Reconfigurable optical wireless applications in data centers

Abstract

Data centers (DCs) are a critical piece of today's networked applications in both private and public sectors. The key factors that have driven this trend are economies of scale, reduced management costs, better utilization of hardware via statistical multiplexing, and the ability to elastically scale applications in response to changing workload patterns. A robust datacenter network fabric is fundamental to the success of DCs and to ensure that the network does not become a bottleneck for high-performance applications. In this context, DC network design must satisfy several goals: high performance (e.g., high throughput and low latency), low equipment and management cost, robustness to dynamic traffic patterns, incremental expandability to add new servers or racks, and other practical concerns such as cabling complexity, and power and cooling costs. Current DC network architectures do not seem to provide a satisfactory solution, with respect to the above requirements. In particular, traditional static (wired) networks are either: (i) overprovisioned to account for worst-case traffic patterns, and thus incur high cost (e.g., fat-trees or Clos), or (ii)oversubscribed (e.g., simple trees or leaf-spine architectures) which incur low cost but offer poor performance due to congested links. Recent works have tried to overcome the above limitations by augmenting a static (wired) “core” with some flexible links (RF-wireless or optical). These augmented architectures show promise, but offer only incremental improvement in performance. Specifically, RF-wireless based augmented solutions also offer only limited performance improvement, due to inherent interference and range constraints of RF links. Optical solutions offer high-bandwidth links and low latency, but have limited scalability, offer only limited flexibility (e.g., bipartitematchings between the racks), and have a single point of failure. Furthermore, all the above architectures incur high cabling cost and complexity.