Paving the Way for Next Generation Wireless Data Center Networks

Abstract

Data Centers (DCs) have become an intrinsic element of emerging technologies such as big data, artificial intelligence, cloud services; all of which entails interconnected and sophisticated computing and storage resources. Recent studies of conventional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack traffic and unidentified flow classes: delay sensitive mice flows (MFs) and throughput-hungry elephant flows (EFs). Unfortunately, existing DCN topologies support only uniform distribution of capacities, provide limited bandwidth flexibilities and lacks of efficient flow classification mechanism.

Fortunately, wireless DCs can leverage wireless communication emerging technologies, such as multi-terabit free-space optic (FSO), to provide flexible and reconfigurable DCN topologies. It is worth noting that indoor FSO links are less vulnerable to outdoor FSO channel impairments. Consequently, indoor FSO links are more robust and can offer high bandwidths with long stability, which can further be enhanced with wavelength division multiplexing (WDM) methods. In this thesis, we alleviate the bandwidth inefficiency by FSO links that have the desired agility by allocating the transmission powers to adapt link capacity for dynamically changing traffic conditions, and to reduce the maintenance costs and overhead.

While routing the two classes along the same path causes unpleasant consequences, the DC researchers proposed traffic management solutions to treat them separately. However, the solutions either suffer from packet reordering and high queuing delay, or lack of accurate visibility and estimation on end-to-end path status. Alternatively, we leverage WDM to design elastic network topologies (i.e., part of the wavelengths are assigned to route MFs and the remaining for EFs). Since bandwidth demands can be lower than available capacity of WDM channels, we use traffic grooming to aggregate multiple flows into a larger flow and to enhance the link utilization.

On the other hand, to reap the benefits of the proposed WDM isolated topology, an accurate and fast EF detection mechanism is necessary. Accordingly, we propose a scheme that uses TCP communication behavior and collect indicative packets for its flow classification algorithm, it demonstrates perfect flow classification accuracy, and is in order of magnitudes faster than existing solutions with low communication and computation overhead.