ON THE TRANSITION FROM HEAVY TRAFFIC TO HEAVY TAILS FOR THE M/G/1 QUEUE: THE REGULARLY VARYING CASE

被引:14
作者
Olvera-Cravioto, Mariana [1 ]
Blanchet, Jose [1 ]
Glynn, Peter [2 ]
机构
[1] Columbia Univ, Dept Ind Engn & Operat Res, New York, NY 10027 USA
[2] Stanford Univ, Dept Management Sci & Engn, Stanford, CA 94305 USA
关键词
M/G/1; queue; heavy traffic; heavy tails; uniform approximations; large deviations; LARGE DEVIATIONS; WAITING TIME; APPROXIMATIONS; PROBABILITIES; NETWORKS;
D O I
10.1214/10-AAP707
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Two of the most popular approximations for the distribution of the steady-state waiting time, W-infinity, of the M/G/1 queue are the so-called heavy-traffic approximation and heavy-tailed asymptotic, respectively. If the traffic intensity, rho, is close to 1 and the processing times have finite variance, the heavy-traffic approximation states that the distribution of W-infinity is roughly exponential at scale O((1 - rho)(-1)), while the heavy tailed asymptotic describes power law decay in the tail of the distribution of W-infinity for a fixed traffic intensity. In this paper, we assume a regularly varying processing time distribution and obtain a sharp threshold in terms of the tail value, or equivalently in terms of (1- rho), that describes the point at which the tail behavior transitions from the heavy-traffic regime to the heavy-tailed asymptotic. We also provide new approximations that are either uniform in the traffic intensity, or uniform on the positive axis, that avoid the need to use different expressions on the two regions defined by the threshold.
引用
收藏
页码:645 / 668
页数:24
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