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Probabilistic Inference in Queueing Networks
2008
USENIX Symposium on Operating Systems Design and Implementation
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In this paper, we analyze queueing networks from the probabilistic modeling perspective, applying inference methods from graphical models that afford significantly more modeling flexibility. ...
In particular, we present a Gibbs sampler and stochastic EM algorithm for networks of M/M/1 FIFO queues. ...
Acknowledgments We thank Peter Bodik for providing the data used in Section 5. ...
dblp:conf/osdi/SuttonJ08
fatcat:lwkyfq7apvbhbeczzg72ausrke
Page 4627 of Mathematical Reviews Vol. , Issue 93h
[page]
1993
Mathematical Reviews
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Summary: “We analyze the probabilistic variation of the multicom- modity discrete network design problem named the probabilistic network design problem in which the commodities are generated probabilistically ...
methods for constructing the a priori network both in the worst case model and in the probabilistic model. ...
Page 1410 of Automation and Remote Control Vol. 54, Issue 9
[page]
1993
Automation and Remote Control
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On the whole, the statements in this section permit us to inference that approximation 4 also best suits networks obeying nonexponential laws of distribution. ...
queue length at some nodes of the network. ...
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
[article]
2012
arXiv
pre-print
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We illustrate this algorithm on examples used in teaching probabilistic models, computational cognitive science research, and game theory. ...
We solve these equations by fixed-point iteration in topological order. ...
While the queue is not empty, the algorithm takes the first task in the queue and evaluates the function call f (). ...
arXiv:1206.3555v2
fatcat:vxso33pvgngw3gpbr6gvi73uae
Probabilistic Neural Programs
[article]
2016
arXiv
pre-print
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We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use ...
Probabilistic neural programs combine a computation graph for specifying a neural network with an operator for weighted nondeterministic choice. ...
As with probabilistic programs, various inference algorithms can be applied to a sketch. ...
arXiv:1612.00712v1
fatcat:mffogftuwzg47mqo3imqvdnnrm
Cognitive Congestion Control for Data Portals with Variable Link Capacity
2012
International Journal of Communications, Network and System Sciences
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Based on simulation results, the proposed method is capable of adjusting the available bandwidth by tuning the queue length, and provides a stable queue in the network. ...
In this study, a data portal is considered as an application-based network, and a cognitive method is proposed to deal with congestion in this kind of network. ...
During this step, the collected information in the observation phase is used by the Bayesian network model to build a probabilistic structure to predict variations of queue length. ...
doi:10.4236/ijcns.2012.58058
fatcat:jkw6s7plozc6lpx5wllh6e67uq
Revisiting TCP Congestion Control Using Delay Gradients
[chapter]
2011
Lecture Notes in Computer Science
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Delay-based TCP CC algorithms infer congestion from delay measurements and tend to keep queue lengths low. ...
improved tolerance of non-congestion related losses (86 % better goodput than NewReno in the presence of 1 % packet losses). ...
Acknowledgments This work was made possible in part by a grant from the Cisco University Research Program Fund at Community Foundation Silicon Valley. ...
doi:10.1007/978-3-642-20798-3_25
fatcat:cvkbhvbuhne6vfv5repqio5sfi
Parameter and State Estimation in Queues and Related Stochastic Models: A Bibliography
[article]
2024
arXiv
pre-print
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This is an annotated bibliography on estimation and inference results for queues and related stochastic models. ...
Note that this bibliography is also a companion to our survey of parameter and state estimation in queues [20]. ...
Bayesian estimators of the traffic intensity in an M/M/1 queue are derived under a precautionary loss function. Whitt [331] : Fitting birth-and-death queueing model to data. ...
arXiv:1701.08338v3
fatcat:adyyvj5yv5g2bmaxm6cv7p7qe4
Abductive inference in Bayesian belief networks using swarm intelligence
2012
The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems
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Abductive inference in Bayesian belief networks, also known as most probable explanation (MPE) or finding the maximum a posterior instantiation (MAP), is the task of finding the most likely joint assignment ...
to all of the (nonevidence) variables in the network. ...
