Pricing and distributed QoS control for elastic network traffic

We study a processor-sharing model in which users choose between a high- and a low-priority service, based on their utility functions and prices charged by the service provider. The latter aims at revenue maximization. The model is motivated by file transmissions in data networks with distributed congestion control.     

Routing and wavelength assignment algorithms for multiclass WDM optical networks

In this paper dynamic routing and wavelength assignment strategies have been proposed for multiclass WDM optical networks. Multiclass optical networks provide multiple classes of services to the subscriber according to the requirement, which in turn increase operational profitability. Each class of service could be characterized by parameters like number of wavelengths, expected call holding time and average arrival rate of request. The proposed strategies have been analyzed and compared with existing strategies on the basis of blocking probabilities for multiclass traffic scenarios. Simulation results on different network topologies demonstrate that the performance of proposed strategies “Fixed shortest/alternate shortest path routing with wavelength reservation (FSASWR)” and “Fixed alternate shortest path routing with least priority wavelength assignment (FASPL)” are much better as compared to existing strategies. Proposed strategies minimize blocking probability of the multiclass network using limited number of wavelengths.     

Instability of LAS multiclass queueing networks

We provide an example of a strictly subcritical multiclass queueing network which is unstable under the least attained service (LAS) service protocol. It is a reentrant line with two servers and six customer classes. The customer interarrival times in our system are bounded below and have finite exponential moments, while the corresponding service times are deterministic. As a special case, we obtain a deterministic, strictly subcritical unstable LAS network.     

Kernel Logistic Regression and the Import Vector Machine

The support vector machine (SVM) is known for its good performance in two-class classification, but its extension to multiclass classification is still an ongoing research issue. In this article, we propose a new approach for classification, called the import vector machine (IVM), which is built on kernel logistic regression (KLR). We show that the IVM not only performs as well as the SVM in two-class classification, but also can naturally be generalized to the multiclass case. Furthermore, the IVM provides an estimate of the underlying probability. Similar to the support points of the SVM, the IVM model uses only a fraction of the training data to index kernel basis functions, typically a much smaller fraction than the SVM. This gives the IVM a potential computational advantage over the SVM.     

Existence and generic stability of solutions for multiclass multicriteria traffic equilibrium problems with capacity constraints of arcs

ABSTRACT

In this paper, we introduce the multiclass multicriteria traffic equilibrium problem with capacity constraints of arcs and its equilibrium principle. Using Fan–Browder's fixed points theorem and Fort's lemma to prove the existence and generic stability results of multiclass multicriteria traffic equilibrium flows with capacity constraints of arcs.     

Marginal productivity index policies for scheduling a multiclass delay-/loss-sensitive queue

This paper addresses the problem of scheduling a Markovian multiclass queue with a finite dedicated buffer for each class, where class-dependent linear holding and rejection cost rates model differing levels of tolerance to delay and loss. The goal is to design well-grounded and tractable scheduling policies that nearly minimize expected total discounted or long-run average cost. New dynamic index policies are introduced, awarding higher priority to classes with larger index values, where a class’ index measures the marginal productivity of work at its current state. The results are obtained by deploying the work-cost analysis approach to marginal productivity indices (MPIs) for restless bandits developed by the author, which is extended to the bias criterion. The MPI furnishes new insights: for a loss-sensitive class, it is a decreasing function of the number of empty buffer spaces, independent of the buffer size; for a delay-sensitive class, it is a decreasing function of the queue length. Such opposite orderings show that preventive work is more valuable than reactive work for the latter classes, whereas the opposite holds for the former. The results of a computational study on two-class instances are reported, shedding light on how the MPI policy’s relative performance varies with each parameter. Parameter ranges are thus identified where the MPI policy is near optimal, and substantially outperforms conventional benchmark policies. 2000 Mathematics Subject Classification 90B22 · 90B36 · 90B18 · 60K25 · 60K30 · 68M20     

A multiclass,multicriteria logit-based traffic equilibrium assignment model under ATIS

This paper presents a multiclass, multicriteria (cost versus time) logit-based traffic equilibrium assignment model in road networks served by advanced traveler information systems (ATIS). All users are differentiated by their own value of time (VOT) that follows some probability distribution. Users of each class, having their own VOT, are further divided into two groups, equipped and unequipped with ATIS respectively. The travel disutility received by each user is defined as a linear bi-criteria combination of travel time and monetary travel cost. It is assumed that all users make their route choices in a logit-based stochastic manner, but the equipped users have lower perception variation on the travel disutility than the unequipped due to the ATIS service. The model is formulated as a fixed-point problem and solved by the method of successive averages in conjunction with logit assignment. Numerical results show that the traditional single-class and/or single-criterion models may overestimate or underestimate the benefit from ATIS services.     

Brownian models of multiclass queueing networks: Current status and open problems

This paper is concerned with Brownian system models that arise as heavy traffic approximations for open queueing networks. The focus is on model formulation, and more specifically, on the formulation of Brownian models for networks with complex routing. We survey the current state of knowledge in this dynamic area of research, including important open problems. Brownian approximations culminate in estimates of complete distributions; we present numerical examples for which complete sojourn time distributions are estimated, and those estimates are compared against simulation.     

p-Nitro-N-propylaniline/silica: synthesis, characterization, and its application in matrix solid phase dispersion for multiresidue analysis of pesticides in carrots

A new material for matrix solid phase dispersion (MSPD) was synthesized -- p-nitro-N-propylaniline/silica (pNNPASi) by grafting reactions, characterized by elemental analysis and N(2)-adsorption-desorption isotherms, and tested for multiclass multiresidue analysis of pesticides in wet and freeze-dried carrots. Results obtained applying this new solid phase sorbent to MSPD extraction of ten pesticides (trichlorphon, trifluralin, dicloran, chlorothalonil, prometryn, linuron, captan, procymidone, prochloraz, and deltametrin) in wet carrots showed better results than the ones obtained for freeze-dried samples. Recoveries were in the range of 48-106% and precisions varied from 6 to 20% when wet samples were employed. Comparison between pNNPASi sorbent and C(18) showed better performance of pNNPASi for eight out of ten pesticides tested. The LOQs show that the developed method can be used to detect the pesticides investigated in carrots at concentrations below the maximum residue levels (MRL) established by EU, USEPA, and National Sanitary Surveillance Agency (ANVISA). Linuron, captan, prochloraz, and deltamethrin were found in at least one of the two commercial samples studied in concentrations above the LOQ of this method. Concentrations of the last three pesticides were above the European MRL in one of the commercial samples.     

Multiclass Probability Estimation With Support Vector Machines

Multiclass classification and probability estimation have important applications in data analytics. Support vector machines (SVMs) have shown great success in various real-world problems due to their high classification accuracy. However, one main limitation of standard SVMs is that they do not provide class probability estimates, and thus fail to offer uncertainty measure about class prediction. In this article, we propose a simple yet effective framework to endow kernel SVMs with the feature of multiclass probability estimation. The new probability estimator does not rely on any parametric assumption on the data distribution, therefore, it is flexible and robust. Theoretically, we show that the proposed estimator is asymptotically consistent. Computationally, the new procedure can be conveniently implemented using standard SVM softwares. Our extensive numerical studies demonstrate competitive performance of the new estimator when compared with existing methods such as multiple logistic regression, linear discrimination analysis, tree-based methods, and random forest, under various classification settings. Supplementary materials for this article are available online.