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Degradation or failure events in optical backbone networks affect the service level agreements for cloud services. It is critical to detect and troubleshoot these events promptly to minimize their impact. Existing telemetry systems rely on arcane tools (e.g., SNMP) and vendor-specific controllers to collect optical data, which affects both the flexibility and scale of these systems. As a result, they fail to collect the required data on time to detect and troubleshoot degradation or failure events in a timely fashion. This paper presents the design and implementation of OpTel, an optical telemetry system, that uses a centralized vendor-agnostic controller to collect optical data in a streaming fashion. More specifically, it offers flexible vendor-agnostic interfaces between the optical devices and the controller and offloads data-management tasks (e.g., creating a queryable database) from the devices to the controller. As a result, OpTel enables the collection of fine-grained optical telemetry data at the one-second granularity. It has been running in Tencent's optical backbone network for the past six months. The fine-grained data collection enables the detection of short-lived events (i.e., ephemeral events). Compared to existing telemetry systems, OpTel accurately detects 2x more optical events. It also enables troubleshooting of these optical events in a few seconds, which is orders of magnitude faster than the state-of-the-art. 

Fraud detection is of great importance because fraudulent behaviors may mislead consumers or bring huge losses to enterprises. Due to the lockstep feature of fraudulent behaviors, fraud detection problem can be viewed as finding suspicious dense blocks in the attributed bipartite graph. In reality, existing attribute-based methods are not adversarially robust, because fraudsters can take some camouflage actions to cover their behavior attributes as normal. More importantly, existing structural information based methods only consider shallow topology structure, making their effectiveness sensitive to the density of suspicious blocks. In this paper, we propose a novel deep structure learning model named DeepFD to differentiate normal users and suspicious users. DeepFD can preserve the non-linear graph structure and user behavior information simultaneously. Experimental results on different types of datasets demonstrate that DeepFD outperforms the state-of-the-art baselines. 

ABSTRACT Chromosomal integration of heterologous metabolic pathways is optimal for industrially relevant fermentation, as plasmid-based fermentation causes extra metabolic burden and genetic instabilities. In this work, chromosomal integration was adapted for the production of mevalonate, which can be readily converted into β-methyl-δ-valerolactone, a monomer for the production of mechanically tunable polyesters. The mevalonate pathway, driven by a constitutive promoter, was integrated into the chromosome of Escherichia coli to replace the native fermentation gene adhE or ldhA . The engineered strains (CMEV-1 and CMEV-2) did not require inducer or antibiotic and showed slightly higher maximal productivities (0.38 to ∼0.43 g/liter/h) and yields (67.8 to ∼71.4% of the maximum theoretical yield) than those of the plasmid-based fermentation. Since the glycolysis pathway is the first module for mevalonate synthesis, atpFH deletion was employed to improve the glycolytic rate and the production rate of mevalonate. Shake flask fermentation results showed that the deletion of atpFH in CMEV-1 resulted in a 2.1-fold increase in the maximum productivity. Furthermore, enhancement of the downstream pathway by integrating two copies of the mevalonate pathway genes into the chromosome further improved the mevalonate yield. Finally, our fed-batch fermentation showed that, with deletion of the atpFH and sucA genes and integration of two copies of the mevalonate pathway genes into the chromosome, the engineered strain CMEV-7 exhibited both high maximal productivity (∼1.01 g/liter/h) and high yield (86.1% of the maximum theoretical yield, 30 g/liter mevalonate from 61 g/liter glucose after 48 h in a shake flask). IMPORTANCE Metabolic engineering has succeeded in producing various chemicals. However, few of these chemicals are commercially competitive with the conventional petroleum-derived materials. In this work, chromosomal integration of the heterologous pathway and subsequent optimization strategies ensure stable and efficient (i.e., high-titer, high-yield, and high-productivity) production of mevalonate, which demonstrates the potential for scale-up fermentation. Among the optimization strategies, we demonstrated that enhancement of the glycolytic flux significantly improved the productivity. This result provides an example of how to tune the carbon flux for the optimal production of exogenous chemicals.