Demand-Aware Flow Allocation in Data Center Networks


In this work we consider a relatively large and highly dynamic data center network in which flows have small interarrival times and different demands for the network resources. Taking into account the properties and specifics of such networks we consider the problem of flow placement, i.e. assignment of an outgoing port for flows at each hop from source to destination. Using the characteristics of modern data centers from previous measurement studies, in this work we first simulate the flow allocation using several algorithms with and without global knowledge. We find that in all settings local forwarding decisions are almost as good as decisions made with global information at hand. This finding enables us to propose a fully distributed mechanism that relies only on local knowledge and allows to achieve fair and demand aware flow allocation in the data center network. The mechanism has low complexity and performs better than naive random flow allocation

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