In [14] , it has been shown that abductive inference in Bayesian belief networks is NP-hard. ...
doi:10.1109/scis-isis.2012.6505074
dblp:conf/scisisis/PillaiS12
fatcat:uqkftqygwrb6xjoppwuqh6vgly
GraphBPT: An Efficient Hierarchical Data Structure for Image Representation and Probabilistic Inference
[chapter]
2015
Lecture Notes in Computer Science
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Finally, we illustrate how the proposed tool takes benefit from probabilistic inference techniques by empowering the BPT with its equivalent factor graph. ...
The relevance of GraphBPT is illustrated in the context of image segmentation. ...
A demonstration of empowering BPTs with probabilistic inference. ...
doi:10.1007/978-3-319-18720-4_26
fatcat:ei4b5tm22bdmvnsvadxjorngpi
Towards The Automated Inference Of Queueing Network Models From High-Precision Location Tracking Data
2009
ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera
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This paper proposes an automated four-stage data processing pipeline which takes as input raw high-precision location tracking data and which outputs a queueing network model of customer flow. ...
We evaluate our method's effectiveness and accuracy in four experimental case studies. ...
Together, these yield a parameterised queueing network model of the real-life system. ...
doi:10.7148/2009-0664-0672
dblp:conf/ecms/HorngDJK09
fatcat:sbzgrke7izbtdntuu5367xpb74
Probabilistic Resource-Aware Session Types (Artifact)
[article]
2022
Zenodo
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Type inference relies on linear constraint solving to automatically derive symbolic bounds for various cost metrics. ...
Experiments demonstrate that PRast is applicable in different domains such as cost analysis of randomized distributed algorithms, analysis of Markov chains, probabilistic analysis of amortized data structures ...
Recent work on analyzing probabilistic networks [Foster et al. 2016; Gehr et al. 2018; Smolka et al. 2019 ] can be viewed as reasoning systems for probabilistic message passing systems. ...
doi:10.5281/zenodo.7147007
fatcat:xxa52kf5djcubpu7uhsdz5hquq
Book announcements
1993
Discrete Applied Mathematics
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Queues with combined arrivals and departures. Specialized Poisson queues. Non-Possion queues. Queues with priorities for service. Tandem or series queues. Chapter 16: Queueing Theory in Practice. ...
PART II: PROBABILISTIC MODELS. Chapter 11: Data Representation in Operations Research. Nature of data in OR. Forecasting techniques. Chapter 12: Decision Theory and Games. Decisions under risk. ...
Chapter 7: Order Statistics in Statistical Inference. Introduction. Types of order statistics data. Order statistics and sufficiency. Maximum-likelihood estimation. ...
doi:10.1016/0166-218x(93)90030-r
fatcat:ss6bl44ckvh63gsk22rycr44ja
Inference and Learning in Networks of Queues
2010
Journal of machine learning research
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The most popular performance models are networks of queues. However, no current methods exist for parameter estimation or inference in networks of queues with missing data. ...
In this paper, we present a novel viewpoint that combines queueing networks and graphical models, allowing Markov chain Monte Carlo to be applied. ...
, Intel, Network Appliance, SAP, VMWare and Yahoo! ...
dblp:journals/jmlr/SuttonJ10
fatcat:rkb72x26anhjnofujqloe4cqsa
Higway Criuse Control System for Vehicles using Low Power RF and Can Protocol
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Routers are responsible for performing the direction of network traffic over the internet Congestion control mechanisms provide a better way of handling network congestion. ...
The rapid growth and increased communications over internet has also increased the demand for an effective and efficient communication over the network. ...
Unlike traditional queue management algorithms that drops packets when the buffer is insufficient to handle the arriving packets RED drops packets probabilistically. ...
doi:10.35940/ijitee.f4054.049620
fatcat:kulg47m7jjbhjinwyx3wew2rha
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