Increased capacity in the access network poses capacity challenges on the transport network due to the aggregated traffic. However, there are spatial and time correlation in the user data demands that could potentially be utilized. To that end, we investigate a wireless transport network architecture that integrates beamforming and coded-caching strategies. Especially, our proposed design entails a server with multiple antennas that broadcasts content to cache nodes responsible for serving users. Traditional caching methods face the limitation of relying on the individual memory with additional overhead. Hence, we develop an efficient genetic algorithm-based scheme for beam optimization in the coded-caching system. By exploiting the advantages of beamforming and coded-caching, the architecture achieves gains in terms of multicast opportunities, interference mitigation, and reduced peak backhaul traffic. A comparative analysis of this joint design with traditional, un-coded caching schemes is also conducted to assess the benefits of the proposed approach. Additionally, we examine the impact of various buffering and decoding methods on the performance of the coded-caching scheme. Our findings suggest that proper beamforming is useful in enhancing the effectiveness of the coded-caching technique, resulting in significant reduction in peak backhaul traffic.

Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12.3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files are less likely to be reused. Due to this difference in data access patterns, the cache system has implemented a policy to avoid evicting smaller files when processing larger files. We also build a machine learning model to study the predictability of the cache behavior. Tests show that this model is able to accurately predict the cache accesses, cache misses, and network throughput, making the model useful for future studies on resource provisioning and planning.

Often models that operate on source code consume ASTs by linearizing them (usually with a depth-first traversal) (Amodio et al., 2017; Liu et al., 2017; Li et al., 2017), but they can also be processed by deep learning models that take graphs as input, as in White et al. (2016) and Chen et al. (2018) who use Recursive Neural Networks (RveNNs) (Goller & Kuchler, 1996) on ASTs. RveNNs are models that operate on tree-topology graphs, and have been used extensively for language modeling (Socher et al., 2013) and on domains similar to source code, like mathematical expressions (Zaremba et al., 2014; Arabshahi et al., 2018). They can be considered a special case of Message Passing Neural Networks (MPNNs) in the framework of Gilmer et al. (2017): in this analogy RveNNs are to Belief Propagation as MPNNs are to Loopy Belief Propagation. They can also be considered a special case of Graph Networks in the framework of Battaglia et al. (2018). ASTs also serve as a natural basis for models that generate code as output, as in Maddison & Tarlow (2014), Yin & Neubig (2017), Rabinovich et al. (2017), Chen et al. (2018), and Brockschmidt et al. (2018).