Queues with Dropping Functions and General Arrival Processes
نویسندگان
چکیده
منابع مشابه
Markovian Queues with Correlated Arrival Processes
In an attempt to examine the effect of dependencies in the arrival process on the steady state queue length process in single server queueing models with exponential service time distribution, four different models for the arrival process, each with marginally distributed exponential interarrivals to the queueing system, are considered. Two of these models are based upon the upper and lower bou...
متن کاملStationary Birth-and-Death Processes Fit to Queues with Periodic Arrival Rate Functions
To better understand how to interpret birth-and-death (BD) processes fit to service system data, we investigate the consequences of fitting a BD process to a multi-server queue with a periodic time-varying arrival rate function. We study how this fitted BD process is related to the original queue-length process. If a BD process is fit to a segment of the sample path of the queue-length process,...
متن کاملHeavy-traffic limits for queues with periodic arrival processes
We establish conventional heavy-traffic limits for the number of customers in a Gt/GI/s queue with a periodic arrival process. We assume that the arrival counting process can be represented as the composition of a cumulative stochastic process that satisfies an FCLT and a deterministic cumulative rate function that is the integral of a periodic function. We establish three different heavy-traff...
متن کاملAnalysis of AQM Queues with Queue Size Based Packet Dropping
Queueing systems in which an arriving job is blocked and lost with a probability that depends on the queue size are studied. The study is motivated by the popularity of Active Queue Management (AQM) algorithms proposed for packet queueing in Internet routers. AQM algorithms often exploit the idea of queue-size based packet dropping. The main results include analytical solutions for queue size d...
متن کاملMarkov Decision Processes with General Discount Functions
In Markov Decision Processes, the discount function determines how much the reward for each point in time adds to the value of the process, and thus deeply a ects the optimal policy. Two cases of discount functions are well known and analyzed. The rst is no discounting at all, which correspond to the totaland average-reward criteria. The second case is a constant discount rate, which leads to a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2016
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0150702