NeTS: Small: Hybrid Switching in Data Center Networks: Systems-driven Modeling and Principled Algorithms

数据分析越来越成为技术和经济增长的引擎。它们通常涉及跨大型数据中心中的许多互连服务器的分布式计算。随着应用程序数量的不断增加和数据中心规模的迅速扩大，数据中心服务器之间的互连模式迫切需要快速、动态和经济的变化。为了解决这个问题，该项目将利用光路交换机，它可以处理具有稀疏连接模式的大数据流量。 它将开发调度算法，以管理何时通过这些交换机连接服务器，同时考虑到与交换硬件相关的实际约束以及实际系统约束。解决这一挑战将使数据中心资源更有效地用于数据分析和其他计算密集型任务。该项目解决了快速动态切换的问题，以适应数据中心的流量模式。 特别是它调查的问题，平衡的开销重新配置电路交换机的能力，动态满足交通需求的要求。该项目将为混合交换机（光路交换机和电子分组交换机的组合）设计可扩展的调度算法，直接应用于现代数据中心，满足广泛的实际需求。这些目标通过两步方法来实现：（a）系统建模，即导出硬件约束下的优化问题的规范模型（速度、连接限制和交换成本）以及实际系统要求（例如吞吐量和尾部延迟）;（B）原理算法：通过对潜在的混合连续/组合优化问题进行第一原理研究，导出具有正式最优性保证的可扩展调度解决方案。 该项目的成果预计将影响现代数据中心的实际网络设计以及具有高连接性和低交换成本的下一代光交换机的开发。

Data analytics are increasingly an engine of technical and economic growth. They typically involve distributed computation across many interconnected servers in large data centers. The increasing number of applications and the rapidly growing scale of datacenters are driving a strong need for fast, dynamic and economic changes in the interconnection patterns among datacenter servers. To address this, this project will make use of optical circuit switches, which can handle large data traffic flows with sparse connection patterns. It will develop scheduling algorithms to manage when to connect servers via these switches, taking into account practical constraints associated with the switching hardware as well as practical systems constraints. Addressing this challenge will enable more efficient use of data center resources for data analytics and other computationally intensive tasks. This project addresses the problem of fast dynamic switching for adaptation to datacenter traffic patterns. In particular it investigates the issue of balancing the overhead of reconfiguring circuit switches with the capability of dynamically meeting traffic demand requirements. The project will design scalable scheduling algorithms for hybrid switches (combination of optical circuit switches and electronic packet switches) with direct applications to modern data centers, addressing a wide range of practical requirements. These goals are addressed via a two-step approach: (a) Systems modeling, i.e. deriving canonical models of the optimization problems underlying the constraints of the hardware (speed, connectivity constraints and switching costs) as well as practical system requirements (such as throughput and tail latency); (b) Principled algorithms: deriving scalable scheduling solutions with formal optimality guarantees by conducting a first-principles study of the underlying mixed continuous/combinatorial optimization problems. The output of this project is expected to impact both practical networking designs of modern data centers as well as the development of next generation optical switches with high connectivity and small switching costs.