• 제목/요약/키워드: Network loss

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Strengthening Packet Loss Measurement from the Network Intermediate Point

  • Lan, Haoliang;Ding, Wei;Zhang, YuMei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권12호
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    • pp.5948-5971
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    • 2019
  • Estimating loss rates with the packet traces captured from some point in the middle of the network has received much attention within the research community. Meanwhile, existing intermediate-point methods like [1] require the capturing system to capture all the TCP traffic that crosses the border of an access network (typically Gigabit network) destined to or coming from the Internet. However, limited to the performance of current hardware and software, capturing network traffic in a Gigabit environment is still a challenging task. The uncaptured packets will affect the total number of captured packets and the estimated number of packet losses, which eventually affects the accuracy of the estimated loss rate. Therefore, to obtain more accurate loss rate, a method of strengthening packet loss measurement from the network intermediate point is proposed in this paper. Through constructing a series of heuristic rules and leveraging the binomial distribution principle, the proposed method realizes the compensation for the estimated loss rate. Also, experiment results show that although there is no increase in the proportion of accurate estimates, the compensation makes the majority of estimates closer to the accurate ones.

유무선 혼합망에서 TCP와 Snoop 프로토콜 비교에 관한 연구 (Comparison about TCP and Snoop protocol on wired and wireless integrated network)

  • 김창희
    • 디지털산업정보학회논문지
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    • 제5권2호
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    • pp.141-156
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    • 2009
  • As the TCP is the protocol designed for the wired network that packet loss probability is very low, because TCP transmitter takes it for granted that the packet loss by the wireless network characteristics is occurred by the network congestion and lowers the transmitter's transmission rate, the performance is degraded. The Snoop Protocol was designed for the wired network by putting the Snoop agent module on the BS(Base Station) that connect the wire network to the wireless network to complement the TCP problem. The Snoop agent cash the packets being transferred to the wireless terminal and recover the loss by resending locally for the error occurred in the wireless link. The Snoop agent blocks the unnecessary congestion control by preventing the dupack (duplicate acknowledgement)for the retransmitted packet from sending to the sender and hiding the loss in the wireless link from the sender. We evaluated the performance in the wired/wireless network and in various TCP versions using the TCP designed for the wired network and the Snoop designed for the wireless network and evaluated the performance of the wired/wireless hybrid network in the wireless link environment that the continuous packet loss occur.

신원 확인을 위한 멀티 태스크 네트워크 (Multi-Task Network for Person Reidentification)

  • 조종경;이효종
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2019년도 춘계학술발표대회
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    • pp.472-474
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    • 2019
  • Because of the difference in network structure and loss function, Verification and identification models have their respective advantages and limitations for person reidentification (re-ID). In this work, we propose a multi-task network simultaneously computes the identification loss and verification loss for person reidentification. Given a pair of images as network input, the multi-task network simultaneously outputs the identities of the two images and whether the images belong to the same identity. In experiments, we analyze the major factors affect the accuracy of person reidentification. To address the occlusion problem and improve the generalization ability of reID models, we use the Random Erasing Augmentation (REA) method to preprocess the images. The method can be easily applied to different pre-trained networks, such as ResNet and VGG. The experimental results on the Market1501 datasets show significant and consistent improvements over the state-of-the-art methods.

Modeling of a controlled retransmission scheme for loss recovery in optical burst switching networks

  • Duong, Phuoc Dat;Nguyen, Hong Quoc;Dang, Thanh Chuong;Vo, Viet Minh Nhat
    • ETRI Journal
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    • 제44권2호
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    • pp.274-285
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    • 2022
  • Retransmission in optical burst switching networks is a solution to recover data loss by retransmitting the dropped burst. The ingress node temporarily stores a copy of the complete burst and sends it each time it receives a retransmission request from the core node. Some retransmission schemes have been suggested, but uncontrolled retransmission often increases the network load, consumes more bandwidth, and consequently, increases the probability of contention. Controlled retransmission is therefore essential. This paper proposes a new controlled retransmission scheme for loss recovery, where the available bandwidth of wavelength channels and the burst lifetime are referred to as network conditions to determine whether to transmit a dropped burst. A retrial queue-based analysis model is also constructed to validate the proposed retransmission scheme. The simulation and analysis results show that the controlled retransmission scheme is more efficient than the previously suggested schemes regarding byte loss probability, successful retransmission rate, and network throughput.

