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In optical networks, simulation is a cost-efficient and powerful way for network planning and design. It helps researchers and network designers quickly obtain preliminary results on their network performance and easily adjust the design. Unfortunately, most optical simulators are not open-source and there is currently a lack of optical network simulation tools that leverage machine learning techniques for network simulation. Compared to Wavelength Division Multiplexing (WDM) networks, Elastic Optical Networks (EON) use finer channel spacing, a more flexible way of using spectrum resources, thus increasing the network spectrum efficiency. Network resource allocation is a popular research topic in optical networks. In EON, this problem is classified as Routing, Modulation and Spectrum Allocation (RMSA) problem, which aims to allocate sufficient network resources by selecting the optimal modulation format to satisfy a call request. SimEON is an open-source simulation tool exclusively for EON, capable of simulating different EON setup configurations, designing RMSA and regenerator placement/assignment algorithms. It could also be extended with proper modelings to simulate CapEx, OpEx and energy consumption for the network. Deep learning (DL) is a subset of Machine Learning, which employs neural networks, large volumes of data and various algorithms to train a model to solve complex problems. In this paper, we extended the capabilities of SimEON by integrating the DeepRMSA algorithm into the existing simulator. We compared the performance of conventional RMSA and DeepRMSA algorithms and provided a convenient way for users to compare different algorithms’ performance and integrate other machine learning algorithms. 

Abstract Carbohydrate Active EnZymes (CAZymes) are significantly important for microbial communities to thrive in carbohydrate rich environments such as animal guts, agricultural soils, forest floors, and ocean sediments. Since 2017, microbiome sequencing and assembly have produced numerous metagenome assembled genomes (MAGs). We have updated our dbCAN-seq database (https://bcb.unl.edu/dbCAN_seq) to include the following new data and features: (i) ∼498 000 CAZymes and ∼169 000 CAZyme gene clusters (CGCs) from 9421 MAGs of four ecological (human gut, human oral, cow rumen, and marine) environments; (ii) Glycan substrates for 41 447 (24.54%) CGCs inferred by two novel approaches (dbCAN-PUL homology search and eCAMI subfamily majority voting) (the two approaches agreed on 4183 CGCs for substrate assignments); (iii) A redesigned CGC page to include the graphical display of CGC gene compositions, the alignment of query CGC and subject PUL (polysaccharide utilization loci) of dbCAN-PUL, and the eCAMI subfamily table to support the predicted substrates; (iv) A statistics page to organize all the data for easy CGC access according to substrates and taxonomic phyla; and (v) A batch download page. In summary, this updated dbCAN-seq database highlights glycan substrates predicted for CGCs from microbiomes. Future work will implement the substrate prediction function in our dbCAN2 web server. 

In this article, we enhanced the capability of SIMON (Simulator for Optical Networks) by considering the nonlinear effect of the optical network components and different industry network devices. This is achieved by using an optical route planning library called GNPy (Gaussian Noise model in python) as the calculation model within SIMON. SIMON is implemented in C++ and has mainly been used as an optical network learning tool for studying the performance of wavelength-routed optical networks. It measures the network blocking probability by taking into consideration the optical device characteristics. SIMON can capture the most significant impairments when estimating the Bit-Error Rate (BER) but does not consider fiber dispersion and non-linearities. These impairments can be significant when simulating a large-scale network. GNPy, on the other hand, considers those physical impairments and can give a more accurate signal-to-noise ratio (SNR) estimation validated by real-world measurements. By integrating GNPy with SIMON, we are able to set a minimum SNR threshold, which must be satisfied by any call set up in the network. The integration of SIMON and GNPy makes the resulting simulator not only suitable for academic learning but also valuable for real-world network planning, evaluation, and deployment of optical networks. 

Network service mesh architectures, by interconnecting cloud clusters, provide access to services across distributed infrastructures. Typically, services are replicated across clusters to ensure resilience. However, end-to-end service performance varies mainly depending on the service loads experienced by individual clusters. Therefore, a key challenge is to optimize end-to-end service performance by routing service requests to clusters with the least service processing/response times. We present a two-phase approach that combines an optimized multi-layer optical routing system with service mesh performance costs to improve end-to-end service performance. Our experimental strategy shows that leveraging a multi-layer architecture in combination with service performance information improves end-to-end performance. We evaluate our approach by testing our strategy on a service mesh layer overlay on a modified continental united states (CONUS) network topology. 

Mobile apps nowadays are often packaged with third-party ad libraries to monetize user data. Many mobile ad networks exploit these mobile apps to extract sensitive real-time geographical data about the users for location-based targeted advertising. However, the massive collection of sensitive information by the ad networks has raised serious privacy concerns. Unfortunately, the extent and granularity of private data collection of the location-based ad networks remain obscure. In this work, we present a mobile tracking measurement study to characterize the severity and significance of location-based private data collection in mobile ad networks, by using an automated fine-grained data collection instrument running across different geographical areas. We perform extensive threat assessments for different ad networks using 1,100 popular apps running across 10 different cities. This study discovers that the number of location-based ads tend to be positively correlated with the population density of locations, ad networks' data collection behaviors differ across different locations, and most ad networks are capable of collecting precise location data. Detailed analysis further reveals the significant impact of geolocation on the tracking behavior of targeted ads, and a noteworthy security concern for advertising organizations to aggregate different types of private user data across multiple apps for a better targeted ad experience.