Method for optimal allocation of network resources based on discrete probability model

2021 ◽  
Author(s):  
Zhengqiang Song ◽  
Guo Hao
Keyword(s):  
Optimal Allocation ◽  
Probability Model ◽  
Network Resources ◽  
Discrete Probability

scholarly journals Calculating algorithm of service quality statistical parameters for asynchronous network subscribers

2020 ◽  
Vol 20 (3) ◽  
pp. 1485
Author(s):  
Kemelbekova Zhanar Satibaldievna ◽  
Sembiyev O.Z ◽  
Umarova Zh.R
Keyword(s):  
Optimal Allocation ◽  
Flow Distribution ◽  
Circuit Switching ◽  
Network Resources ◽  
Statistical Parameters ◽  
Telecommunication Services ◽  
Digital Network ◽  
Asynchronous Network ◽  
The Matrix

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.

MODEL PERFORMANCE MEASURES FOR EXPECTED UTILITY MAXIMIZING INVESTORS

2003 ◽  
Vol 06 (04) ◽  
pp. 355-401 ◽  
Author(s):  
CRAIG FRIEDMAN ◽  
SVEN SANDOW
Keyword(s):  
Performance Measures ◽  
Expected Utility ◽  
Probability Model ◽  
Model Performance ◽  
Performance Measure ◽  
Relative Performance ◽  
Conditional Probability Density ◽  
New Paradigm ◽  
Discrete Probability ◽  
Wide Range

We examine model performance measures in four contexts: Discrete Probability, Continuous Probability, Conditional Discrete Probability and Conditional Probability Density Models. We consider the model performance question from the point of view of an investor who evaluates models based on the performance of the (optimal) strategies that the models suggest. Under this new paradigm, the investor selects the model with the highest estimated expected utility. We interpret our performance measures in information theoretic terms and provide new generalizations of entropy and Kullback-Leibler relative entropy. We show that the relative performance measure is independent of the market prices if and only if the investor's utility function is a member of a logarithmic family that admits a wide range of possible risk aversions. In this case, we show that the relative performance measure is equivalent to the (easily understood) differential expected growth of wealth or the (familiar) likelihood ratio. We state conditions under which relative performance measures for general utilities are well approximated by logarithmic-family-based relative performance measures. Some popular probability model performance measures (including ROC methods) are not consistent with our framework. We demonstrate that rank based performance measures can suggest model selections that are disastrous under various popular utilities.

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

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.

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

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%.

scholarly journals Applied Research on Distributed Generation Optimal Allocation Based on Improved Estimation of Distribution Algorithm

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2363
Author(s):  
Lei Yang ◽  
Xiaohui Yang ◽  
Yue Wu ◽  
Xiaoping Liu
Keyword(s):  
Distributed Generation ◽  
Distribution System ◽  
Optimal Allocation ◽  
Probability Model ◽  
Hybrid Approach ◽  
Posteriori Probability ◽  
Estimation Model ◽  
Dominant Group ◽  
A Posteriori Probability ◽  
Distribution Estimation

Most of the current algorithms used to solve the optimal configuration problem in the distributed generation (DG) of electricity depend heavily on control parameters, which may lead to local optimal solutions. To achieve a rapid and effective algorithm of optimized configuration for distributed generation, a hybrid approach combined with Bayesian statistical-inference and distribution estimation is proposed. Specifically, a probability distribution estimation model based on the theory of Bayesian inference is established, then a posteriori probability model with the prior distribution and the conditional distribution is generated, and new individual generators are formed into a dominant group. The information of each individual of this dominant group is used to update the probability model and the updated posteriori probability is used for sampling until the optimal solution is obtained. Finally, the 12 bus, 34 bus and 69 bus radial distribution system is used as an example and comparison is performed to show the effectiveness of the proposed algorithm.

scholarly journals Pareto Optimal Allocation under Uncertain Preferences

2017 ◽  
Author(s):  
Haris Aziz ◽  
Ronald de Haan ◽  
Baharak Rastegari
Keyword(s):  
Probability Distribution ◽  
Optimal Allocation ◽  
Probability Model ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Pareto Optimal ◽  
Linear Orders ◽  
The Difference ◽  
Uncertain Preferences ◽  
Uncertainty Models

The assignment problem is one of the most well-studied settings in social choice, matching, and discrete allocation. We consider this problem with the additional feature that agents' preferences involve uncertainty. The setting with uncertainty leads to a number of interesting questions including the following ones. How to compute an assignment with the highest probability of being Pareto optimal? What is the complexity of computing the probability that a given assignment is Pareto optimal? Does there exist an assignment that is Pareto optimal with probability one? We consider these problems under two natural uncertainty models: (1) the lottery model in which each agent has an independent probability distribution over linear orders and (2) the joint probability model that involves a joint probability distribution over preference profiles. For both of these models, we present a number of algorithmic and complexity results highlighting the difference and similarities in the complexity of the two models.

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