1 research outputs found
Linear Programming Approaches for Power Savings in Software-defined Networks (The Extended Version)
Software-defined networks have been proposed as a viable solution to decrease
the power consumption of the networking component in data center networks.
Still the question remains on which scheduling algorithms are most suited to
achieve this goal. We propose 4 different linear programming approaches that
schedule requested traffic flows on SDN switches according to different
objectives. Depending on pre-defined software quality requirements such as
delay and performance, a single variation or a combination of variations can be
selected to optimize the power saving and the performance metrics. Our
simulation results demonstrate that all our algorithm variations outperform the
shortest path scheduling algorithm, our baseline on power savings, less or more
strongly depending on the power model chosen. We show that in FatTree networks,
where switches can save up to 60% of power in sleeping mode, we can achieve 15%
minimum improvement assuming a one-to-one traffic scenario. Two of our
algorithm variations privilege performance over power saving and still provide
around 45% of the maximum achievable savings