Sub-Sum Constraint Function을 이용한 동적 실시간 VBR 트래픽 특성화

Dynamic rt-VBR Traffic Characterization using Sub-Sum Constraint Function

This paper studies a real-time VBR traffic characterization. There are two big approaches to determine traffic. One is a statistic approach and the other is a deterministic approach. This paper proposes a new constraint function, what we called “Sub-Sum Constraint Function”(SSCF). This function is mainly based on a deterministic approach and uses a statistic approach. It predicts and calculates the next rate with a present information about the stream. SSCF captures the intuitive bounded by a rate lower than its peak rate and closer to its long-term average rate. This model makes a order of the constraint function much less than any other works (O(n)). It can also be mapped on a token bucket algorithm which consists of r (token rate) and b (token depth). We use a concept, EB(effective bandwidth) for a utility of our function and comparing with other techniques such as CBR, average VBR. We simulated 21 multimedia sources for verifying the utility of our function.