Provisioning network resource to meet the quality of Service (QoS) demand is a key issue for future network services. Such functions may be realized by an admission control algorithm, which determines whether or not a new traffic flow can be admitted into the network. It is widely accepted that many traffic flows have self-similar character that has detrimental influence on network performance. This characteristic has made old mathematical models invalid, and a new model must work with self-similar fractal instead. This paper applies Fractional Brownian Motion(FBM) model and integrates it into the comprehensive admission control scheme, which takes account of aggregated traffic behavior to get the statistical multiplexing performance gain. Experiment verifies that FBM model can be used to realistically describe packet traffic in modern packet networks and accurately predict their performance. Provisioning network resource to meet the quality of Service (QoS) demand is a key issue for future network services. Which determines whether or not a new traffic flow can be admitted into the network. It is widely accepted that many traffic flows have self-similar character that has detrimental influence on network performance. This characteristic has made old mathematical models invalid, and a new model must work with self-similar fractal instead. This paper applies Fractional Brownian Motion (FBM ) model and integrates it into the comprehensive admission control scheme, which takes account of aggregated traffic behavior to get the statistical multiplexing performance gain. Experiment verifies that FBM model can be used to realistically describe packet traffic in modern packet networks and precisely predict their performance.