TCP Flow Classification and Bandwidth Aggregation in Optically Interconnected Data Center Networks

Journal article, 2016

Optical functionality is being used to realize new data center architectures that minimize electronic switching overheads, pushing the processing to the edge of the network. A challenge in optically interconnected data center networks is to identify the large, bandwidth-hungry flows ( i.e., elephants) and efficiently establish the optical circuits. Moreover, the amount of optical resources to be provisioned during the network planning phase is a critical design problem. Flow classification accuracy affects the efficiency of optical circuits. Optical channel bandwidth, on the other hand, directly relates to the additive-increase, multiplicative-decrease congestion control mechanism of the transmission control protocol and affects the effective bandwidth allocated to elephant flows. In this paper, we simultaneously investigate the impact of two important mechanisms on data center network performance: traffic flow classification accuracy and optical bandwidth aggregation ( i.e., the consolidation of several low-capacity channels into a single high-capacity one by employing advanced modulation formats for short-reach communications). We develop a discrete-event simulator for a hybrid data center network, enabling the tuning of flow classification parameters. Our simulations indicate that data center performance is highly sensitive to the aggregation level. We could observe up to a 74.5% improvement in network throughput only due to consolidating the optical channel bandwidth. We further noticed that the role of flow classification becomes more pronounced with higher bandwidth per wavelength as well as with more hot-spot traffic. Compared to a random classification benchmark, adaptive flow classification could lead to throughput improvements as large as 54.7%.