CAJ | 학술논문

与传统的接纳控制算法相比，基于测量的接纳控制有诸多优点，首先它无需知道应用的流量模型，其次它能动态适应网络的负载变化，提高网络资源的利用率.文中分析了基于测量的接纳控制的基本思想，并在此基础上提出和实现了一种自适应的接纳控制算法（AdaptiveMeasurement-BasedAdmissionControl,AMBAC）.作者通过实验对该算法进行了验证，发现在系统资源利用率(或接纳能力)接近的情况下，与传统的(固定时间窗口的)MBAC相比，AMBAC能达到更低的平均分组丢失率.
여전통적접납공제산법상비，기우측량적접납공제유제다우점，수선타무수지도응용적류량모형，기차타능동태괄응망락적부재변화，제고망락자원적이용솔.문중분석료기우측량적접납공제적기본사상，병재차기출상제출화실현료일충자괄응적접납공제산법（AdaptiveMeasurement-BasedAdmissionControl,AMBAC）.작자통과실험대해산법진행료험증，발현재계통자원이용솔(혹접납능력)접근적정황하，여전통적(고정시간창구적)MBAC상비，AMBAC능체도경저적평균분조주실솔.
In contrast to traditional admission control mechanisms, the mostattractive feature of Measurement-Based Admission Control (MBAC) is that it does not require an apriori traffic model, because it is very difficult or even impossible for the user or application to come up with a tight traffic model before establishing a flow. Other advantages of MBAC include that an overly conservative specification does not result in an over-allocation of resources for the entire duration of the session, and it can adapt to the changing traffic load dynamically, so it improves the network utilization while offering quality of service to users.This paper first studies how MBAC works. Existing MBACs adopt the fixed-length Time-window Measurement Mechanism to estimate the network traffic load LCL. There are two important parameters (measurement window T and sampling window S) that impact the estimation of LCL. Because S has smaller impact than T, in this paper, we only consider T. In MBAC, small T means more adaptability and higher resource utilization, but larger T results in greater stability and lower resource utilization. Hence, to select an appropriate T is very important for MBAC. To solve this problem, we propose an Adaptive Measurement-Based Admission Control (AMBAC) algorithm. In AMBAC, we set two thresholds: LTmax and LTmin. When the measured traffic load LCL is larger than LTmax, our algorithm enlarges T automatically, which makes AMBAC more conservative and hence decreases the network's admission ability. When LCL is smaller than LTmin, our algorithm shrinks T, which improves the network's admission ability. When LCL is between LTmax and LTmin, our algorithm does not alter T. By altering T, AMBAC makes the network adapt to the changing traffic load dynamically, so the network utilization is improved. To evaluate AMBAC we implemented our algorithm on FreeBSD. We test it under different traffic scenarios and compare it with the traditional MBAC. Our simulation results show AMBAC can get lower packet-loss while achieving a high level of utilization.