NeTS: Small: Big Data and Optical Lightpaths-Driven Software Defined Networking

科学和工程越来越依赖于数据和处理大量数据的能力，以解决难题并推动基础发现和创新。这一趋势带来的挑战通常被称为"大数据"问题。大数据处理应用的示例包括地震数据分析、数据密集型文本处理、大型基因组组装、机器学习、数据挖掘和社交网络分析。该项目将研究软件定义网络（SDN）的新方向，这些新方向受到大数据处理应用所带来的网络挑战以及使用光学光路进行大数据传输的潜在好处的推动。 该项目将开发有效的解决方案，联合配置丰富的光设备和SDN交换机，实现满足大数据应用需求的网络服务。具体而言，该项目将开发光设备资源分配算法，拓扑设计和路由算法，贪婪和保证资源分配策略之间的比较，流量和网络的协同调度系统，数据洗牌传输技术，以及协同设计的应用程序和网络控制器。该项目开发的方法，算法和软件将在一个名为BOLD的现实实验基础设施中进行评估。 该项目可能具有超越计算学科的深远社会影响。该项目的结果可以大大加快广泛的计算科学发现。光网络设备消耗很少的功率，但可以支持巨大的数据速率;该项目的结果可能会为IT行业带来更环保的可持续发展未来。该项目活动将为本科生和研究生，特别是代表性不足的少数民族学生提供培训和教育的机会，在尖端的大数据驱动的网络。最后，该项目制作的软件、数据和课程材料将作为免费的开放源码资源传播，供更广泛的社区使用。

Science and engineering are increasingly relying on data and the ability to process a massive amount of data to solve hard problems and drive fundamental discoveries and innovations. Challenges arising from this trend are often referred to as "Big Data" problems. Examples of big data processing applications include seismic data analysis, data-intensive text processing, assembly of large genomes, machine learning, data mining, and social-network analysis. This project will investigate new directions in software defined networking (SDN) that are motivated by the networking challenges stemming from big data processing applications and by the potential benefits of using optical lightpaths for big data transport. The project will develop effective solutions for jointly configuring a rich set of optical devices and SDN switches to realize network services that meet the needs of big data applications. Specifically, the project will develop optical device resource allocation algorithms, topology design and routing algorithms, comparisons between greedy and guaranteed resource allocation policies, co-scheduling systems for traffic and network, techniques for data shuffle transmissions, and co-designed application and network controllers. The approaches, algorithms, and software developed by this project will be evaluated in a realistic experimental infrastructure called BOLD. The project may have far reaching societal impacts beyond the computing discipline. Results from the project can dramatically speed up a wide range of computational scientific discoveries. Optical networking devices consume very little power, yet can support enormous data rates; the project results could lead to a more environmentally sustainable future for the IT industry. The project activities will provide exciting opportunities for training and education of undergraduate and graduate students, and particularly under-represented minority students, in cutting edge big data-driven networking. Finally, software, data, and curriculum materials produced by this project will be disseminated as free open-source resources for the wider community's use.