CSR: Small: A Unified Reinforcement Learning Approach for Autoconfiguration of Virtualized Resources and Appliances

CSR：小型：用于自动配置虚拟化资源和设备的统一强化学习方法

• 批准号: 0914330
• 负责人: Chengzhong Xu
• 金额: $ 43.39万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914330&HistoricalAwards=false
• 关键词: CSR; Small; Unified; Reinforcement; Learning

URL: A Unified Reinforcement Learning Approach for Auto-configuration ofVirtualized Resources and AppliancesThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Cloud computing, unlocked by virtualization, is emerging as an increasingly important service-oriented computing paradigm. The goal of this project is to develop a unified learning approach, namely URL, to automate the configuration processes of virtualized machines and applications running on the virtual machines and adapt the systems configuration to the dynamics of cloud. The URL approach features three innovations: First is a reinforcement learning (RL) methodology for auto-configuration of virtual machines (VMs) on distributed computing resources in a real-time manner. Second is a unified RL approach for auto-configuration of both VMs and multi-tier web appliances. It is able to adapt the VM resource budget and appliance parameter settings in a coordinated way to the cloud dynamics and the changing workload for the objective of service quality assurance. Third is a distributed, cooperative RL approach that allows the RL-based learning and optimization agents running on different servers and with independent action choices to make an optimal joint configuration policy in large-scale systems. Deliverables that emerge from this project will advance discovery and understanding of autonomic management of large-scale complex systems with profound technical, economic, and societal impact. In addition, this project has an integral educational component. It will raise the level of awareness of system management issues and the power of machine learning technology, and prepare the students to enter the industry with adequate understanding of the challenges and opportunities in cloud computing.

URL：虚拟资源和设备自动配置的统一强化学习方法该奖项是根据2009年《美国复苏和再投资法案》(Public Law 111-5)资助的。云计算因虚拟化而解锁，正在成为一种日益重要的面向服务的计算范式。该项目的目标是开发一种统一的学习方法，即URL，使运行在虚拟机上的虚拟机器和应用程序的配置过程自动化，并使系统配置适应云的动态。URL方法有三个创新：第一是强化学习(RL)方法，用于实时自动配置分布式计算资源上的虚拟机(VM)。其次是用于自动配置VM和多层Web设备的统一RL方法。它能够以协调的方式调整虚拟机资源预算和设备参数设置，以适应云动态和不断变化的工作负载，以实现服务质量保证的目标。第三种是分布式、协作的RL方法，它允许基于RL的学习和优化代理运行在不同的服务器上，并具有独立的动作选择，以在大规模系统中制定最优的联合配置策略。该项目产生的成果将促进对具有深远技术、经济和社会影响的大型复杂系统的自主管理的发现和理解。此外，该项目还包含一个完整的教育部分。它将提高对系统管理问题的认识水平和机器学习技术的力量，并使学生在充分了解云计算的挑战和机遇的情况下进入该行业。