| 摘要 |
Data communication in network traffic is modeled in real time and is analyzed using a 2-state Markov modified Poissen process (MMPP). The traffic inter-arrival times for bursty and idle states define a transition window [lambda<SUP>1</SUP><SUB>max</SUB>, lambda<SUP>2</SUP><SUB>min</SUB>] represented by the boundary values lambda<SUP>1</SUP><SUB>max </SUB>for the inter-arrival time for bursty traffic, and lambda<SUP>2</SUP><SUB>min </SUB>for the inter-arrival time for idle traffic. Changes in the values of lambda<SUP>1</SUP><SUB>max </SUB>and lambda<SUP>2</SUP><SUB>min </SUB>are tracked over time, and the size of the transition window is enlarged or decreased based upon relative changes in these values. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model. The modeling is applicable to the synchronization of polling and blocking in a low-latency network system. This permits the adoptive selection of poll or block to maximize CPU utilization and interrupt latency.
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