1,152 research outputs found
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
Was There a Quiet Revolution? Belarus After the 2006 Presidential Election
No full textThe 2006 presidential election in Belarus mobilized a large cross-section of society to protest against the Lukashenko regime. Although unprecedented, the mass mobilization was short-lived, failing to develop into another kind of coloured revolution in the region. The key to our understanding of the endurance of Lukashenko's regime seems to lie in its internal environment, and notably, in the seemingly contradictory feature of the Belarusian electorate. Not only do they fully identify with the president, thus effectively legitimizing his politics and policies; they also do so knowingly, through their strategic learning of how to survive and even thrive under Lukashenko's regime. This type of learning, however, may not necessarily lead to a critical reflection of the regime's malpractice, and thus is unlikely to challenge its foundations
Rational Legitimacy as a Basic of Presidency of Alexander Lukashenko
No full textВ данной работе представлен анализ оснований рациональной легитимности политического режима президента Республики Беларусь А. Г. Лукашенко с точки зрения категорий экономики и безопасности. Делается вывод, что проводимая президентом политическая линия будет позволять и дальше поддерживать легитимность политического режима.This article is dedicated to the analysis of the grounds of rational legitimacy of the Alexander Lukashenko in Belarus. This analysis is considered of the economic and security categories. The author draws a conclusion about strengthening of Lukashenko’s regime legitimacy in the future
On the efficiency of bridge Monte-Carlo estimator
No full textНаличие долговременной зависимости в современных сетях передачи данных приводит к тому, что объем передаваемого трафика может быть большим на протяжении значительного периода времени. Это, в свою очередь, влечет перегрузку систем на протяжении длительного периода времени. В данной работе рассматривается задача оценки вероятности занятости системы обслуживания с гауссовским входным потоком в течение некоторого заданного периода T. При больших значениях T интересующее нас событие является редким, и для оценки его вероятности с приемлемой точностью необходимо использовать специальные методы понижения дисперсии оценки. В статье рассмотрен частный случай условного метода Монте Карло, который заключается в том, что искомая вероятность может быть выражена как математическое ожидание некоторой функции от так называемого гауссовского моста. Исследована эффективность предложенной процедуры, а также влияние шага дискретизации на свойство получаемой оценки.Long-term correlation is a key feature of traffic flows and has a deep impact on network performance. Indeed, the arrival rate can persist on relatively high values for a considerable amount of time, provoking long busy periods and possibly bursts of lost packets. The authors focus on Gaussian processes, well-recognized and flexible traffic models, and consider the probability that the normalized cumulative workload grows at least as the length T of the considered interval. As T increases, such event becomes rare and ad-hoc techniques should be used to estimate its probability. To this aim, the authors present a variant of the well-known conditional Monte-Carlo (MC) method, in which the target probability is expressed as a function of the corresponding bridge process. In more detail, they derive the analytical expression of the estimator, verify its effectiveness through simulations (for different sets of parameters), and investigate the effects of the discretization step
A Gaussian approximation of the distributed computing process
No full textThe authors propose a refinement of the stochastic model describing the dynamics of the Desktop Grid (DG) project with many hosts and many workunits to be performed, originally proposed by Morozov et al. in 2017. The target performance measure is the mean duration of the runtime of the project. To this end, the authors derive an asymptotic expression for the amount of the accumulated work to be done by means of limit theorems for superposed on-off sources that lead to a Gaussian approximation. In more detail, depending on the distribution of active and idle periods, Brownian or fractional Brownian processes are obtained. The authors present the analytic results related to the hitting time of the considered processes (including the case in which the overall amount of work is only known in a probabilistic way), and highlight how the runtime tail distribution could be estimated by simulation. Taking advantage of the properties of Gaussian processes and the Conditional Monte-Carlo (CMC) approach, the authors present a theoretical framework for evaluating the runtime tail distribution
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
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
Questioning Democracy Promotion: Belarus' response to the 'colour revolutions'
No full textThe article focuses on the aftermath of the colour revolutions by analysing and questioning the real success, as often depicted by the West, of democracy promotion in the East European region. First of all, the article challenges the conventional logic of democracy promotion – even when backed by moral reasoning and resource availability – as sufficient and adequate for instigating democratic change in non-liberal regimes. By examining the case of Belarus it further contends that authoritarian regimes effectively learn to resist and counteract foreign-led democracy promotion, and often do so legitimately, with a minimal use of force. The article concludes that in order to exercise democracy promotion (if such a thing is possible at all) a far deeper understanding of autocratic narratives is needed, associated with a much closer look at societal norms and values, as well as an individual country's geopolitical resources and strategies
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 Effective Envelopes for Fluid Queues with Gaussian Input
No full textThanks to their flexibility and compact characterization, Gaussian processes have emerged as popular models to describe the traffic dynamics in a wide class of the modern telecommunication networks. A relatively new characterization of traffic flows is based on the effective envelopes, which represent a probabilistic generalization of the arrival curve of Network Calculus. In this paper, we analyse the effective envelopes for a general Gaussian process and use these results to derive non-asymptotic performance bounds for a fluid queuing system. To highlight the effectiveness of the proposed approach, numerical results are shown taking into account heterogeneous traffic flows as well as different correlation structure
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