Network Congestion Rate Research Articles

Dynamic resilience analysis of the liner shipping network: From structure to cooperative mechanism

Liner shipping networks play a vital role in global and regional trade. However, they are susceptible to damage from unexpected interruptions, which can trigger dynamic cascading failures and undermine the system’s resilience. To address this challenge, we propose a novel cascading failure model for liner shipping networks that considers the characteristics of the network structure and port functions. First, we design two load redistribution methods that rely on network topology and employ a cooperative mechanism for coordination. This cooperative mechanism aims to balance the benefits for carriers and shippers by effectively controlling losses. Subsequently, we develop three metrics—network congestion rate, failure rate, and shipper loss—to assess the resilience of the network during cascading failures. To verify the impact of the cooperative mechanism, we apply the proposed methods to the China-Europe liner shipping network. Through simulations involving various port failures and resistance levels, we analyze the effectiveness of the cooperative mechanism. The results demonstrate that redistributing the load to downstream ports within the network effectively mitigates deep cascading failures. Additionally, the implementation of a port cooperative mechanism enhances resilience in the face of uncertainties by safeguarding crucial ports within the network and significantly reducing shipper losses. When port resistance is low, the cooperative mechanism reduces shipper losses by nearly half and lowers the average congestion rate. Although port reserve capacity can resist cascading failures, it falls short in the face of severe disruptions. In such cases, the cooperative mechanism compensates for capacity shortages, enhancing port resilience at a low cost. This study contributes to combating and minimizing cascading congestion in liner shipping networks, offering valuable insights for risk prevention and management strategies for ports and shipping companies. It also has implications for yield management and policy decisions from a network perspective.

Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

According to Cisco’s Internet Report 2020 white paper, there will be 29.3 billion connected devices worldwide by 2023, up from 18.4 billion in 2018. 5G connections will generate nearly three times more traffic than 4G connections. While bringing a boom to the network, it also presents unprecedented challenges in terms of flow forwarding decisions. The path assignment mechanism used in traditional traffic scheduling methods tends to cause local network congestion caused by the concentration of elephant flows, resulting in unbalanced network load and degraded quality of service. Using the centralized control of software-defined networks, this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing (MCQG). The ideal transmission path is selected for data flows while considering the network congestion rate and quality of service. Different traffic scheduling strategies are used according to the characteristics of different service types in data centers. Reroute scheduling for elephant flows that tend to cause local congestion. The path evaluation function is formed by the maximum link utilization on the path, the number of elephant flows and the time delay, and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant flows. It is used to reduce the possibility of local network congestion occurrence. Equal cost multi-path (ECMP) protocols with faster response time are used to schedule mouse flows with shorter duration. Used to guarantee the quality of service of the network. To achieve isolated transmission of various types of data streams. The experimental results show that the proposed strategy has higher throughput, better network load balancing, and better robustness compared to ECMP under different traffic models. In addition, because it can fully utilize the resources in the network, MCQG also outperforms another traffic scheduling strategy that does rerouting for elephant flows (namely Hedera). Compared with ECMP and Hedera, MCQG improves average throughput by 11.73% and 4.29%, and normalized total throughput by 6.74% and 2.64%, respectively; MCQG improves link utilization by 23.25% and 15.07%; in addition, the average round-trip delay and packet loss rate fluctuate significantly less than the two compared strategies.

An Evaluation of Uncle Block Mechanism Effect on Ethereum Selfish and Stubborn Mining Combined With an Eclipse Attack

Ethereum accelerates the transaction process through a quicker block creation design. Since the time interval between the generation of blocks is very short (about 15s), block propagation time in an inefficient network is not negligible compared with the block time interval. This lead to the production of a large number of orphan blocks. In order to solve the security problems that may be caused by the orphan block and improve the transaction processing efficiency, Ethereum introduces the uncle block mechanism, i.e., an orphan block may get part of minted reward if it gets a reference by a regular block. In this paper, we show the weakness of the uncle block mechanism. Firstly, we describe the specific differences of Ethereum selfish and stubborn mining in every state from the ones in Bitcoin. Secondly, we simulate possible attacks, and the results show that the Ethereum selfish and stubborn mining strategies not only increase the reward of an attacker but also decrease the security threshold. The security threshold refers to the proportion of the attacker's computational power that needs to be achieved in order to obtain a higher reward than he should. In a practical network congestion rate, the security threshold are weakened to 0.129 and 0.216 against the Lead stubborn mining strategy and the original selfish mining strategy, respectively. When the congestion rate is rising, the reward is increasing and the threshold is decreasing. Thirdly, possible strategies are evaluated to find out the optimal one in different settings. Fourthly, we also extend the evaluation by combining three eclipse attack strategies with selfish or stubborn mining. Most of combinations bring more advantages to an attacker than a single strategy.

