Abstract
Manycasting allows a single source to reach multiple destinations while providing flexibility in destination selection. Our goal in this paper is to improve the cost of the manycast drop at member node (MA-DMN) overlay algorithm in terms of energy consumption and associated greenhouse gas (GHG) emissions. To reduce the environmental impact, ideally, a large percentage of the network nodes along the transmission and the chosen destinations need to be green. We present a novel energy-conservative emission-aware variant of the MA-DMN algorithm. We then propose further modifications to increase the utilization of those destinations that are powered by renewable energy sources: manycast drop at greenest nodes (MA-DGN). The potential for emission reduction by those algorithms is two-fold: The data are transported in the most efficient way and processed at the greenest available data centers. We compare the approaches by simulating realistic quantities of dynamic traffic. We assume heterogeneously distributed and time-dependent availability of renewable energy sources to power nodes throughout the network. We find that the energy-source-aware algorithms lower both energy-consumption and GHG emissions at stable network performance levels, in some cases even lowers blocking rate.
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