The Department of Energy (DOE) supports scientific collaboration in six program areas, ranging from nuclear physics, to biological and environmental research. To enable these data intensive large scale collaborations, the Energy Sciences Network (ESnet) is used to move upwards of 50 Petabytes a month between sites within the US and Europe. Bandwidth within ESnet can be requested, reserved, and utilized through the On-demand Secure Circuits and Advance Reservation System (OSCARS), which provisions network resources with guaranteed bandwidth over a known reservation schedule. Traditionally, OSCARS has only supported simple point-to-point connections between endpoints (e.g. universities, research laboratories), which has limited how efficiently the network may be harnessed by users. This paper details recent extensive enhancements prototyped for OSCARS, to be incorporated into a future release, which enable users to select novel service types including survivability, asymmetric bandwidth or routes, and anycast/manycast. We quantitatively compare several of these enhancements to the baseline point-to-point service, and find that the new services provide not only greater flexibility for the end-user, but savings for network administrators in terms of blocking and network resource usage as well.

Resources in a network are imperfect, and equipment failure can have detrimental effects on data and transmission success rates. Attempts to improve the survivability of network communications when these failures occur focus primarily on protection against the common occurrence of link failures, while nodal failure has been largely overlooked. In data-critical infrastructures, such as cloud computing or data center networks, wherein the critical points of interest are at the nodes, a natural disaster or directed attack could have catastrophic consequences for the localized data. To overcome single points of failure, replicated multicast transmissions can be used to distribute copies of critical data to various geographically dispersed locations on the grid. We therefore explore strategies to protect against single critical node failures during multicast transmissions. We propose three novel multicast survivable heuristics and quantitatively analyze and then compare them to traditional multicast provisioning schemes through extensive simulation1.

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.