341 research outputs found
Two-dimensional fluid queues with temporary assistance
We consider a two-dimensional stochastic fluid model with ON-OFF inputs
and temporary assistance, which is an extension of the same model with
in Mahabhashyam et al. (2008). The rates of change of both buffers are
piecewise constant and dependent on the underlying Markovian phase of the
model, and the rates of change for Buffer 2 are also dependent on the specific
level of Buffer 1. This is because both buffers share a fixed output capacity,
the precise proportion of which depends on Buffer 1. The generalization of the
number of ON-OFF inputs necessitates modifications in the original rules of
output-capacity sharing from Mahabhashyam et al. (2008) and considerably
complicates both the theoretical analysis and the numerical computation of
various performance measures
Rigorous Bounds for Loss Probabilities in Multiplexers of Discrete Heterogenous Markovian Sources
Exponential upper bounds of the form P[queue ≥ b] ≤ φy^(-b) are obtained for the distribution of the queue length in a model of a multiplexer in which the input is a heterogeneous superposition of discrete Markovian on-off sources. These bounds are valid at all queue lengths, rather than just asymptotic in the limit b→∞. The decay constant y is found by numerical solution of a single transcendental equation which determines the effective bandwidths of the sources in the limit b→∞. The prefactor φ is given explicitly in terms of y. The bound provides a means to determine rigorous corrections to effective bandwidths for multiplexers with finite buffers
Exponential Bounds for Queues with Markovian Arrivals
Exponential bounds P[queue ≥ b] ≤ φe^(-γb) are found for queues whose increments are described by Markovian Additive Processes. This is done application of maximal inequalities to exponential martingales for such processes. Through a thermodynamic approach the constant γ is shown to be the decay rate for an asymptotic lower bound for the queue length distribution. The class of arrival processes considered includes a wide variety of Markovian multiplexer models, and a general treatment of these is given, along with that of Markov modulated arrivals. Particular attention is paid to the calculation of the prefactor φ
Some aspects of traffic control and performance evaluation of ATM networks
The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation
Fast simulation of packet loss rates in a shared buffer communications switch
This paper describes an efficient technique for estimating, via simulation, the probability of buffer overflows in a queueing model that arises in the analysis of ATM (Asynchronous Transfer Mode) communication switches. There are multiple streams of (autocorrelated) traffic feeding the switch that has a buffer of finite capacity. Each stream is designated as either being of high or low priority. When the queue length reaches a certain threshold, only high priority packets are admitted to the switch's buffer. The problem is to estimate the loss rate of high priority packets. An asymptotically optimal importance sampling approach is developed for this rare event simulation problem. In this approach, the importance sampling is done in two distinct phases. In the first phase, an importance sampling change of measure is used to bring the queue length up to the threshold at which low priority packets get rejected. In the second phase, a different importance sampling change of measure is used to move the queue length from the threshold to the buffer capacity
Effective bandwidth of non-Markovian packet traffic
We demonstrate the application of recent advances in statistical mechanics to
a problem in telecommunication engineering: the assessment of the quality of a
communication channel in terms of rare and extreme events. In particular, we
discuss non-Markovian models for telecommunication traffic in continuous time
and deploy the "cloning" procedure of non-equilibrium statistical mechanics to
efficiently compute their effective bandwidths. The cloning method allows us to
evaluate the performance of a traffic protocol even in the absence of
analytical results, which are often hard to obtain when the dynamics are
non-Markovian
Large Deviations and the Generalized Processor Sharing Scheduling: Upper and Lower Bounds Part I: Two-Queue Systems
We prove asymptotic upper and lower bounds on the asymptotic decay rate of per-session queue length tail distributions for a single constant service rate server queue shared by multiple sessions with the generalized processor sharing (GPS) scheduling discipline. The simpler case of a GPS system with only two queues needs special attention, as under this case, it is shown that the upper bounds and lower boundsmatch, thus yielding exact bounds. This result is established in this part (Part I) of the paper. The general case is much more complicated, and is treated separately in Part II of the paper [42], where tight upper and lower bound results are proved by examining the dynamics of bandwidth sharing nature of GPS scheduling. The proofs use sample-path large deviation principle and are based on some recent large deviation results for a single queue with a constant service rate server. These results have implications in call admission control for high-speed communication networks
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