Optimal allocation of limited and random network resources to discrete stochastic demands for standardized cargo transportation networks

It is often necessary to determine statistical parameters that characterize the quality of service on the network by managing when designing computer networks using the concept of virtual connections with bypass directions. In many ways, the attainable level of quality of the services provided is determined at the stage of network design, when decisions was made regarding the subscriber capacity of stations, the capacity of bundles of trunk lines, the composition and volume of telecommunication services provided. Despite constant progress in the field of network technologies, the problem of determining the necessary amount of network resources and ensuring the quality of user service remains relevant. In this regard, this article discusses a broadband digital network with service integration, based on an asynchronous network in which an iterative method implemented. Here the flow distribution is determined by the route matrix, and the load distribution between the nodes of each pair of nodes made through the path tree obtained on the matrix of routes when calculating this pair. At the same time, an algorithm has been built for allow optimal allocation of channel resources between circuit switching and packet switching subnets within an asynchronous network.

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The integration research of pre-multicast method and adaptive routing protocol with random network coding based on the OMNeT++ platform

MATEC Web of Conferences ◽

10.1051/matecconf/201818904007 ◽

2018 ◽

Vol 189 ◽

pp. 04007

Author(s):

Qing Nie ◽

Kai Song ◽

Fei Gao

Keyword(s):

Network Coding ◽

Routing Protocol ◽

Network Performance ◽

Random Network ◽

Adaptive Routing ◽

Network Resources ◽

Random Network Coding ◽

Intermediate Node ◽

Network Topologies ◽

Network Transmission

In this paper, both the pre-multicast method and the adaptive routing protocol are proposed. The random network coding with these protocols are not only more stable and reliable, but also improves the utilization of network resources. According to the function of the nodes in the network, this paper establishes the source node model, the intermediate node model and the sink node model based on the OMNeT++ simulation platform. In particular, in this paper, both pre-multicast and adaptive routing modules are combined in the node model. By the simulation, using or not using both pre-multicast module and adaptive routing module in the network transmission module, the network transmission reliability is analyzed. The network performance, such as delay and effective throughput, is evaluated to verify the expected results. Because the designed node models are independent of each other, they can be applied to most real network topologies. Researchers can also modify a module of a model or improve an algorithm to achieve more complex functions according to their own needs.

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Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users

10.20944/preprints201805.0117.v1 ◽

2018 ◽

Author(s):

Erkki Laitinen ◽

Igor Konnov ◽

Aleksey Kashuba

Keyword(s):

Optimal Allocation ◽

Optimization Problems ◽

Telecommunication Network ◽

Test Problems ◽

Network Resources ◽

Convex Optimization Problem ◽

Wireless Telecommunication ◽

Total Capacity ◽

The One ◽

Levels Of Service

We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users requirements. This approach leads to a convex optimization problem with balance and capacity constraints. We present several decomposition type methods to find a solution of this problem, which exploit its special features. We suggest to apply first the dual Lagrangian method with respect to the total capacity constraint, which gives the one-dimensional dual problem. However, calculation of the value of the dual cost function requires solution of several optimization problems. Our methods differ in approaches of solution of these auxiliary problems. We compare the performance of the suggested methods on several series of test problems. They show rather satisfactory convergence. Nevertheless, proper decomposition technique enhance the convergence essentially.

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An Optimal Allocation Method of Power Multimodal Network Resources Based on NSGA-II

Wireless Communications and Mobile Computing ◽

10.1155/2021/9632277 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Ao Xiong ◽

Yuanzheng Tong ◽

Shaoyong Guo ◽

Yanru Wang ◽

Sujie Shao ◽

...

Keyword(s):

Resource Allocation ◽

Optimal Allocation ◽

Optimization Method ◽

Resource Optimization ◽

Normal Operation ◽

Network Resources ◽

Nsga Ii ◽

Network Resource ◽

Allocation Method ◽

Multimodal Network

Basic services for power business were provided by the power multimodel network providers. However, because the power multimodal network is usually complex and changeable, the service of power business is often unstable. This problem can be solved by a suitable network resource optimization method. Therefore, how to design a network resource optimization method that seeks a compromise between multiple performance indicators that achieve the normal operation of power multimode networks is still extremely challenging. An optimal allocation method of power multimodal network resources based on NSGA-II was proposed by this paper. Firstly, the power multimodal network-resource model is established, and the problems existing in the resource optimization process are analyzed. Secondly, preprocessing technology and indirect coding technology are applied to NSGA-II, which solves the coding problem and convergence problem of the application of genetic algorithm to the optimization of network resource allocation. Finally, the simulation results show that, compared with the control algorithm, this method has further optimized the various indicators of the resource allocation of the power multimodal network, and the performance has been improved by more than 6%.

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