We review the notions of multivariate regular variation (MRV) and hidden regular variation (HRV) for distributions of random vectors and then discuss methods for generating models exhibiting both properties concentrating on the non-negative orthant in dimension two. Furthermore we suggest diagnostic techniques that detect these properties in multivariate data and indicate when models exhibiting both MRV and HRV are plausible fits for the data. We illustrate our techniques on simulated data, as well as two real Internet data sets.

A large-scale flexible service system with two large server pools and two types of customers is considered. Servers in pool 1 can only serve type 1 customers, while server in pool 2 are flexible – they can serve both types 1 and 2. (This is a so-called “N-system.”) The customer arrival processes are Poisson and customer service requirements are independent exponentially distributed. The service rate of a customer depends both on its type and the pool where it is served. A priority service discipline, where type 2 has priority in pool 2, and type 1 prefers pool 1, is considered. We consider the Halfin-Whitt asymptotic regime, where the arrival rate of customers and the number of servers in each pool increase to infinity in proportion to a scaling parameter $n$, while the overall system capacity exceeds its load by $O(\sqrt{n})$.

For this system we prove tightness of diffusion-scaled stationary distributions. Our approach relies on a single common Lyapunov function $G^{(n)}(x)$, depending on parameter $n$ and defined on the entire state space as a functional of the drift-based fluid limits (DFL). Specifically, $G^{(n)}(x)=\int_{0}^{\infty}g(y^{(n)}(t))dt$, where $y^{(n)}(\cdot)$ is the DFL starting at $x$, and $g(\cdot)$ is a “distance” to the origin. ($g(\cdot)$ is same for all $n$). The key part of the analysis is the study of the (first and second) derivatives of the DFLs and function $G^{(n)}(x)$. The approach, as well as many parts of the analysis, are quite generic and may be of independent interest.

We consider a network revenue management problem and advance its dual formulation. The dual formulation reveals that the (optimal) shadow price of capacity forms a nonnegative martingale. This result is proved under minimal assumptions on network topology and stochastic nature of demand, allowing an arbitrary statistical dependence structure across time and products. Next, we consider a quadratic perturbation of the network revenue management problem and show that a simple (perturbed) bid-price control is optimal for the perturbed problem; and it is $\varepsilon$-optimal for the original network revenue management problem. Finally, we consider a predictable version of this control, where the bid prices used in the current period are last updated in the previous period, and provide an upper bound on its optimality gap in terms of the (quadratic) variation of demand. Using this upper bound we show that there exists a near-optimal such control in the usual case when periods are small compared to the planning horizon provided that either demand or the incremental information arriving during each period is small. We establish the martingale property of the (near) optimal bid prices in both settings. The martingale property can have important implications in practice as it may offer a tool for monitoring the revenue management systems.

We model the problem of managing capacity in a build-to-order environment as a Brownian drift control problem. We formulate a structured linear program that models a practical discretization of the problem and exploit a strong relationship between relative value functions and dual solutions to develop a functional lower bound for the continuous problem from a dual solution to the discrete problem. Refining the discretization proves a functional strong duality for the continuous problem. The linear programming formulation is so badly scaled, however, that solving it is beyond the capabilities of standard solvers. By demonstrating the equivalence between strongly feasible bases and deterministic unichain policies, we combinatorialize the pivoting process and by exploiting the relationship between dual solutions and relative value functions, develop a mechanism for solving the LP without ever computing its coefficients. Finally, we exploit the relationship between relative value functions and dual solutions to develop a scheme analogous to column generation for refining the discretization so as to drive the gap between the discrete approximation and the continuous problem to zero quickly while keeping the LP small. Computational studies show our scheme is much faster than simply solving a regular discretization of the problem both in terms of finding a policy with a low average cost and in terms of providing a lower bound on the optimal average cost.

We study the problem of the existence of a giant component in a random multipartite graph. We consider a random multipartite graph with $p$ parts generated according to a given degree sequence $n_{i}^{\mathbf{d}}(n),n\ge1$ which denotes the number of vertices in part $i$ of the multipartite graph with degree given by the vector $\mathbf{d}$ in an $n$-node graph. We assume that the empirical distribution of the degree sequence converges to a limiting probability distribution. Under certain mild regularity assumptions, we characterize the conditions under which, with high probability, there exists a component of linear size. The characterization involves checking whether the Perron-Frobenius norm of the matrix of means of a certain associated edge-biased distribution is greater than unity. We also specify the size of the giant component when it exists. We use the exploration process of Molloy and Reed Molloy and Reed (1995) to analyze the size of components in the random graph. The main challenges arise due to the multidimensionality of the random processes involved which prevents us from directly applying the techniques from the standard unipartite case. In this paper we use techniques from the theory of multidimensional Galton-Watson processes along with Lyapunov function technique to overcome the challenges.