Traffic grooming is necessary for the efficient use of optical networks. It generally requires a signal-conversion from the optical to the electrical domain and back. Planners have some freedom in selecting the location where this energy-intensive process takes place. A subset of nodes in a network may be powered by renewable energy sources. Those green nodes cause lower green house gas (GHG) emissions than others. We suggest to choose these sites for grooming. We propose a model for GHG emissions caused by network operations. Depending on the level of penetration of renewable sources in the power supplies across the network, emissions can be reduced significantly. Potentially spanning different network domains, we introduce a hierarchical grooming approach to collect and multiplex traffic at those green nodes, groom it there and therefore save emissions. We evaluate the emission model and the hierarchical approaches' performance. Further, we apply them to various network/demand scenarios in an effort to identify favorable conditions for this scheme. We find that by relaxing energy-efficiency requirements in green nodes, and being more stringent in black nodes, we can reduce overall network emissions. The hierarchical approach is scalable and efficiently applicable to operating and future networks.

Today, large amounts of data, growing from terabytes to petabytes, need to be transferred across the globe in a timely manner. To accommodate this efficiently, network operators are augmenting their networks to 100 Gbps links and beyond. The spectrum-sliced elastic optical path network (SLICE) architecture enables accommodation of variable-rate data traffic in a highly spectrum-efficient manner. The blocking in SLICE networks observed in literature is much lower than on traditional fixed grid WDM networks. Still, the current routing and spectrum allocation (RSA) algorithms have potential for blocking performance improvements. To increase the spectral efficiency, we introduce a concept called routing, spectrum, and segment allocation (RSSA). We propose several novel continuous and non-continuous time heuristics based on the concept of RSSA. We consider dynamic advance reservation traffic requests over SLICE networks and evaluate the performance of the proposed RSSA heuristics. Through extensive simulations, we observe that our continuous-time, non-contiguous spectrum RSSA heuristics outperform existing heuristics in terms of both blocking probability and spectral utilization.

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.

Manycasting is an emerging communication paradigm which allows a single source to reach multiple destinations while providing flexibility in the selection of which destinations to connect with. Traditional wavelength division multiplexed (WDM) networks do not support the all-optical splitting of signals to multiple output ports as required by point-to-multipoint communication schemes. Previous work has proposed an overlay approach known as Manycasting with Drop at Member Node (MA-DMN) to provide manycast support as a logical overlay to basic point-to-point lightpath connections. This approach has been studied extensively and compared to alternative overlay models, and has emerged the obvious candidate for supporting manycast overlays. Throughout its evaluation though, MA-DMN has never been scrutinized in terms of its costs for energy consumption and associated greenhouse gas (GHG) emissions. In this work, we subject MA-DMN to these evaluations, while also proposing a new more energy-conservative emission-aware variant known as MA-DMN using Least Impact Trees (MA-DMN-LIT). We compare these two approaches by simulating realistic quantities of dynamic traffic, and uniformly distributing renewable energy sources to power nodes throughout the network. We find that MA-DMN-LIT reduces energy consumption over MA-DMN by 6-10% across the network, while also reducing CO2emissions by as much as 27%. We further conclude that MA-DMN-LIT also provides lower connection blocking by not over-subscribing shorter paths in the network as its emission-blind counterpart does.

For long-term operated networks, pre-planning is a challenging task due to uncertainty in future demand requests. In this paper we first address the question of how severe the impact of demand uncertainty on the network costs really is. Results indicate that only for special cases, uncertainty is interesting to be addressed in the planning process itself. Additionally, we introduce a Stochastic Programming model in order to handle the influence of such a special case. It can be observed that including an improbable demand development into the planning process leads to cost efficient network solutions for all considered scenarios.