NeTS: Small: Online Management, Experimentation, and GAme (OMEGA) of Large-Scale Networks

NeTS：小型：大型网络的在线管理、实验和游戏 (OMEGA)

• 批准号: 1321069
• 负责人: Mehmet Gunes
• 金额: $ 20万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1321069&HistoricalAwards=false
• 关键词: NeTS; Small; Online; Management; Experimentation

As critical applications are converging onto the Internet, effective response to large-scale network dynamics like failures and demand spikes is gaining more importance. Major portion of the time for handling such network dynamics is determining how to respond, mostly performed manually in the current practice. Experienced human administrators are typically the ones who can quickly find a close-to-optimum response. However, as the networks are getting larger and more diverse, managing and attaining effective responses for an online operational network necessitates meta-tools to swiftly learn and characterize the network. This project will develop tools for automated management of a running network by framing heuristic optimization, empirical learning, experimental design, and network management with a simulation interface. The project will develop an online management and experimentation system for large-scale networks in an environment that enables trainee administrators to explore what-if scenarios, without having to risk the network operation. The project will also develop algorithms for empirical characterization of network dynamics, and tools for quick and close-to-optimal configuration of numerous network parameters in response to failures or customer traffic trends.The project will integrate behavioral scientific concepts into the practice of operational network management. The automated management using online optimization may establish a foundation for managing multi-owner systems, e.g., power grid, transportation, and water infrastructure networks. The project's heuristic optimization and experiment design methods as well as the approach to operator training are applicable to training in safety and mission critical industries where mistakes of ill-trained administrators are intolerable, e.g., airline pilot and nuclear reactor administrator training.

随着关键应用程序向互联网融合，对大规模网络动态（如故障和需求高峰）的有效响应变得越来越重要。用于处理这种网络动态的大部分时间是确定如何响应，在当前实践中主要是手动执行的。经验丰富的人类管理员通常能够迅速找到接近最佳响应的人。然而，随着网络变得越来越大，越来越多样化，管理和实现在线操作网络的有效响应需要元工具来快速学习和描述网络。本项目将通过构建启发式优化、经验学习、实验设计和具有模拟界面的网络管理来开发用于运行网络的自动化管理的工具。该项目将为大规模网络开发一个在线管理和实验系统，使受训管理员能够探索假设情景，而不必冒网络运行的风险。该项目还将开发用于网络动态的经验表征的算法，以及用于响应故障或客户流量趋势的众多网络参数的快速和接近最佳配置的工具。该项目将把行为科学概念整合到运营网络管理实践中。使用在线优化的自动化管理可以建立用于管理多所有者系统的基础，电网、交通和水利基础设施网络。该项目的启发式优化和实验设计方法以及操作员培训方法适用于安全和使命关键行业的培训，这些行业的管理人员训练不当，航空公司飞行员和核反应堆管理员培训。