566912
Diffusion approximations for Kumar-Seidman network under a priority service discipline
Unlike for a single-class queueing network, diffusion approximations for a multiclass queueing network may not exist under the heavy traffic condition. In this paper, we derive a necessary and sufficient condition for the existence of the diffusion approximation for a four-class two-station multiclass queueing network (known as Kumar-Seidman network) under a priority service discipline.
7847310
The Finite Element Method for Computing the Stationary Distribution of an SRBM in a Hypercube with Applications to Finite Buffer Queueing Networks
This paper proposes an algorithm, referred to as BNAfm (Brownian network analyzer with finite element method), for computing the stationary distribution of a semimartingale reflecting Brownian motion (SRBM) in a hypercube. The SRBM serves as an approximate model of queueing networks with finite buffers. Our BNAfm algorithm is based on the finite element method and an extension of a generic algorithm developed by Dai and Harrison [14]. It uses piecewise polynomials to form an approximate subspace of an infinite-dimensional functional space. The BNAfm algorithm is shown to produce good estimates for stationary probabilities, in addition to stationary moments. This is in contrast to the BNAsm algorithm (Brownian network analyzer with spectral method) of Dai and Harrison [14], which uses global polynomials to form the approximate subspace and which sometimes fails to produce meaningful estimates of these stationary probabilities. Extensive computational experiences from our implementation are reported, which may be useful for future numerical research on SRBMs. A three-station tandem network with finite buffers is presented to illustrate the effectiveness of the Brownian approximation model and our BNAfm algorithm.
34160766
Capacity competition of make-to-order firms
Two make-to-order firms, each modelled as a single-server queue, compete for a common stream of (potential) customers by setting their service capacities (rates) and service prices. Each customer maximizes her expected return by getting service from a firm or by balking. We completely characterize the Nash equilibrium of the competition.
5269633
Existence Condition for the Diffusion Approximations of Multiclass Priority Queueing Networks
In this paper, we extend the work of Chen and Zhang [12] and establish a new sufficient condition for the existence of the (conventional) diffusion approximation for multiclass queueing networks under priority service disciplines. This sufficient condition relates to the weak stability of the fluid networks and the stability of the high priority classes of the fluid networks that correspond to the queueing networks under consideration. Using this sufficient condition, we prove the existence of the diffusion approximation for the last-buffer-first-served reentrant lines. We also study a three-station network example, and observe that the diffusion approximation may not exist, even if the “proposed” limiting semimartingale reflected Brownian motion (SRBM) exists.
26582047
Optimal intensity control of a multi-class queue
We study a mixed problem of optimal scheduling and input and output control of a single server queue with multi-classes of customers. The model extends the classical optimal scheduling problem by allowing the general point processes as the arrival and departure processes and the control of the arrival and departure intensities. The objective of our scheduling and control problem is to minimize the expected discounted inventory cost over an infinite horizon, and the problem is formulated as an intensity control. We find the well-knowncμ is the optimal solution to our problem.
3121075
Parameterized Compilability
Compilability is a measure of how effectively compilation (or preprocessing) can be applied to knowledge bases specified in a particular knowledge representation formalism; the aim of compilation is to allow for efficient, on-line query processing. A theory of compilability has been established for organizing knowledge representation formalisms according to a scheme of "compilability classes", and bears strong analogies to the classical theory of complexity, which permits the organization of computational problems according to complexity classes. We develop a novel theory of compilability, called parameterized compilability, which incorporates the notion of parameterization as used in parameterized complexity and permits for refined analysis of compilability.
30863517
Optimization, Games, and Quantified Constraint Satisfaction
Optimization problems considered in the literature generally assume a passive environment that does not react to the actions of an agent. In this paper, we introduce and study a class of optimization problems in which the environment plays an active, adversarial role and responds dynamically to the actions of an agent; this class of problems is based on the framework of quantified constraint satisfaction. We formalize a new notion of approximation algorithm for these optimization problems, and consider certain restricted versions of the general problem obtained by restricting the types of constraints that may appear. Our main result is a dichotomy theorem classifying exactly those restricted versions having a constant factor approximation algorithm.
15840540
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
Presented is a model of an integrate-and-fire neuron with active dendrites and a spike-timing dependent Hebbian learning rule. The learning algorithm effectively trains the neuron when responding to several types of temporal encoding schemes: temporal code with single spikes, spike bursts and phase coding. The neuron model and learning algorithm are tested on a neural network with a self-organizing map of competitive neurons. The goal of the presented work is to develop computationally efficient models rather than approximating the real neurons. The approach described in this paper demonstrates the potential advantages of using the processing functionalities of active dendrites as a novel paradigm of computing with networks of artificial spiking neurons.
641995
An Admission Control Scheme for Predictable Server Response Time for Web Accesses
The diversity in web object types and their resource requirements contributes to the unpredictability of web service provisioning. In this paper, an eÆcient admission control algorithm, PACERS, is proposed to provide di erent levels of services based on the server workload characteristics. Service quality is ensured by periodical allocation of system resources based on the estimation of request rate and service requirements of prioritized tasks. Admission of lower priority tasks is restricted during high load periods to prevent denial-of-services to high priority tasks. A doublequeue structure is implemented to reduce the e ects of estimation inaccuracy and to utilize the spare capacity of the server, thus increasing the system throughput. Response delays of the high priority tasks are bounded by the length of the prediction period. Theoretical analysis and experimental study show that the PACERS algorithm provides desirable throughput and bounded response delay to the prioritized tasks, without any signi cant impact on the aggregate throughput of the system under various workload.
17605322
Motion Activity Based Semantic Video Similarity Retrieval
Semantic feature extraction of video shots and fast video sequence matching are important and required for efficient retrieval in a large video database. In this paper, a novel mechanism of similarity retrieval is proposed. Similarity measure between video sequences considering the spatio-temporal variation through consecutive frames is presented. For bridging the semantic gap between low-level features and the rich meaning that users desire to capture, video shots are analyzed and characterized by the high-level feature of motion activity in compressed domain. The extracted features of motion activity are further described by the 2D-histogram that is sensitive to the spatio-temporal variation of moving objects. In order to reduce the dimensions of feature vector space in sequence matching, Discrete Cosine Transform (DCT) is exploited to map semantic features of consecutive frames to the frequency domain while retains the discriminatory information and preserves the Euclidean distance between feature vectors. Experiments are performed on MPEG-7 testing videos, and the results of sequence matching show that a few DCT transformed coefficients are adequate and thus reveal the effectiveness of the proposed mechanism of video retrieval.