On the Impact of Scale-Free Structure on End-to-End TCP Performance

Sato, Yusuke; Ohsaki, Hiroyuki

doi:10.1109/compsac.2015.129

Cited by 4 publications

(5 citation statements)

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“…Inspired by the above properties of scale-free networks, the researchers try to introduce scale-free features into the topology of existing networks to improve its performance. For example, [91] analyzes the end-to-end performance (send rate, probability of packet loss, and round-trip time) of TCP streams over a scale-free network, which benefits from decreasing the maximum of link betweennesses. For the same reason, [92] proposes an Arbitrary Weight based Scale-Free topology control algorithm (AWSF) which introduces the scale-free characteristics of complex networks into the topology of WSN to reduce transmission delays.…”

Section: Advantages Of Scale-free Industry Networkmentioning

confidence: 99%

“…Community detection [89] Industrial wireless sensor networks [10][11] Delay Tolerant Networks (DTNs) [58] Detection of the community structure of smart city network system [59] Topology planning [88] Grid system [31] East China power grid [32] HUST WU WUT CSU SYSU [29] Network attack [98] Wireless sensor network [92] User-side big data balanced partitioning [94] The end-to-end TCP [91] Job scheduling [60] Joint localization and data acquisition in wireless sensor networks [85][86] Optimize the average network latency of the network [57] Information center network [51] Network routing [12] cally the following, power consumption, delay, ductility, robustness. Figure .7 summarizes the industrial interconnection network topology mentioned above.…”

Section: Small-world Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on the Network Models applied in the Industrial Network Optimization

Chen¹,

Xiong²,

Xue³

et al. 2022

Preprint

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Network architecture design is very important for the optimization of industrial networks. The type of network architecture can be divided into small-scale network and large-scale network according to its scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, its structure often has regular topology. For large-scale networks, the existing research mainly focuses on the random characteristics of network nodes and edges. Recently, popular models include random networks, small-world networks and scale-free networks. Starting from the scale of network, this survey summarizes and analyzes the network modeling methods based on graph theory and the practical application in industrial scenarios. Furthermore, this survey proposes a novel network performance metric -system entropy. From the perspective of mathematical properties, the analysis of its non-negativity, monotonicity and concave-convexity is given. The advantage of system entropy is that it can cover the existing regular network, random network, small-world network and scale-free network, and has strong generality. The simulation results show that this metric can realize the comparison of various industrial networks under different models.

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Section: Advantages Of Scale-free Industry Networkmentioning

confidence: 99%

Section: Small-world Networkmentioning

confidence: 99%

A Survey on the Network Models applied in the Industrial Network Optimization

Chen¹,

Xiong²,

Xue³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…[11]. In the conference paper [26], we presented the short version of this paper. In this paper, we add the explanation of our analysis in detail, and the numerical results for the validation of our analysis.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we add the explanation of our analysis in detail, and the numerical results for the validation of our analysis. Therefore, this paper is the extension of the conference papers [11], [26].…”

Section: Related Workmentioning

confidence: 99%

Fluid-Based Analysis for Understanding TCP Performance on Scale-Free Structure

Sato

Ohsaki

2016

Journal of Information Processing

Self Cite

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Scale-free structure is one of the most notable properties of the Internet as a complex network. Many researchers have investigated the end-to-end performance (e.g., throughput, packet loss probability, and round-trip time between source/destination nodes) of TCP congestion control mechanisms, but the impact of the scale-free structure on the TCP performance has not been fully understood. In this paper, we analyze the TCP performance on a scale-free tree whose strength of the scale-free property can be adjusted by a parameter. A scale-free tree represents the communication kernel for investigating a scale-free network since TCP mainly transmits packets on a shortest path between TCP source/destination nodes, and most shortest paths are included in the scale-free tree. Our numerical results show that the scale-free structure of a network improves the TCP performance, and that such performance improvement is caused by a reduction in the average path length and also a reduction of the traffic intensity at the bottleneck link. Furthermore, we confirm the validity of our analysis through a comparison with an optimization-based analysis.

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“…Some related works [8,9,10,11,12] have focused on this problem and developed root cause analysis techniques that can determine the primary cause for the throughput limitation of a TCP flow from a passively captured packet trace. The novelty of our present work is that our technique is completely independent of both the applications and underlying PHY/MAC layer technologies.…”

Section: Introductionmentioning

confidence: 99%

A fine-grained response time analysis technique in heterogeneous environments

Hafsaoui¹,

Dandoush

Urvoy-Keller

et al. 2018

Computer Networks

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Ii is crucial for the network operators and Internet service providers (ISPs) to determine the reasons that cause large response time fluctuations. In this paper, we consider passive measurements from heterogeneous environment (ADSL, FTTH and 3G/3G+ access technologies) of an European ISP 'Orange'. Through experimental analysis of real traces, the need of a fine-grained traffic analysis technique is demonstrated. We show that finding the root causes of the observed poor performance using simple metrics such as response time, RTT and packet loss is difficult. In view of this fact, the different factors that play a role in determining the resulting response time are described through examples. Then, a breakdown method that drills down into the passively observed TCP connections is proposed. The method decomposes the end-to-end response time into many time periods and maps each one to a specific parameter or a physical phenomenon. Thus, the impact of not only the network parameters but also the application configuration and user behavior is captured. The resulting time periods are given as input to a clustering algorithm in order to group together transfers with similar performance holding traffic of different application protocols over different access technologies. As a result, the contribution of each participant in the performance bottleneck is identified. The proposed technique is validated through extensive simulations and real passively measured traces and it is compared to other works. Exemplifying the technique on real traces from Internet and enterprise traffic is introduced and discussed to demonstrate the power of the approach and its simplicity. In contrast to some existing tools, ISPs and enterprise administrators do not need to modify their network architecture or to install a new software or a plugin at the client or at the server side in order to use our technique. In addition, data sampling is not used. This is particularly important in order to keep data consistency and to detect metrics peaks. Last, our tool deals with both long and short TCP connections.

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On the Impact of Scale-Free Structure on End-to-End TCP Performance

Cited by 4 publications

References 5 publications

A Survey on the Network Models applied in the Industrial Network Optimization

A Survey on the Network Models applied in the Industrial Network Optimization

Fluid-Based Analysis for Understanding TCP Performance on Scale-Free Structure

A fine-grained response time analysis technique in heterogeneous environments

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