1,189 research outputs found
Lukashenko, Putin and the protests: why Belarus is being pulled further into Russia’s orbit
No full textFor almost four months, protesters have taken to the streets of Belarus demanding the resignation of President Alexander Lukashenko. Oleg Chupryna argues that with Lukashenko increasingly reliant on Vladimir Putin’s support, there is a risk Belarus could be pulled further under the influence of Russia
Rare-Event Simulation for the Hitting Time of Gaussian Processes
No full textIn reliability theory and network performance analysis a relevant role is played by the time needed to reach a given threshold, known in probability theory as hitting time. Although such issue has been widely investigated, closed-form results are available only for independent increments of the input process. Hence, in this paper we focus on the estimation of the upper tail of the hitting time distribution for general Gaussian processes by means of discrete-event simulation. Indeed, Gaussian processes often arise as a powerful modelling tool in many real-life systems and suitable ad-hoc techniques have developed for their analysis and simulation. Since the event of interest becomes rare as the threshold increases, a variant of Conditional Monte Carlo, based on the bridge process, is introduced and the explicit expression of the estimator is derived. Finally, simulation results highlight the unbiasedness and effectiveness (in terms of relative error) of the proposed approac
On conditional Monte Carlo estimation of busy period probabilities in gaussian queues
No full textDue to the self-similar nature of broadband traffic, the arrival rate can persist on relatively high values for a considerable amount of time. Such a behavior, closely related to the duration of busy periods, has a deep impact on queueing performance in terms of loss probability and distribution of losses. In the paper we consider the probability that the normalized cumulative workload grows at least as the length T of the considered interval in case of Gaussian input traffic. As T increases, the event becomes rare and standard Monte Carlo simulation would require a large number of generated sample paths to get an accurate estimate. To cope with this problem, we propose a variant of the well-known conditional Monte Carlo method, in which conditioning is expressed in terms of the bridge process.We derive the analytical expression of the estimator and verify its effectiveness through simulations
Conditional Monte Carlo Estimation of High Activity Period Duration in Gaussian Queues
No full textDue to the self-similar nature of broadband traffic, the arrival rate can persist on relatively high values for a considerable amount of time. Such a behavior, closely related to the duration of busy periods, has a deep impact on queueing performance in terms of loss probability and distribution of losses. In the paper we consider the probability that the normalized cumulative workload grows at least as the length T of the considered interval in case of Gaussian input traffic. As T increases, the event becomes rare and standard Monte Carlo simulation would require a large number of generated sample paths to get an accurate estimate.
To cope with this problem, we propose a variant of the well-known conditional Monte Carlo method, in which conditioning is expressed in terms of the bridge process. We derive the analytical expression of the estimator and verify its effectiveness through simulations
On the use of a bridge process in a conditional monte carlo simulation of Gaussian queues
No full textIn spite of their low frequency, rare events often play a major role in determining systems performance. In most cases they can be analysed only through simulation with ad-hoc techniques since traditional Monte Carlo approaches are quite inefficient in terms of simulation length and/or estimation accuracy. Among rare event simulation techniques, conditional Monte Carlo is an interesting approach as it always leads to variance reduction. Unfortunately, it is often impossible, or at least very difficult, to find a suitable conditioning strategy. To tackle this issue, the applicability of a bridge process is proposed in the case of queueing systems with Gaussian inputs. In more detail, overflow probability and busy-period length are investigated and the analytical expressions of the corresponding estimators are derived. Finally, the effectiveness of the proposed approach is investigated through simulations
Statistical analysis of RMD method for generating fractional Brownian motion sample paths
No full textFractional Brownian Motion (FBM) has emerged as a powerful traffic
model, able to fit the long-term correlations of actual network traffic flows with
a limited number of parameters. An open research issue is the fast generation of
FBM sample paths to be used in network simulations, which might require a large
