3rd International Workshop on Network Meets Intelligent Computations

Session NMIC


8:30 AM — 10:00 AM HKT
Dec 13 Tue, 7:30 PM — 9:00 PM EST

Interval Matching Algorithm for Task Scheduling with Time Varying Resource Constraints

Weiguan Li, Jialun Li, Yujie Long, Weigang Wu

The co-location of online services and offline tasks has become very popular in data centers, which can largely improve resource utilization. Scheduling co-located offline tasks is challenging due to the interference with online services. Existing co-location scheduling algorithms try to find the best combination of different workloads to avoid performance interference and maximize the utilization of data centers, but few of them take the time varying resource constraints into account. We propose a heuristic algorithm named interval matching scheduling algorithm based on the idea that the time series of available resources and task scheduling can be regarded as interval endpoints. The proposed scheduling algorithm makes decisions based on a scoring method that calculates the matching degrees of the tasks and the changing resource series. The experimental results show that the proposed algorithm has achieved better performance under different parameter settings comprehensively.

Privacy protection scheme based on certificateless in VSNs environment

Yanfei Lu, Suzhen Cao, Yi Guo, Qizhi He, Zixuan Fang, Junjian Yan

In vehicular social networks (VSNs), cloud service providers can provide many convenient services for vehicles to ensure the safety of driving. However, the wireless communication between entities in VSNs is vulnerable to attacks, which can lead to vehicle privacy leakage. To solve this problem, a certificateless searchable encryption scheme with privacy-preserving features that can resist keyword guessing attacks is proposed based on the VSNs application environment. The scheme combines proxy re-encryption technology, which enables vehicle users to obtain accurate request results without disclosing privacy information to cloud service providers, and achieves the privacy of vehicle identity and confidentiality of transmitted data. In addition, the authorization process of the data service provider not only ensures the security of the data but also achieves revocability of user authorization. Based on the computational Diffie-Hellman problem and the discrete logarithm problem, the scheme is proved to be resistant to internal or external keyword guessing attacks under the random oracle model, and the experimental results show that the scheme has better performance in terms of computational and communication efficiency.

Measurement and Analysis: Does QUIC Outperform TCP?

Xiang Qin, Xiaochou Chen, Wenju Huang, Yi Xie, Yixi Zhang

Many web applications adopt Transfer Control Protocol (TCP) as the underlying protocol, where congestion control (CC) plays a vital role in reliable transmission. However, some TCP mechanisms cannot cope with the requirements of new applications and ever-increasing network traffic. Therefore, people have proposed Quick UDP Internet Connection (QUIC), an excellent potential alternative based on UDP, which introduces new features to improve transmission performance and is compatible with existing CC algorithms. This paper has conducted many experiments in the testbed and actual environments to measure and compare QUIC and TCP regarding communication quality, compatibility fairness, and user experience, while considering the impacts of three typical CC algorithms: NewReno, Cubic, and BBR. QUIC outperforms TCP in most experiments for web browsing and online video, but its performance is susceptible to CC algorithms and network conditions. For example, with the Cubic algorithm, QUIC enabling the 0-RTT feature can decrease the webpages loading time by 37.11% compared with TCP. Using the BBR algorithm, both QUIC and TCP achieve high throughput, slight fluctuation, and few delayed events when playing online videos. TCP with BBR provides better fairness, while QUIC with BBR is more robust in a network with high latency or packet loss.

Binary Neural Network with P4 on Programmable Data Plane

Junming Luo, Waixi Liu, Miaoquan Tan, Haosen Chen

Deploying machine learning (ML) on the programmable data plane (PDP) has some unique advantages, such as quickly responding to network dynamics. However, compared to demands of ML, PDP have limited operations, computing and memory resources. Thus, some works only deploy simple traditional ML approaches (e.g., decision tree, K-means) on PDP, but their performance is not satisfactory. In this article, we propose P4-BNN (Binary Neural Network based on P4), which uses P4 to completely executes binary neural network on PDP. P4- BNN addresses some challenges. First, in order to use shift and simple integer arithmetic operations to replace multiplication, P4- BNN proposes a tailor-made data structure. Second, we use an equivalent replacement programming method to support matrix operation required by ML. Third, we propose a normalization method in PDP which needn?€?t floating-point operations. Fourth, by using register storing the model parameters, the weights of P4- BNN model can be updated without interrupting the P4 program running. Finally, as two use-cases, we deploy P4-BNN on a Netronome SmartNIC (Agilio CX 2x10GbE) to achieve flow classification and anomaly detection. Compared to the N3IC, decision tree and K-means, the accuracy of P4-BNN has 1.7%, 3.4% and 47.7% improvement respectively.

