Quasi-stationary analysis for queues with temporary overload

Motivated by the high variation in transmission rates for document transfer in the Internet and file down loads from web servers, we study the buffer content in a queue with a fluctuating service rate. The fluctuations are assumed to be driven by an independent stochastic process. We allow the queue to be overloaded in some of the server states. In all but a few special cases, either exact analysis is not tractable, or the dependence of system performance in terms of input parameters (such as the traffic load) is hidden in complex or implicit characterizations. Various asymptotic regimes have been considered to develop insightful approximations. In particular, the so-called quasi-stationary approximation has proven extremely useful under the assumption of uniform stability. We refine the quasi-stationary analysis to allow for temporary instability, by studying the “effective system load” which captures the effect of accumulated work during periods in which the queue is unstable.

[1]  Alan Scheller-Wolf,et al.  Fundamental characteristics of queues with fluctuating load , 2006, SIGMETRICS '06/Performance '06.

[2]  J. Michael Harrison,et al.  Pointwise Stationary Fluid Models for Stochastic Processing Networks , 2009, Manuf. Serv. Oper. Manag..

[3]  Ward Whitt,et al.  Control and recovery from rare congestion events in a large multi-server system , 1997, Queueing Syst. Theory Appl..

[4]  W. A. Massey,et al.  Uniform acceleration expansions for Markov chains with time-varying rates , 1998 .

[5]  Vaidyanathan Ramaswami,et al.  Introduction to Matrix Analytic Methods in Stochastic Modeling , 1999, ASA-SIAM Series on Statistics and Applied Mathematics.

[6]  Tom Burr,et al.  Introduction to Matrix Analytic Methods in Stochastic Modeling , 2001, Technometrics.

[7]  W. R. Scheinhardt,et al.  Markov-modulated and feedback fluid queues , 1998 .

[8]  Alexandre Proutière,et al.  Modeling integration of streaming and data traffic , 2004, Perform. Evaluation.

[9]  Ward Whitt,et al.  Responding to Unexpected Overloads in Large-Scale Service Systems , 2009, Manag. Sci..

[10]  Awi Federgruen,et al.  Queueing Systems with Service Interruptions , 1986, Oper. Res..

[11]  Sing Kwong Cheung,et al.  Processor-sharing queues and resource sharing in wireless LANs , 2007 .

[12]  Frank E. Grubbs,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[13]  R. Núñez Queija,et al.  Performance analysis of traffic surges in multi-class communication networks , 2010, 2010 22nd International Teletraffic Congress (lTC 22).

[14]  R. Núñez Queija,et al.  Centrum Voor Wiskunde En Informatica Reportrapport Sojourn times in a Processor Sharing Queue with Service Interruptions Sojourn times in a Processor Sharing Queue with Service Interruptions , 2022 .

[15]  D. Mitra,et al.  Stochastic theory of a data-handling system with multiple sources , 1982, The Bell System Technical Journal.

[16]  B. Harshbarger An Introduction to Probability Theory and its Applications, Volume I , 1958 .

[17]  S. Ross Average delay in queues with non-stationary Poisson arrivals , 1978, Journal of Applied Probability.

[18]  Robert B. Cooper,et al.  Stochastic Decompositions in the M/G/1 Queue with Generalized Vacations , 1985, Oper. Res..

[19]  Mor Harchol-Balter,et al.  Fluid and diffusion limits for transient sojourn times of processor sharing queues with time varying rates , 2006, Queueing Syst. Theory Appl..