Abstract
The statistical analyses indicate that the measured traffic traces from the packet networks often contain non-stationary effects. In these cases several popular tests for long-range dependence and/or stationarity can result in wrong conclusions and unreliable estimate of the Hurst parameter. In this paper non-stationarities are modeled as the trends and/or level shifts in Internet traffic data. MMPP-Based Hierarchical Model simulation data are used for stationarity tests. Application of testing results are integrated into network resource allocation function as a Partially Observable Markov Decision process. Ill. 4, bibl. 7, tabl. 1 (in English; abstracts in English and Lithuanian). http://dx.doi.org/10.5755/j01.eee.112.6.439
Highlights
Network management techniques have long been of interest to the networking research community
In this paper we study the problem of finding an optimal policy for Network resource allocation as a Partially Observable Markov Decision Process (POMDP)
The tests used in our experiments does not enable to decide between non-stationarities and Long Range Dependence (LRD)
Summary
Network management techniques have long been of interest to the networking research community. Networks can be viewed as a distributed system in which coordinated and informed decision making is crucial for optimal resource allocation. In this paper we study the problem of finding an optimal policy for Network resource allocation as a Partially Observable Markov Decision Process (POMDP). Testing the stationarity of Network traffic is one of the keystone problem. This paper restricts itself to two network management techniques: admission control and the partitioning of transmission and buffer resources among two or more classes of traffic using a common transmission path. The Decision Policy Agent (DPA) model and Network model are presented in fig.. The aim of this paper is to estimate the various stationarity testing procedures for intergation into Network resource allocation agents
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