Flow interaction based propagation model and bursty influence behavior analysis of Internet flows

Wu, Xiao-Yu; Gu, Rentao; Ji, Yuefeng

doi:10.1016/j.physa.2016.06.007

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…ese two reasons lead to the low utilization rate of the nodes involved in the failure in the route recovery, which affects the search of the optimal path. Also, some researchers investigated the robustness from the flow aspect, which raises a new direction [22,23], and Bachmann et al discussed the robustness of interdependent networks [24]. A reliable control design for networked control systems which is applied in satellite systems is proposed in [25].…”

Section: Related Workmentioning

confidence: 99%

Recovery Routing Based on Q-Learning for Satellite Network Faults

Qin

Dong³

et al. 2020

Complexity

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With the fierce research on the space and terrestrial network, the satellite network as the main component has received increasing attention. Due to its special operating environment, there are temporary link failures caused by interference and permanent port failures caused by equipment problems. In this paper, we propose a new satellite network routing technology for fault recovery based on fault detection. Based on Bayesian decision, this technology judges the probability of each fault by a priori probability of the two faults to achieve the purpose of effectively distinguishing between two types of faults and locate faulty links and node ports. Then, corresponding to the previous two stages of the fault detection, different stages and different methods are updated for different types of fault. We also combine satellite network data from satellite simulation software to validate our study. The results show that the recovery strategy has good performance, and the effective resource utilization rate is improved significantly.

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Section: Related Workmentioning

confidence: 99%

Recovery Routing Based on Q-Learning for Satellite Network Faults

Qin

Dong³

et al. 2020

Complexity

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“…A small initial shock, such as a flow burst or the breakdown of an internet router can trigger cascading failure. Understanding the response of internet flow to the occurrence of failure is essential, because it directly affects the quality of internet service [27,28]. The number of internet flows is enormous, and its relationship with network components (routers and links) is complicated; thus, analyzing flow behavior is extremely difficult.…”

Section: Introductionmentioning

confidence: 99%

Dynamic behavior analysis of an internet flow interaction model under cascading failures

et al. 2019

Phys. Rev. E

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Cascading failures in the internet have attracted recent attention due to their unpredictability and destructive consequences. Exploring the failure behavior patterns is necessary because they can provide effective intervention approaches to prevent huge network disasters. To analyze internet flow behaviors during cascading failures (chain reactions in router and link failures), we characterize the internet as two coupled networks, the router network and the flow network. Here the flow network is an abstract representation of data correlations obtained from the router network. We use this coupled network to build a cascading failure model for studying flow transmission and competition, which is reflected in bandwidth competition given by limited link capacity. We first study the dependency between routers and flows to explore the flow transmission efficiency when a failure event occurs. Moreover, we find that rerouting enables flow competition area (the number of flows with which one flow has a competitive relationship) to initially remain stable during a failure episode, but that it then quickly drops due to poor physical network connectivity. Additionally, in the early stage after the failure event, the degree of flow competition sharply increases because of the growing number of the flows and congestion. Subsequently, the flow competition decreases due to the failure of flow transmission.

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“…The reasons [27,29] and effects [29][30][31] of Internet bursty flows have been studied for many years. Wu et al [32] established an influence propagation model of Internet bursty flow to describe the impacts of bursty flow on its neighbours. To effectively alleviate the negative impacts of bursty flows, many researchers attempted to weaken the intensities of bursty flows at the source [33][34][35].…”

mentioning

confidence: 99%

“…However, due to the limitations of the router queue length and the link bandwidth capacity, Internet flows which share the same routers or links often compete with each other. Then, a bandwidth turbulence in one flow will ultimately spread to the whole network through the flow competitions under congested condition [16,32].…”

mentioning

confidence: 99%

Bandwidth turbulence control based on flow community structure in the Internet

2016

EPL

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Bursty flows vary rapidly in short period of time, and cause fierce bandwidth turbulence in the Internet. In this letter, we model the flow bandwidth turbulence process by constructing a flow interaction network (FIN network), with nodes representing flows and edges denoting bandwidth interactions among them. To restrain the bandwidth turbulence in FIN networks, an immune control strategy based on flow community structure is proposed. Flows in community boundary positions are immunized to cut off the inter-community turbulence spreading. By applying this control strategy in the first- and the second-level flow communities separately, 97.2% flows can effectively avoid bandwidth variations by immunizing 21% flows, and the average bandwidth variation degree reaches near zero. To achieve a similar result, about 70%–90% immune flows are needed with targeted control strategy based on flow degrees and random control strategy. Moreover, simulation results showed that the control effect of the proposed strategy improves significantly if the immune flow number is relatively smaller in each control step.

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Flow interaction based propagation model and bursty influence behavior analysis of Internet flows

Cited by 5 publications

References 30 publications

Recovery Routing Based on Q-Learning for Satellite Network Faults

Recovery Routing Based on Q-Learning for Satellite Network Faults

Dynamic behavior analysis of an internet flow interaction model under cascading failures

Bandwidth turbulence control based on flow community structure in the Internet

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