Fundamentals of Queueing Theory

By R. Syski. 1999

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Citations in the corpus

GenDICE: Generalized Offline Estimation of Stationary Values (2020, background): “An example from engineering is queueing theory, where the queue lengths and waiting time under the limiting distribution have been extensively studied (Gross et al., 2018).”
Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization (2001, background): “networks provided students have already studied fundamental queueing systems at the level of [3] or [4].”
QoS performance of optical burst switching in IP-over-WDM networks (2000, result): “Since the number of virtual FDLs is , the maximum number of bursts in the switch (including those being transmitted and those being delayed) is . According to the model [ 19 ], the lower bound on the loss probability is”
A Software Defined Networking architecture for the Internet-of-Things (2014, methodology): “Generally, there are two methodologies in analysing QoS in communication networks, one is Queueing Theory [22] and the other is Network Calculus [23].”
Smart Grid — The New and Improved Power Grid: A Survey (2012, unknown): “From a utility’s perspective, they can use smart meters to realize real-time pricing, which tries to encourage users to reduce their demands in peak load periods, or to optimize power flows according to the information sent from demand sides.”
Performance analysis of IEEE 802.11 MAC protocols in wireless LANs (2004, methodology): “If we can approximate the distribution of MAC service time by an exponential distribution, the steadystate probability for the M/M/1/K model [15] is given by:”
Mobile Data Offloading: How Much Can WiFi Deliver? (2013, result): “Note that the stationary distribution of a non-Markovian system is equivalent to that of the Markovian system in Fig.”
Estimation of Traffic Intensity Based on Queue Length in a Single M/M/1 Queue (2013, background): “The steady state distribution of initial queue length n0 (see Gross and Harris, 1998) in an M/M/1 queueing system is geometric and is given as pn0 = 1− n0 (2.6) Hence, for the random variables N0 N1 Nn , the likelihood function can be expressed in terms of the transition probabilities given by L =…”
Estimating functions in indirect inference (2004, methodology): “The joint density c.y/ of a single interdeparture time from a steady state M/G/1 queue with independent exponential interarrivals can be computed; see Gross and Harris (1998), page 234.”
On selfish routing in Internet-like environments (2003, methodology): “In our evaluations, we use .ve representative latency functions: M/M/1, M/D/1 [18], P/M/1, P/D/1 [19], and BPR [9].”
pFabric: minimal near-optimal datacenter transport (2013, methodology): “The derivation is based on using the well-known Pollaczek-Khintchine formula [13] to compute the average waiting time for a flow in each priority queue (assuming M/G/1 queues) and can easily be generalized for any number of priority queues (see the longer version of this paper [6] for details).”
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning (2017, methodology): “Please note that according to the Little’s Theorem [28], the average number of VMs pending in the system is proportional to the average VM (job) latency.”