All-terminal reliability evaluation through a Monte Carlo simulation based on an MPI implementation

All-terminal reliability (ATR), defined as the probability that every node in a network can communicate with every other node, is an important problem in research areas such as mobile ad-hoc wireless networks, grid computing systems, and telecommunications. The assessment of ATR has also been part of related problems like the reliability allocation problem. However, the exact calculation of ATR is a NP-hard problem. To obtain this probability, there are approaches based on analytic methods for small networks or estimation through Monte Carlo simulation (MCS). In this paper, a parallel MCS implementation, based on the Message Passing Interface (MPI) standard is presented. The implementation can take advantage of the existence of multiprocessor thus reducing the time required for the ATR assessment. Three examples related to real network illustrate the benefits.Peer ReviewedPostprint (author’s final draft

Reliability of Mobile Agents for Reliable Service Discovery Protocol in MANET

Recently mobile agents are used to discover services in mobile ad-hoc network (MANET) where agents travel through the network, collecting and sometimes spreading the dynamically changing service information. But it is important to investigate how reliable the agents are for this application as the dependability issues(reliability and availability) of MANET are highly affected by its dynamic nature.The complexity of underlying MANET makes it hard to obtain the route reliability of the mobile agent systems (MAS); instead we estimate it using Monte Carlo simulation. Thus an algorithm for estimating the task route reliability of MAS (deployed for discovering services) is proposed, that takes into account the effect of node mobility in MANET. That mobility pattern of the nodes affects the MAS performance is also shown by considering different mobility models. Multipath propagation effect of radio signal is considered to decide link existence. Transient link errors are also considered. Finally we propose a metric to calculate the reliability of service discovery protocol and see how MAS performance affects the protocol reliability. The experimental results show the robustness of the proposed algorithm. Here the optimum value of network bandwidth (needed to support the agents) is calculated for our application. However the reliability of MAS is highly dependent on link failure probability

An Efficient Algorithm for Computing Network Reliability in Small Treewidth

We consider the classic problem of Network Reliability. A network is given together with a source vertex, one or more target vertices, and probabilities assigned to each of the edges. Each edge appears in the network with its associated probability and the problem is to determine the probability of having at least one source-to-target path. This problem is known to be NP-hard. We present a linear-time fixed-parameter algorithm based on a parameter called treewidth, which is a measure of tree-likeness of graphs. Network Reliability was already known to be solvable in polynomial time for bounded treewidth, but there were no concrete algorithms and the known methods used complicated structures and were not easy to implement. We provide a significantly simpler and more intuitive algorithm that is much easier to implement. We also report on an implementation of our algorithm and establish the applicability of our approach by providing experimental results on the graphs of subway and transit systems of several major cities, such as London and Tokyo. To the best of our knowledge, this is the first exact algorithm for Network Reliability that can scale to handle real-world instances of the problem.Comment: 14 page

An artificial neural network model for optimization of finished goods inventory

In this paper, an artificial neural network (ANN) model is developed to determine the optimum level of finished goods inventory as a function of product demand, setup, holding, and material costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden neurons and one output as the optimum network. The model is tested with a manufacturing industry data and the results indicate that the model can be used to forecast finished goods inventory level in response to the model parameters. Overall, the model can be applied for optimization of finished goods inventory for any manufacturing enterprise in a competitive business environment. © 2011Growing Science Ltd. All rights reserved

