Performance Analysis of Multiple Server Queues in Conjunction with MMPP Fitting for Self-Similar Traffic
Date Issued
2006
Date
2006
Author(s)
Chen, Chun-Yang
DOI
en-US
Abstract
Seminal studies have shown that real Internet traffic is self-similar, that is, the traffic posseses long-range dependence property. This self-similar nature degrades the network performance. Most of the self-similar traffic models are asymptotic in nature thus they are less effective in view of queueing-based performance when buffer sizes are small. In view of this, some studies argued that short-range dependent models could fit into self-similar traffic to some extent. As queueing theory techniques are available for these models, thereby one can have network performance measures of the self-similar traffic.
In addition, matrix-analytical methods (MAMs) have become very popular in the performance analysis of queueing system with Markovian input. The method is specifically useful when buffer sizes are not too large. We could make use of this technique in the analysis of multiple server systems with finite buffers. A multiple server system is a facility that let several branch traffics enter into system and copes with fixed number of packets at one processing time. Besides, multiple server systems are basic modules such as concentrators, routers, and multiplexers, which are extensively applied in networks.
MMPP model is a short-range dependent model, has been proven that it can emulate the second-order self-similar traffic and gives fine queueing-estimation in single server queueing system. Analysis of multiple server systems with MMPP input traffic (pseudo self-similar traffic) provides hardware designer a right performance statistics. We hope that the analysis is helpful for the people in their further research.
Subjects
多重伺服器
自我相似性訊務
馬可夫模組化卜瓦松模型
multiple server queues
MMPP
self-similar traffic
Type
thesis
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