护等活动带来的动态性。该项目正在开发新颖的方法、算法，以及能够使应用程序架构师能够（1）明智地跨多个云数据中心架构应用程序，同时考虑应用程序性能要求、成本节约目标以及由云组件的性能和成本模型指导的云定价方案的系统;（2）通过统计机器学习技术自动学习有效的应用配置和配置-性能预测模型;以及（3）能够通过在较短时间尺度上的事务重新分配来适应云环境中的持续动态的可重用应用，以及更长时间范围内的应用程序迁移。这项研究的影响是多方面的：（1）使IT组织能够通过优化将其运营迁移到云来显著降低成本;（2）基于运营部署的应用程序创建基准，并收集工作负载跟踪，这些跟踪将提供给研究社区;（3）使开发的算法和系统作为开源软件广泛使用;（4）为泰国全国医疗保健云的设计提供信息;（4）在本科生和研究生课程中引入云计算相关主题;培养多名博士，医学硕士，和本科生，并明确努力招募和培训来自代表性不足的少数群体的学生。

Cloud computing offers IT organizations the ability to creategeo-distributed, and highly scalable applications while providingattractive cost-saving advantages. Yet, architecting, configuring, andadapting cloud applications (latency-sensitive web applications andbulk data processing applications) to meet their stringent performancerequirements is a challenge given the rich set of configurationoptions, shared multi-tenant nature of cloud platforms, and dynamicsresulting from activities such as planned maintenance.This project is developing novel methodologies, algorithms, andsystems that can enable application architects to (1) judiciouslyarchitect applications across multiple cloud data-centers whileconsidering application performance requirements, cost savingobjectives, and cloud pricing schemes guided by performance and costmodels of cloud components; (2) automatically learn effectiveapplication configurations and configuration-to-performance predictionmodels through statistical machine learning techniques; and (3) createapplications that can adapt to ongoing dynamics in cloud environmentsthrough transaction reassignment over shorter time-scales, andapplication migration over longer time-scales.The impact of this research is multi-fold: (1) Enable IT organizationsto significantly reduce costs by optimally moving their operations tothe cloud; (2) create benchmarks based on operationally deployedapplications and collecting workload traces which will be madeavailable to the research community; (3) make developed algorithms andsystems widely available as open source software; (4) inform thedesign of a nation-wide health-care cloud in Thailand; (4) introducecloud computing related topics in the undergraduate and graduatecurriculum; and (6) train multiple Ph.D., M.S., and undergraduatestudents, with explicit effort to recruit and train students fromunder-represented minority groups.