Deep Learning based Loss Recovery Mechanism for Video Streaming over Mobile Information-Centric Network

  • Han, Longzhe;Maksymyuk, Taras;Bao, Xuecai;Zhao, Jia;Liu, Yan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권9호
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    • pp.4572-4586
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    • 2019
  • Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

Passive Overall Packet Loss Estimation at the Border of an ISP

  • Lan, Haoliang;Ding, Wei;Zhang, YuMei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권7호
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    • pp.3150-3171
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    • 2018
  • In this paper, a heuristic method that leverages packet traces captured at the entire boarder of an ISP to distinguish and estimate the overall packet loss within an ISP's management domain (Intra_Path_Loss) and that in the outside Internet (Inter_Path_Loss) is proposed. Our method is inspired by that packet losses happened at different locations will cause different TCP sequence number patterns at the border of an ISP. Thereby, we leverage these TCP sequence number patterns to build a series of heuristic rules to estimate Intra_Path_Loss and Inter_Path_Loss, respectively. We do this work with an eye towards showing that the overall packet losses defined and estimated in this paper can provide the operators with some valuable information to help them precisely grasp the overall performance of network paths and narrow down the range of network anomalies. The proposed method is rigorously validated with simulations, and finally the results from a regional academic network JSERNET verify its effectiveness and practicability.

부하수준 별 한계손실계수 변동특성 분석 (Analysis of MLF Characteristics on 12 Load Levels)

  • 문영환;김호용;남궁재용;심우정
    • 대한전기학회논문지:전력기술부문A
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    • 제51권6호
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    • pp.284-289
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    • 2002
  • The transmission networks do not consist of perfect conductors and a percentage of the power generated is therefore lost before it reaches the loads. Since this network loss contributes to the cost of suppling power to consumers, it must be considered that the most efficient dispatch and location of generators and loads are to be achieved. In this paper, marginal loss factors are calculated for 12 load levels that represent the impact of marginal network losses on nodal prices at the transmission network connection points at which generators are located. Based on comparison analysis of marginal loss factors on 12 load levels, we found the MLF characteristics in KOREA.

TCP 성능개선을 위한 SVM 기반 LDA 설계 및 성능평가 (Design and Performance Evaluation of Support Vector Machine based Loss Discrimination Algorithm for TCP Performance Improvement)

  • 김도호;이재용;김병철
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2019년도 춘계학술대회
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    • pp.451-453
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    • 2019
  • 최근 무선 통신기기의 사용이 증가함에 따라 무선 네트워크 사용량이 증가하여 유선 네트워크와 무선 네트워크가 혼합되어 네트워크가 형성 되었다. 기존 TCP 알고리즘들은 유선 네트워크에 적합하게 설계 되었다. 따라서 현대의 네트워크 환경에서 패킷 손실을 정확히 구별하지 못하고 부적절한 혼잡제어를 수행하여 TCP의 성능 저하를 초래한다. 본 논문에서는 TCP 성능을 개선하기 위하여 패킷 손실이 발생한 환경에 따라 정확히 구분할 수 있는 SLDA(Support vector machine based Loss Discrimination Al gorithm)를 제안하고 그 성능을 평가한다.

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3차원 합성곱 신경망 기반 향상된 스테레오 매칭 알고리즘 (Enhanced Stereo Matching Algorithm based on 3-Dimensional Convolutional Neural Network)

  • 왕지엔;노재규
    • 대한임베디드공학회논문지
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    • 제16권5호
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    • pp.179-186
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    • 2021
  • For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.

Improving the Performance of Multi-Hop Wireless Networks by Selective Transmission Power Control

  • Kim, Tae-Hoon;Tipper, David;Krishnamurthy, Prashant
    • Journal of information and communication convergence engineering
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    • 제13권1호
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    • pp.7-14
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    • 2015
  • In a multi-hop wireless network, connectivity is determined by the link that is established by the receiving signal strength computed by subtracting the path loss from the transmission power. Two path loss models are commonly used in research, namely two-ray ground and shadow fading, which determine the receiving signal strength and affect the link quality. Link quality is one of the key factors that affect network performance. In general, network performance improves with better link quality in a wireless network. In this study, we measure the network connectivity and performance in a shadow fading path loss model, and our observation shows that both are severely degraded in this path loss model. To improve network performance, we propose power control schemes utilizing link quality to identify the set of nodes required to adjust the transmission power in order to improve the network throughput in both homogeneous and heterogeneous multi-hop wireless networks. Numerical studies to evaluate the proposed schemes are presented and compared.