number of samples in case rare events are involved. In this paper we analyse the
statistical behaviour of the well-known Random Midpoint Displacement algorithm,
an approximate generation algorithm whose complexity is linear with the simulation
length, taking into account marginal distribution as well as correlation structure
On the Overflow Probability Asymptotics in a Gaussian Queue
No full textGaussian processes are a powerful tool in networkmodeling since they permit to capture the longmemory property of actual traffic flows. In more detail, under realistic assumptions, fractional Brownian motion (FBM) arise as the limit process when a huge number of on-off sources (with heavy-tailed sojourn times) are multiplexed in backbone networks. This paper studies fluid queuing systems with a constant service rate fed by a sum of independent FBMs, corresponding to the aggregation of heterogeneous traffic flows. For such queuing systems, logarithmic asymptotics of the overflow probability, an upper bound for the loss probability in the corresponding finite-buffer queues, are derived, highlighting that the FBM with the largest Hurst parameter dominates in the estimation. Finally, asymptotic results for the workload maximum in the more general case of a Gaussian input with slowly varying at infinity variance are given
On Conditional Monte Carlo Estimation of Rare Events in Gaussian Queuing Systems
No full textWe study the estimation of the probability Gaussian process being “above
the diagonal” over long time interval [0; T]. This event becomes rare when T grows,
hence standard Monte Carlo requires a large number of generated sample paths. We
discuss the application of well-known conditional Monte Carlo method for variance
reduction of the target probability estimator
Оценивание качества обслуживания систем с гауссовским входным потоком
No full textВ связи с распространением различных сетевых приложений возникает необходимость анализа загрузки в сетях, т. е. расчета различных характеристик, таких, например, как емкости буферов, пропускная способность и т. д.
Последние два десятилетия ознаменовались существенными достижениями в исследовании сетевого трафика. Было, в частности, установлено, что процессы, протекающие в компьютерных сетях, могут обладать фрактальными свойствами (эффект самоподобия) и долговременной зависимостью (долгой памятью) [1]. Такие свойства радикально отличают современные модели от пуассоновских моделей, которые адекватно описывали сетевые процессы на протяжении долгого времени. Например, пуассоновские модели опираются на экспоненциальные распределения интервалов входного потока и времени обслуживания заявок (пакетов) и обладают короткой памятью.
Столь существенное отличие в свойствах сетевого трафика потребовало разработки новых моделей и методов их анализа. В частности, наличие долговременной зависимости между данными сетевого трафика сделало весьма популярными модели, основанные на гауссовских процессах. Самым известным и изученным самоподобным гауссовским процессом с долговременной зависимостью является дробное Броуновское движение (ДБД). Так, например, данный процесс, названный фрактальным трафиком, впервые был использован в качестве модели входного потока в работе [2]. Выбор такого рода входных потоков продиктован функциональными предельными теоремами, согласно которым гауссовские процессы возникают при суперпозиции большого числа независимых так называемых on/off-источников с тяжелыми хвостами на больших масштабах времени [3].
Цель нашей работы состоит в том, чтобы при помощи асимптотических и статистических методов получить оценки основных характеристик систем обслуживания при разных гауссовских процессах, включая комбинацию ДБД с разными значениями параметра Херста.
Литература в стиле:
1. Leland W. E., Taqqu M. S., Willinger W., Wilson D. V. On the self-similar nature of ethernet traffic (extended version) // IEEE/ACM Transactions of Networking. 1994. No 2. С. 1–15.
2. Norros I. A storage model with self-similar input // Queueing Syst. 1994. No 16. С. 387–396.
3. Taqqu M. S., Willinger W., Sherman R. Proof of a fundamental result in self-similar traffic modeling // Computer communication review. 1997. No 27. С. 5–23
Trade policies in Central Asia after EAEU enlargement and after Russian WTO accession: regionalism and integration into the world economy revisited
No full textThis dataset reproduces empirical results for the paper: Andrzej Cieślik & Oleg Gurshev (2023) Trade policies in Central Asia after EAEU enlargement and after Russian WTO accession: regionalism and integration into the world economy revisited, Eurasian Geography and Economics, DOI: 10.1080/15387216.2022.2162098
It includes data, graphs, and 3SLS gravity analysis performed in the paper. This research was funded in whole by National Science Centre, Poland under PRELUDIUM 20 grant №2021/41/N/HS4/00759. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission
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