7,310 research outputs found
Analysis and Decentralised Optimal Flow Control of Heterogeneous Computer Communication Network Models
General closed queueing networks are used to model the local flow
control in multiclass computer communication networks with single and
multiple transmission links. The problem of analysing multiclass
general closed queueing network models with single server and
multiserver is presented followed by the problem of decentralised
optimal local flow control of multiclass general computer
communication networks with single and multiple transmission links.
The generalised exponential (GE) distributional model with known first
two moments has been used to represent general interarrival and
transmission time distributions as various users have various traffic
characteristics.
A new method of general model reduction using the Norton' s
theorem for general queueing networks in conjunction with the
universal maximum entropy algorithm is proposed for the analysis of large general closed queueing networks. This extension to Norton's
theorem has an advantage over the direct application of the universal
maximum entropy approach whereby the study of a subset of queueing
centres of interest can be done without repeatedly solving the entire
network.
The principle of maximum entropy is used to derive new
approximate solutions for the joint queue length distributions of
multiclass general queueing network models with single server and
multiserver and favourable comparisons with other methods are made.
The decentralised optimal local flow control of the multiclass
computer communication networks with single and multiple transmission
links is shown to be a state dependent window type mechanism that has
been traditionally used in practice
Large closed queueing networks in semi-Markov environment and its application
The paper studies closed queueing networks containing a server station and
client stations. The server station is an infinite server queueing system,
and client stations are single-server queueing systems with autonomous service,
i.e. every client station serves customers (units) only at random instants
generated by a strictly stationary and ergodic sequence of random variables.
The total number of units in the network is . The expected times between
departures in client stations are . After a service completion
in the server station, a unit is transmitted to the th client station with
probability , and being processed in the th client
station, the unit returns to the server station. The network is assumed to be
in a semi-Markov environment. A semi-Markov environment is defined by a finite
or countable infinite Markov chain and by sequences of independent and
identically distributed random variables. Then the routing probabilities
and transmission rates (which are expressed via
parameters of the network) depend on a Markov state of the environment. The
paper studies the queue-length processes in client stations of this network and
is aimed to the analysis of performance measures associated with this network.
The questions risen in this paper have immediate relation to quality control of
complex telecommunication networks, and the obtained results are expected to
lead to the solutions to many practical problems of this area of research.Comment: 35 pages, 1 figure, 12pt, accepted: Acta Appl. Mat
Closed queueing networks under congestion: non-bottleneck independence and bottleneck convergence
We analyze the behavior of closed product-form queueing networks when the
number of customers grows to infinity and remains proportionate on each route
(or class). First, we focus on the stationary behavior and prove the conjecture
that the stationary distribution at non-bottleneck queues converges weakly to
the stationary distribution of an ergodic, open product-form queueing network.
This open network is obtained by replacing bottleneck queues with per-route
Poissonian sources whose rates are determined by the solution of a strictly
concave optimization problem. Then, we focus on the transient behavior of the
network and use fluid limits to prove that the amount of fluid, or customers,
on each route eventually concentrates on the bottleneck queues only, and that
the long-term proportions of fluid in each route and in each queue solve the
dual of the concave optimization problem that determines the throughputs of the
previous open network.Comment: 22 page
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application
Loss systems in a random environment
We consider a single server system with infinite waiting room in a random
environment. The service system and the environment interact in both
directions. Whenever the environment enters a prespecified subset of its state
space the service process is completely blocked: Service is interrupted and
newly arriving customers are lost. We prove an if-and-only-if-condition for a
product form steady state distribution of the joint queueing-environment
process. A consequence is a strong insensitivity property for such systems.
We discuss several applications, e.g. from inventory theory and reliability
theory, and show that our result extends and generalizes several theorems found
in the literature, e.g. of queueing-inventory processes.
We investigate further classical loss systems, where due to finite waiting
room loss of customers occurs. In connection with loss of customers due to
blocking by the environment and service interruptions new phenomena arise.
We further investigate the embedded Markov chains at departure epochs and
show that the behaviour of the embedded Markov chain is often considerably
different from that of the continuous time Markov process. This is different
from the behaviour of the standard M/G/1, where the steady state of the
embedded Markov chain and the continuous time process coincide.
For exponential queueing systems we show that there is a product form
equilibrium of the embedded Markov chain under rather general conditions. For
systems with non-exponential service times more restrictive constraints are
needed, which we prove by a counter example where the environment represents an
inventory attached to an M/D/1 queue. Such integrated queueing-inventory
systems are dealt with in the literature previously, and are revisited here in
detail
The MVA Priority Approximation
A Mean Value Analysis (MVA) approximation is presented for computing the average performance measures of closed-, open-, and mixed-type multiclass queuing networks containing Preemptive Resume (PR) and nonpreemptive Head-Of-Line (HOL) priority service centers. The approximation has essentially the same storage and computational requirements as MVA, thus allowing computationally efficient solutions of large priority queuing networks. The accuracy of the MVA approximation is systematically investigated and presented. It is shown that the approximation can compute the average performance measures of priority networks to within an accuracy of 5 percent for a large range of network parameter values. Accuracy of the method is shown to be superior to that of Sevcik's shadow approximation
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