A Smart Congestion Control Mechanism for the Green IoT Sensor-Enabled Information-Centric Networking.

Information-Centric Networking (ICN) is a new Internet architecture design, which is considered as the global-scale Future Internet (FI) paradigm. Though ICN offers considerable benefits over the existing IP-based Internet architecture, its practical deployment in real life still has many challenges, especially in the case of high congestion and limited power in a sensor enabled-network for the Internet of Things (IoT) era. In this paper, we propose a smart congestion control mechanism to diminish the network congestion rate, reduce sensor power consumptions, and enhance the network performance of ICN at the same time to realize a complete green and efficient ICN-based sensor networking model. The proposed network system uses the chunk-by-chunk aggregated packets according to the content popularity to diminish the number of exchanged packets needed for data transmission. We also design the sensor power-based cache management strategy, and an adaptive Markov-based sensor scheduling policy with selective sensing algorithm to further maximize power savings for the sensors. The evaluation results using ndnSIM (a widely-used ICN simulator) show that the proposed model can provide higher network performance efficiency with lower energy consumption for the future Internet by achieving higher throughput with higher cache hit rate and lower Interest packet drop rate as we increase the number of IoT sensors in ICN.

Delay prediction algorithm based on bidirectional associative memory neural network

In this paper, a delay prediction algorithm based on bidirectional associative memory neural network is proposed. The algorithm firstly uses RTT similarity to define and establish the delay function of router processing. Then, the random coefficient and other parameters of uncertain factors such as network congestion rate and network delay are calculated, to establish the prediction model of network delay. Then, the bidirectional associative memory neural network algorithm is combined with the normal distribution, and the obtained model is input to get the network delay law. The experimental results show that the proposed algorithm can predict the network delay with high accuracy, and is a feasible prediction algorithm.

Intelligent detection algorithm of MIMO wideband frequency modulated signal in complex multipath channel

The intelligent detection algorithm of MIMO wideband frequency modulated signal based on orthogonal matching tracking has the disadvantages of high network congestion rate, high noise content in signal and large memory space in complex multipath channel. Intelligent detection algorithm of MIMO wideband frequency modulated signal in complex multipath channel is proposed based on fractional Fourier transform. The noise in the signal is analyzed with the hierarchical search introducing into the signal detection process. The signal normalization process is performed to complete the signal detection algorithm. Experiments show that the algorithm can effectively reduce the noise content in the signal, occupying small memory space of the computer to reduce the impact of detection on the computer network.

Online OSPF weights optimization in IP networks

The high volatility of traffic patterns in IP networks calls for dynamic routing schemes allowing to adapt resource utilization to prevailing traffic. In this paper, we focus on the problem of link weight optimization in OSPF networks where the traffic is routed along shortest paths according to the link metrics. We propose an online approach to optimize OSPF weights, and thus the routing paths, adaptively as some changes are observed in the traffic. The approach relies on the estimation of traffic demands using SNMP link counts. Experimental results on both simulated and real traffic data show that the network congestion rate can be significantly reduced with respect to a static weight configuration.

Research on Second Ring Expressway Traffic Congestion in Beijing

According to the urban expressway traffic congestion of Beijing, from the perspective of traffic flow, the study makes a calculation and analysis on road network congestion rate, load of road network and traffic operation index. Training the traffic congestion and load of road network by using the BP neutral network in MATLAB, and put forward some countermeasures to solve the problem of traffic congestion. Through analyzing the load of different section and congestion rate, we can obtain the spatial distribution of the traffic flow and grasp the overall traffic on the Ring Road running load, develop appropriate management control programs for traffic managers, provide service and travel mode for travelers at the same time, so that balance the overall network load tends.