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Measurement-based quality of service provisioning in multimedia telecommunication networks.

机译:多媒体电信网络中基于测量的服务提供质量。

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In modern high-speed telecommunication networks, statistical multiplexing of Variable Bit Rate (VBR) sources provides a means of effectively utilizing the network resources without violating the desirable Quality of Service (QoS). QoS is quantified as the maximum allowable fraction of lost or delayed packets. To maintain QoS, a Call Admission Control (CAC) mechanism, which decides whether to admit a new call based on an estimate of the resulting buffer overflow probability, has to be implemented. Large Deviations theory provides useful approximations of the buffer overflow probability for many important classes of arrival processes, provided that an exact stochastic model for the arrival process is known.; In this work, we consider the problem of estimating buffer overflow probabilities when the statistics of the input traffic are not known and have to be estimated from measurements. In this case, a certainty equivalence approach is not adequate, since it can substantially underestimate the overflow probability. We are proposing new estimators for the overflow probability in a queue fed by a Markov-modulated process (MMP) that are less likely to lead to underestimation. To that end, we establish a theorem that can be viewed as an inverse of Sanov's theorem for Markov chains. Computing these new estimators amounts to solving nonlinear programming problems with special structure. We develop some special algorithms that take advantage of this structure to solve these problems more efficiently.; We then address the issue of optimally modeling a generic discrete-time, continuous range stochastic process as an MMP, based on a single, finite realization of the process. We investigate methods for optimal model selection using maximum likelihood techniques, including the Akaike Information Criterion .; Lastly, we develop an importance sampling technique for obtaining small buffer overflow probabilities in a queue fed by a large number of independent periodic sources, via simulation. This traffic model accommodates ON-OFF periodic traffic models and sequences of bit rates generated by actual VBR sources. We devise a heuristic change of measure and demonstrate its efficiency through numerical results. Our method is applicable in both the continuous and the discrete time case as well as for homogeneous and heterogeneous sources.
机译:在现代高速电信网络中,可变比特率 VBR )源的统计复用提供了一种有效利用网络资源而又不会违反期望的 Quality of服务 QoS )。 QoS被量化为丢失或延迟的数据包的最大允许部分。为了维持QoS,必须实施呼叫允许控制 CAC )机制,该机制基于对结果缓冲区溢出概率的估计来决定是否允许新呼叫。 。大偏差理论为许多重要的到达过程类提供了缓冲区溢出概率的有用近似值,前提是已知到达过程的精确随机模型。在这项工作中,当输入流量的统计信息未知且必须根据测量值进行估算时,我们将考虑估算缓冲区溢出概率的问题。在这种情况下,确定性等效方法是不够的,因为它可能会大大低估溢出概率。我们建议对由马尔可夫调制过程 MMP )馈送的队列中的溢出概率进行新的估计,这不太可能导致低估。为此,我们建立了一个定理,该定理可以看作是马尔可夫链的Sanov定理的逆。计算这些新的估计量等于解决具有特殊结构的非线性编程问题。我们开发了一些特殊的算法,利用这种结构来更有效地解决这些问题。然后,我们基于单个有限的过程实现,来解决将通用离散时间,连续范围随机过程作为MMP优化建模的问题。我们研究了使用最大似然技术(包括赤池信息准则)进行最佳模型选择的方法。最后,我们通过仿真开发了一种重要的采样技术,以获取由大量独立周期源提供的队列中的较小缓冲区溢出概率。此流量模型适应实际VBR源生成的ON-OFF周期性流量模型和比特率序列。我们设计了一种启发式的度量更改,并通过数值结果证明了其有效性。我们的方法适用于连续时间和离散时间以及均质和非均质源。

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