ДЕНЕЖНО-КРЕДИТНЫЕ ФАКТОРЫ АКТИВИЗАЦИИ ВНУТРЕННЕГО ИНВЕСТИЦИОННОГО СПРОСА В РОССИЙСКОЙ ЭКОНОМИКЕ

Topic. The article discusses the trends shaping the domestic investment demand in the Russian economy in the context of the regulatory capacity of monetary and credit policy. Purpose. We try to identify factors and conditions that stimulate investment growth in the Russian economy taking into account the active role of the credit system.Methodology. The study is based on the use of systematic, evolutionary and institutional approaches and the artificial neural network method. To calculate data about the volumes and dynamics of investment loans the authors applied the method of indirect calculation using data from Bank of Russia of loans to non-financial enterprises and the Federal state statistics service on the value of business investment in fixed capital, and the share of Bank loans in total sources of financing investments in fixed capital.Result. We discovered some specific features of the influence of the main channels of the transmission mechanism of modern monetary and credit policy of the Bank of Russia on the formation of internal investment demand. The authors understand it as a need, the willingness and ability of economic agentsresidents to the reproduction and accumulation of capital for economic growth based on innovation. According to the obtained results based on analysis of profitability ratios of goods sold (products, works, and services) of main economic activity and interest rates on Bank deposits we found the negative growth trend in the number of main branches of economy, where profitability is lower than the weighted average rate on deposits.Conclusions. We made some suggestions as concerns improving the effects of current monetary and credit policy in the context of forming of internal investment demand. Also, we grounded the principles of choice of strategic priorities of monetary and credit policy adequate to the requirements of sustainable economic growth and interrelated with other components of the macroeconomic policy.Предмет. В статье рассматриваются тенденции формирования внутреннего инвестиционного спроса в российской экономике в контексте регулирующего потенциала денежно-кредитной политики.Цель. Выявить факторы и условия, способствующие росту инвестиций в экономику России с учетом активной роли кредитной системы.Методология. Исследование базируется на использовании системного, эволюционного и институционального подходов, а также метода искусственных нейронных сетей. Для расчета данных об объемах и динамике инвестиционных кредитов применена методика косвенного расчета с использованием данных Банка России о кредитах нефинансовым предприятиям и информации Росстата о величине инвестиций предприятий в основной капитал и доле банковских кредитов в общем объеме источников финансирования инвестиций в основной капитал. Результат. Раскрыты особенности влияния основных каналов трансмиссионного механизма современной денежно-кредитной политики Банка России на формирование внутреннего инвестиционного спроса, под которым авторы понимают потребность, готовность и способность экономических агентов-резидентов к воспроизводству и накоплению капитала, обеспечивающего экономический рост на инновационной основе. По результатам анализа соотношения рентабельности проданных товаров (продукции, работ, услуг) по основным видам экономической деятельности и процентных ставок по банковским депозитам выявлена негативная тенденция роста числа основных отраслей экономики, где рентабельность ниже, чем средневзвешенная ставка по депозитам.Выводы. Вносятся предложения по повышению эффектов современной денежно-кредитной политики в контексте формирования внутреннего инвестиционного спроса. Обоснован выбор стратегических приоритетов денежно-кредитной политики, адекватных требованиям устойчивого экономического роста и взаимоувязанных с другими составляющими макроэкономической политики

Analysis and modelling of flood risk assessment using information diffusion and artificial neural network

Floods are a serious hazard to life and property. The traditional probability statistical method is acceptable in analysing the flood risk but requires a large sample size of hydrological data. This paper puts forward a composite method based on artificial neural network (ANN) and information diffusion method (IDM) for flood analysis. Information diffusion theory helps to extract as much useful information as possible from the sample and thus improves the accuracy of system recognition. Meanwhile, an artificial neural network model, back-propagation (BP) neural network, is used to map the multi-dimensional space of a disaster situation to a one-dimensional disaster space and to enable resolution of the grade of flood disaster loss. These techniques all contribute to a reasonable prediction of natural disaster risk. As an example, application of the method is verified in a flood risk analysis in China, and the risks of different flood grades are determined. Our model yielded very good results and suggests that the methodology is effective and practical, with the potentiality to be used to forecast flood risk for use in flood risk management. It is also hoped that by conducting such analyses lessons can be learned so that the impact of natural disasters such as floods can be mitigated in the future.Keywords: artificial neural network, information diffusion, flood, risk analysis, assessmen

Using Reinforcement Learning to Improve Network Reliability through Optimal Resource Allocation

Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal Policy Optimization (PPO) was used to identify the edge to be improved or a new edge to be added based on the current state of the network and the available budget. To calculate the network’s all-terminal reliability, a reliability polynomial was employed. To understand how the model behaves under a variety of conditions, we explored numerous network configurations with different initial link reliability, added link reliability, number of nodes, and budget structures. We conclude with a discussion of insights gained from our set of designed experiments