Measurement-based Traffic Characterization and Resource Allocation

与传统的语音和数据网络相比，下一代网络将对流量控制提出更困难的挑战，因为(1)承载的流量将更加多样化和复杂，(2)流量速率将更快，以及(3)必须为需要服务质量(QoS)的某些应用保护网络性能水平。流量分析人员试图从收集的流量测量中了解流量的随机性质，并推导流量模型和控制算法来管理网络资源(如ATM网络中的连接准入控制)。不幸的是，流量分析通常是一个耗时的过程，需要高水平的统计专业知识。人们已经研究了许多随机交通模型，但普遍缺乏评估其对实际交通有效性的方法。这项研究的目的是通过开发系统的方法来处理原始流量测量，得出流量特征，并将这些特征应用于实时资源管理，从而提高对真实网络流量的理解。该方法基于交通模型和统计估计技术的模块化库来拟合和测试每个模型与观察到的交通。拟议的研究项目将分三个阶段进行。第一阶段将开发业务模型的模型库，包括众所周知的马尔可夫开关模型、马尔可夫调制泊松过程(MMPP)、马尔可夫调制流体、自回归过程和分数布朗运动或其他自相似模型。每个模型由许多可估计的参数和一组结构属性组成。第一阶段的研究将使用最大似然估计或贝叶斯估计为每个交通模型推导出参数估计方法，并根据每个模型的特定属性使用假设检验、卡方拟合优度检验和其他检验来评估每个模型的有效性。在给定原始流量测量的情况下，可以随时系统地选择库中最适合的流量模型。拟议项目的第二阶段将从第一阶段获得有效的顺序估计器，并在软件中实现这些估计器，以用于实时交通分析和可视化。该软件将与硬件流量收集系统一起工作，以推断一般统计特征(如突发性)并显示最适合的流量模型。流量模型将揭示流量的基本行为属性。最后，第三阶段将使用前面阶段的估计方法和软件来研究实时资源分配。该方法不是假设流量模型是先验的，而是动态地从库中选择最合适的流量模型，并实时调整资源分配决策。这种方法可以消除传统静态方法中的不确定性和不准确性。针对ATM连接接纳控制的具体问题，将通过OPNET仿真来研究该方法。拟议项目的成功成果将是软件工具和算法，帮助网络管理员深入了解网络流量的性质，并微调他们对网络资源的控制。该项目还将通过提供工具来检查实际流量和了解潜在行为，从而对研究人员、教育工作者和学生有用。

Compared to traditional voice and data networks, next generation networks will present more difficult challenges to traffic control because (1) the carried traffic will be much more diverse and complex in nature, (2) traffic rates will be faster, and (3) network performance levels must be protected for certain applications requiring a quality of service (QoS). Traffic analysts attempt to gain an understanding of the stochastic nature of the traffic from volumes of collected traffic measurements, and derive traffic models and control algorithms to manage network resources (such as connection admission control in ATM networks). Unfortunately, traffic analysis is typically a time consuming process and requires a high level of statistical expertise. Many stochastic traffic models have been studied but methods for evaluating their validity for real traffic are generally lacking. The objective of the proposed research is to improve understanding of real network traffic by developing systematic methods for processing raw traffic measurements, deriving traffic characteristics, and applying these characteristics to real-time resource management. The approach is based on a modular library of traffic models and statistical estimation techniques to fit and test eachmodel to observed traffic. The proposed research project will be carried out in three phases. The first phase will develop a modular library of traffic models including the well-known Markov on-off model, Markov modulated Poisson process (MMPP), Markov modulated fluid, autoregressive process, and fractional brownian motion or other self-similar models. Each model consists of a number of estimatable parameters and a set of structural properties. Research in the first phase will derive parameter estimation methods for each traffic model using maximum likelihood estimation or Bayesian estimation, and evaluate the validity of each model using hypothesis testing, chi-square goodness of fit tests, and other tests depending on the particular properties of each model. Given raw traffic measurements, the best-fit traffic model in the library can be methodically selected at any time. The second phase of the proposed project will derive efficient sequential estimators from the first phase and implement these estimators in software for real-time traffic analysis and visualization. The software will work with a hardware traffic collection system to infer general statistical characteristics (such as burstiness) and display the best fitting traffic model. The traffic model will reveal the underlying behavioral properties of the traffic. Finally, the third phase will study real-time resource allocation using the estimation methods and software from the earlier phases. Instead of assuming a traffic model a priori for resource allocation, the proposed method will dynamically select the best fitting traffic model from a library and adapt resource allocation decisions in real time. This approach may eliminate the uncertainty and inaccuracy in the traditional static approach. The method will be studied by means of OPNET simulations for the specific problem of ATM connection admission control. Successful results from the proposed project will be software tools and algorithms to help networkadministrators gain insight into the nature of network traffic and fine-tune their control of network resources. The project will also be useful to researchers, educators, and students by providing tools to examine real traffic and understand underlying behavior.