Semi-Supervised Learning Based on Reference Model for Low-resource TTS

Xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao

Most previous neural text-to-speech (TTS) methods are mainly based on supervised learning methods, which means they depend on a large training dataset and hard to achieve comparable performance under low-resource conditions. To address this issue, we propose a semi-supervised learning method for neural TTS in which labeled target data is limited, which can also resolve the problem of exposure bias in the previous autoregressive models. Specifically, we pre-train the reference model based on Fastspeech2 with much source data, fine-tuned on a limited target dataset. Meanwhile, pseudo labels generated by the original reference model are used to guide the fine-tuned model?€?s training further, achieve a regularization effect, and reduce the overfitting of the fine-tuned model during training on the limited target data. Experimental results show that our proposed semisupervised learning scheme with limited target data significantly improves the voice quality for test data to achieve naturalness and robustness in speech synthesis.

RTSS: Robust Tuple Space Search for Packet Classification

Jiayao Wang, Ziling Wei, Baosheng Wang, Shuhui Chen, and Jincheng Zhong

Packet classification shows an essential role in network functions. Traditional classification algorithms assume that all field values are available and valid. However, such a premise is being challenged as networks become more complex now. Scenarios with field-missing poses great challenges to packet classifiers. Existing approaches can only list all possible situations in such cases, increasing the workload exponentially. RFC algorithm is proved to be helpful for this issue in our previous work, but its spacial performance is much poor. In this paper, we propose a novel classification scheme using Tuple Space Search (TSS) to deal with missing fields. We redesign the hash calculation method and raise a new data structure to recover field-missing packets. The experiment shows that RTSS reduce the memory consumption and construction time by several orders of magnitude. At the same time, RTSS has better classification performance than previous work, while supporting fast updates.

A Novel Reliability Evaluation Method Based on Improved Importance Algorithm for SCADA

Zhu Zhaoqian, Chen Yenan, Su Bo, Li Linsen

Cyber attacks have become a major factor affecting the security and reliability of power SCADA (Supervisory control and data acquisition) in recent years. We urgently need an effective SCADA reliability evaluation algorithm to predict potential risks. However, existing evaluation algorithms have the shortcomings of inefficient sampling and low accuracy of indexes. In this paper, we propose an equal dispersion algorithm and an important sampling algorithm and combine them into an improved importance algorithm. The experimental results show that the improved importance algorithm not only improves the sampling efficiency, but also improves the accuracy of the evaluation index. The evaluation indexs accurately quantify the impact of the six widely used cyber attacks on power SCADA reliability.

Evolutionary Discrete Optimization Inspired by Zero-Sum Game Theory

Ruiran Yu, Haoliang Wen ,Yuhan Xu

In a zero-sum game, the two players compete against each other, and the gain of one player means the loss of the other. Generative adversarial networks (GANs) are models of this kind of thinking. Evolutionary algorithms (EAs) are popular and high robust methods to solve combinatorial optimization problems. However, in the middle stages of evolution, EAs usually suffer from the problem of a serious lack of population diversity. This often results in that EAs fall into local optima. This paper presents a cooperative evolutionary algorithm driven by policy-based GANs (PGAN-CEA) for solving traveling salesman problems (TSPs). PGAN-CEA adopts a policy-gradient method in reinforcement learning to train GANs to generate discrete data. First, GANs are used to construct an initial population. Then, a cooperative evolution strategy driven by GANs is used in the middle of the evolution. Further, a dual-population mechanism is utilized to assist the co-evolution of the dominant solutions generated by GANs and the solutions from the population of EAs. Test cases from TSPLIB and the Mona Lisa Problems are used to evaluate the proposed algorithm. Compared with other GAN-based algorithms, the proposed algorithm can mitigate the problem of local convergence and achieves certain improvements in quite a few performance indicators.

Research on data collection and energy supplement mechanism in WRSN based on UAV: a method to maximize energy supplement efficiency

Wen Xie, Xiangyu Bai, Yaru Ren

Energy has always been a key bottleneck restricting the large-scale deployment and long-term operation of wireless sensor networks (WSNs). Wireless rechargeable sensor networks (WRSNs) can effectively alleviate the energyconstrained problem of sensor nodes. However, due to the constraints of the service capabilities of mobile charging equipment, how to efficiently replenish energy and maintain the long-term operation of the network is still very challenging. In this paper, we propose an UAV-based energy replenishment mechanism in WRSN, which aims to maximize the replenished energy benefit of the network from the energy expended by UAVs. This paper firstly constructs the network model and defines the optimization problem of energy replenishment efficiency maximization. Then, in order to improve the performance of WRSN in terms of energy replenishment efficiency, the node clustering, anchor node selection and flight path planning problems are respectively studied, and a data collection and energy replenishment mechanism is proposed for the above problems. The experimental results show that the proposed scheme can effectively improve the energy replenishment efficiency of the system, prolong the life of the network and balance the energy consumption of the network.

Session Chair

Lei Yang, South China University of Technology, China

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