Advances in Modern Performance Analysis: A New Approach

未来的高速网络将支持数据、语音、图像、视频等多种业务，具有不同的业务流特性和服务质量（QoS）要求。开发一个能有效地支持这种QoS要求的广域分组网络的设计、控制和管理框架是一个根本性的挑战，传统的网络远程业务分析和QoS保证框架要么是确定性的，要么是概率性的。在确定性方法中，使用最坏情况的流量包络。这很可能会导致保守的资源分配策略和网络资源的低效使用。在标准的概率框架中，流量通常被建模为泊松过程的变体，例如，MMPP、MAP等。这些模型适用于传统的远程通信分析，但只能捕获数据中简单而有限的相关结构。研究人员建议显着扩展分析方法，以解决现代计算机和通信网络中出现的复杂远程通信问题。他们的方法基于经典的投票定理，处理了一大类到达过程，包括标准马尔可夫过程、一般周期过程、时间序列描述的过程（自回归和移动平均），或甚至一般的过程所描述的联合分布的数量到达等间隔的非，重叠的离散区间。我们会特别注意推导出算法解，这些解在数值上是稳健的，即使在系统利用率很高的情况下也是有效的。研究人员提出的方法是统一的，它避免了根的发现，不像矩阵分析方法或最近推出的状态空间谱分解方法，它不涉及潜在的昂贵的迭代。此外，它是transform-free的，可以提供一个简单的形式的解决方案，适合进一步的分析研究，如尾部行为orasymptomatic analysis.The研究人员一般到达过程可以用于端到端的性能分析，一旦离开过程从网络节点是适当的近似或probabilisticallybounded。由于它们不限于传统的马尔可夫模型，因此这种方法似乎是可行的。研究人员建议实现我们的通用算法，并将其作为科学包提供给研究界。

The future high-speed networks are expected to support a variety of services such as data,voice, image, and video with diverse traffic flow characteristics and Quality-of-Service (QoS)requirements. Developing a framework for the design, control and management of widearea packet networks which can effciently support such QoS requirements is a fundamentalchallenge.Conventional frameworks for the teletraffic analysis of networks and QoS guarantees areeither deterministic or probabilistic. In the deterministic approach, the worst-case trafficenvelopes are used. This most likely will lead to conservative resource allocation policiesand inefficient usage of the network resources. In the standard probabilistic frameworks,the traffic is usually modeled as a variant of Poisson process, e.g., MMPP, MAP, etc. Suchmodels are amenable to traditional teletraffic analysis but only capture simple and limitedcorrelation structures in the data.The researchers propose to significantly expand the analytical methodology in solving complex teletraffic problems arising in modern computer and communications networks. Their approachwhich is rooted in classical ballot theorem handles a large class of arrival processes includingthe standard Markovian processes, general periodic processes, processes described bytime-series (auto-regressive and moving average), or even general processes described by thejoint distribution of number of arrivals in equally-spaced non-overlapping discrete intervals.Special attention will be paid in deriving algorithmic solutions which are numerically robustand efficient even when the system utilization is high.The researchers proposed approach is unifying, it avoids root-findings, and unlike the matrix analytical approach or recently introduced state-space spectral decomposition method, it does notinvolve potentially expensive iterations. Further, it is transform-free and may provide asimple form of solution amenable for further analytical studies such as tail behavior orasymptotic analysis.The researchers general arrivals processes could be used for the end-to-end performance analysis once the departure process from a network node is appropriately approximated or probabilisticallybounded. Since they are not limited to traditional Markovian models, such an approachappears to be viable.The researchers propose to implement our general algorithms and make them available as a scientific package to the research community.