network resource reallocation strategy based on an improved capacity-load model

Zhou, Jian; Huang, Ning; Sun, Xishan; Xing, Liudong; Zhang, Shuo

doi:10.17531/ein.2015.4.2

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…In common networks, network congestion diffusion models considering congestion related information can be divided into two main types according to whether overload nodes or edges will be removed from networks. The first type focuses mainly on unrecoverable physical node or edge failures and neglects the situations of recoverable performance degradation, such as the widely known cascading models, including capacity-load models [31], [32], the binary influence models [11], [33], and stochastic flow network models [34]. In power grid networks and some other networks, the nodes or edges are indeed removed because the damaged nodes or edges cannot be recovered and used again.…”

Section: Introductionmentioning

confidence: 99%

“…In power grid networks and some other networks, the nodes or edges are indeed removed because the damaged nodes or edges cannot be recovered and used again. In these networks, the information influence is always considered as a control signal and taken as a node and then interdependent networks appear [11], [32]- [36]. However, in most networks, such as, road networks or Internet, congested nodes or edges should not be removed because they will soon recover their performance with less congestion, and then performance degradation models appear.…”

Section: Introductionmentioning

confidence: 99%

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Network Congestion Diffusion Model Considering Congestion Distribution Information

2019

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Network congestion diffusion has become the most stubborn disease and scourge of networks. With the help of widely used congestion distribution information, making full use of network capacity dynamically is a feasible and hopeful way to alleviate network congestion diffusion. Existing studies on network congestion diffusion considering congestion distribution information mainly focus on road networks and describe the distribution information of congestion areas with statistical parameters but not take dynamical congestion distribution into account. However, it is difficult to quantify dynamical congestion distribution as it has multiple influencing factors and complex dynamical coupling relationships, and thus there is still a lack of common network congestion diffusion model considering dynamical congestion distribution information. Inspired by the Langevin diffusion model in signal transduction networks, we propose a novel model for common network congestion diffusion considering the influence of dynamical congestion distribution information based on a set of differential equations. In these equations, we quantify the crosstalk influence of dynamical distribution information by a parameter with reference to the routing optimization method in the ant colony algorithm. And, then firstly the complex dynamical coupling network congestion diffusion under the influence of congestion distribution information is analyzed and simulated in a measurable way. The simulation results prove that there are obvious alleviated effects on network congestion diffusion with proper information influence weights, which is shown to be a bathtub curve relationship. Our model provides a simple mathematical approach to discover the relationship between network congestion diffusion and the influence of dynamical congestion distribution information. Based on this relationship, we can relieve network congestion by dynamically adjusting congestion distribution information influence.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Congestion Diffusion Model Considering Congestion Distribution Information

2019

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“…Decision-making is one of the most common and important activities in many situations of modern society including energy planning (Soroudi and Amraee, 2013), network resource reallocation (Zhou et al, 2015) and communication systems (Pham, 1997). Due to the limitation of human beings and modern technologies, it is impossible to guarantee that all the decisions will be correct all the time.…”

Section: Introductionmentioning

confidence: 99%

Reliability and Cost-Benefit Analysis for Two-Stage Intervened Decision-Making Systems with Interdependent Decision Units

Lin

Pham²

2019

Int J Math, Eng, Manag Sci

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This paper deals with a special type of voting systems, called two-stage intervened decision-making systems, in which the decision time of each decision unit will be a random variable and some supervising mechanism is included. A new decision rule is applied to such kind of systems, which makes the decision units become interdependent from each other. The reliability and cost-benefit models are developed. The optimization for the models is discussed and the optimal solution for a special case is also derived. A numerical example for model optimization is presented as well as some model comparison. Even though a specific application is used for model formulation and derivation throughout this paper, the modeling results can be easily modified and applied to many other systems.

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“…Under different application demands, unreasonable resource allocation can trigger cascading failures, which begin from overloads of some nodes . Allocating limited resources to achieve high network reliability is significant in network design, and users mostly concern whether the application demands could be satisfied to a great extent. Node resource allocation is an important part of resource allocation, including two aspects: the processing ability and the buffer size .…”

Section: Introductionmentioning

confidence: 99%

End user–oriented node resource allocation: An application‐based method

Zhang

Huang

2019

Quality & Reliability Eng

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For a network, how to allocate limited resources to achieve high network reliability has been concerned by engineers. From the perspective of the users, the quality of applications is the most intuitive perception of network reliability. Previous allocation methods focus little on applications, which makes it difficult to optimize resource with given application requirements. This paper proposes an application‐based resource allocation method during network design. Under given application scenario and resource constraints, the allocation model is constructed to maximize application reliability, which is presented as a novel user‐oriented metric to assess the quality of applications. Furthermore, improved genetic algorithm (GA) is adopted to obtain optimal allocation strategy. To improve the efficiency of the algorithm, application‐affected node importance is evaluated to generate initial population. Simulation results demonstrate that the proposed method can achieve higher application reliability than the other three allocation methods when applied to an Avionics Full Duplex Switched Ethernet (AFDX) case. This makes it possible to optimize resource allocation in the light of different application demands in network design.

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network resource reallocation strategy based on an improved capacity-load model

Abstract: Article citation info: Zhou J, huAng n, Sun X, Xing L, ZhAng S. network resource reallocation strategy based on an improved capacity-load model. Eksploatacja i niezawodnosc - Maintenance and Reliability 2015; 17 (4): 487-495, http://dx.doi.org/10.17531/ein.2015.4

Cited by 15 publications

References 35 publications

Network Congestion Diffusion Model Considering Congestion Distribution Information

Network Congestion Diffusion Model Considering Congestion Distribution Information

Reliability and Cost-Benefit Analysis for Two-Stage Intervened Decision-Making Systems with Interdependent Decision Units

End user–oriented node resource allocation: An application‐based method

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