In network measurement, learning-based sketch is a hot topic recently, which combines traditional sketches with machine learning techniques to improve the accuracy of sketches, while reducing the deployment overhead on switches. So far, most learning-based sketches estimate the sizes of either error-prone flows or all flows using machine learning models. These models take the sketch counter values of flows as features and their real sizes as labels for training. However, the flow size distribution is highly skewed, resulting in the effect that the flow sizes estimated by models are biased toward the sizes of mouse flows, severely underestimating elephant flows. To this end, a network measurement framework via back propagation neural network (BPNN) called NeuroSketch is proposed, which can directly estimate flow sizes and flow cardinality without identifying error-prone flows. Meanwhile, in order to provide effective features for BPNNs, a novel bloom filter-based sketch named BF-Sketch is proposed in this paper. BF-Sketch not only records the count values, but also the number of hash collisions in counters as a new feature, which can efficiently reduce the underestimation of elephant flows by machine learning models. The experimental results show that NeuroSketch reduces the average absolute error (AAE) of flow size estimation by 65%, and relative errors of flow cardinality estimation by 72.23%, compared with learning-based sketches. Moreover, BF-Sketch is implemented on OVS platform and P4-programmable switch to justify its feasible deployment in commodity software and hardware switches.

Packet classification is one of the crucial components of networking. With the advent of Software Defined Network (SDN), packet classification has become more challenging. So far, the proposed schemes have shown good performance. However, packet classification has different application scenarios, such as access control and firewalls. The distribution characteristics of rulesets vary in different application scenarios, which affects packet classification throughput. To this end, a tuple selection model named Picking Model (PM) is designed in this paper to perform packet matching via a neural network. Moreover, based on PM, a packet classification scheme called TuplePick (TP) is proposed, which enables to pick a possible good tuple rather than an exhaustive search in the tuple space. The experimental results indicate that its throughput variances of different rulesets are less than state-of-the-art schemes, which means it outperforms current schemes on stability of throughput in different application scenarios.

As an important financial investment commodity, the price fluctuations of gold significantly impact the global economy and the stability of the financial market. Therefore, it is of great significance to accurately predict the price of gold futures. This paper presents a novel gold futures price prediction model, PCA-AGRU, based on Principal Component Analysis (PCA) and Adaptive Gated Recurrent Unit (AGRU). PCA is utilized for the dimensionality reduction of data while extracting essential features. AGRU bases on Gated Recurrent Unit (GRU), embeds Self-Attention (SA), and adds an adaptive adjustment mechanism, making the model more effective in capturing long-term dependencies within time series data. This paper uses the international gold futures market data as the experimental dataset, and uses Maximal Information Coefficient (MIC) to analyze the correlation of the influencing factors. The PCA-AGRU model is compared with five prediction models of GRU, SA-GRU, AGRU, PCA-GRU, and PCA-SA-GRU. The experimental results show that the PCA-AGRU model performs best in forecasting gold futures prices.

Name lookup is a key technology for the forwarding plane of content router in Named Data Networking (NDN). To realize the efficient name lookup, what counts is deploying a high-performance index in content routers. So far, the proposed indexes have shown good performance, most of which are optimized for or evaluated with URLs collected from the current Internet, as the large-scale NDN names are not available yet. Unfortunately, the performance of these indexes is always impacted in terms of lookup speed, memory consumption and false positive probability, as the distributions of URLs retrieved in memory may differ from those of real NDN names independently generated by content-centric applications online. Focusing on this gap, a smart mapping model named Pyramid-NN via neural networks is proposed to build an index called LNI for NDN forwarding plane. Through learning the distributions of the names retrieved in the static memory, LNI that will be trained by real NDN names offline and preset in content routers in the future can not only reduce the memory consumption and the probability of false positive, but also ensure the performance of real NDN name lookup. Experimental results show that LNI-based FIB can reduce the memory consumption to 58.258 MB. Moreover, as it can be deployed on SRAMs, the throughput is about 177 MSPS, which well meets the current network requirement for fast packet processing.