arXiv subjects

Alexander L. Stolyar

Publications and source records attributed to Alexander L. Stolyar.

6 recordsLinked to original sources

arXiv2026

Asymptotic optimality of dynamic first-fit packing on the half-axis

We revisit a classical problem in dynamic storage allocation. Items arrive in a linear storage medium, modeled as a half-axis, at a Poisson rate $r$ and depart after an independent exponentially distributed unit mean service time. The arriving item sizes (lengths) are assumed to be independent and identically distributed (i.i.d.) from a common distribution $H$. A widely employed algorithm for allocating the items is the "first-fit" discipline, namely, each arriving item is placed in the left-most vacant interval large enough to accommodate it. In a seminal 1985 paper, Coffman, Kadota, and Shepp ([6]) proved that in the special case of unit length items (i.e. degenerate $H$), as $r$ tends towards infinity, the first-fit algorithm is asymptotically optimal in the following sense: the steady-state ratio of expected "empty space" (gaps between items) to expected occupied space tends towards $0$. In a sequel to [6], Coffman, Kadota, and Shepp ([5]) conjectured that the first-fit discipline is also asymptotically optimal for non-degenerate $H$. In this paper we provide the first proof of first-fit asymptotic optimality for non-degenerate distributions $H$ of item sizes. Our main result is for the case when $H$ is concentrated on countably many positive real sizes forming an increasing sequence that is either finite or goes to infinity, with the average item size being finite. We prove that under the first-fit discipline, as $r$ tends towards infinity, the steady-state packing configuration (scaled down by $r$) converges in distribution to the limiting packing configuration with smaller items on the left, larger items on the right, and with no gaps between. In particular, this proves asymptotic optimality of first-fit in the sense that in steady-state the empty space (scaled down by $r$) vanishes.

math.PR↗

arXiv2018

Reward Maximization in General Dynamic Matching Systems

We consider a matching system with random arrivals of items of different types. The items wait in queues -- one per each item type -- until they are "matched." Each matching requires certain quantities of items of different types; after a matching is activated, the associated items leave the system. There exists a finite set of possible matchings, each producing a certain amount of "reward". This model has a broad range of important applications, including assemble-to-order systems, Internet advertising, matching web portals, etc. We propose an optimal matching scheme in the sense that it asymptotically maximizes the long-term average matching reward, while keeping the queues stable. The scheme makes matching decisions in a specially constructed virtual system, which in turn control decisions in the physical system. The key feature of the virtual system is that, unlike the physical one, it allows the queues to become negative. The matchings in the virtual system are controlled by an extended version of the greedy primal-dual (GPD) algorithm, which we prove to be asymptotically optimal -- this in turn implies the asymptotic optimality of the entire scheme. The scheme is real-time, at any time it uses simple rules based on the current state of virtual and physical queues. It is very robust in that it does not require any knowledge of the item arrival rates, and automatically adapts to changing rates. The extended GPD algorithm and its asymptotic optimality apply to a quite general queueing network framework, not limited to matching problems, and therefore is of independent interest.

math.PR↗

arXiv2015

A service system with on-demand agent invitations

We consider a service system where agents are invited on-demand. Customers arrive exogenously as a Poisson process and join a customer queue upon arrival if no agent is available. Agents decide to accept or decline invitations after some exponentially distributed random time, and join an agent queue upon invitation acceptance if no customer is waiting. A customer and an agent are matched in the order of customer arrival and agent invitation acceptance under the non-idling condition, and will leave the system simultaneously once matched (service times are irrelevant here). We consider a feedback-based adaptive agent invitation scheme, which controls the number of pending agent invitations, depending on the customer and/or agent queue lengths and their changes. The system process has two components -- `the difference between agent and customer queues' and `the number of pending invitations', and is a countable continuous-time Markov chain. For the case when the customer arrival rate is constant, we establish fluid and diffusion limits, in the asymptotic regime where the customer arrival rate goes to infinity, while the agent response rate is fixed. We prove the process stability and fluid-scale limit interchange, which in particular imply that that both customer and agent waiting times in steady-state vanish in the asymptotic limit. To do this we develop a novel (multi-scale) Lyapunov drift argument; it is required because the process has non-trivial behavior on the state space boundary. When the customer arrival rate is time-varying, we present a fluid limit for the processes in the same asymptotic regime. Simulation experiments are conducted to show good performance of the invitation scheme and accuracy of fluid limit approximations.

math.PR↗

arXiv2014

Tightness of stationary distributions of a flexible-server system in the Halfin-Whitt asymptotic regime

We consider a large-scale flexible service system with two large server pools and two types of customers. 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." Our results hold for a more general class of systems as well.) The service rate of a customer depends both on its type and the pool where it is served. We study a priority service discipline, where type 2 has priority in pool 2, and type 1 prefers pool 1. 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 {\em 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.

math.PR↗

arXiv2013

Systems with large flexible server pools: Instability of "natural" load balancing

We consider general large-scale service systems with multiple customer classes and multiple server (agent) pools, mean service times depend both on the customer class and server pool. It is assumed that the allowed activities (routing choices) form a tree (in the graph with vertices being both customer classes and server pools). We study the behavior of the system under a natural (load balancing) routing/scheduling rule, Longest-Queue Freest-Server (LQFS-LB), in the many-server asymptotic regime, such that the exogenous arrival rates of the customer classes, as well as the number of agents in each pool, grow to infinity in proportion to some scaling parameter $r$. Equilibrium point of the system under LQBS-LB is the desired operating point, with server pool loads minimized and perfectly balanced. Our main results are as follows. (a) We show that, quite surprisingly (given the tree assumption), for certain parameter ranges, the fluid limit of the system may be unstable in the vicinity of the equilibrium point; such instability may occur if the activity graph is not "too small." (b) Using (a), we demonstrate that the sequence of stationary distributions of diffusion-scaled processes [measuring $O(\sqrt{r})$ deviations from the equilibrium point] may be nontight, and in fact may escape to infinity. (c) In one special case of interest, however, we show that the sequence of stationary distributions of diffusion-scaled processes is tight, and the limit of stationary distributions is the stationary distribution of the limiting diffusion process.

math.PR↗

arXiv2012

A large-scale service system with packing constraints: Minimizing the number of occupied servers

We consider a large-scale service system model motivated by the problem of efficient placement of virtual machines to physical host machines in a network cloud, so that the total number of occupied hosts is minimized. Customers of different types arrive to a system with an infinite number of servers. A server packing configuration is the vector $k = (k_i)$, where $k_i$ is the number of type-$i$ customers that the server "contains". Packing constraints are described by a fixed finite set of allowed configurations. Upon arrival, each customer is placed into a server immediately, subject to the packing constraints; the server can be idle or already serving other customers. After service completion, each customer leaves its server and the system. It was shown recently that a simple real-time algorithm, called Greedy, is asymptotically optimal in the sense of minimizing $\sum_k X_k^{1+α}$ in the stationary regime, as the customer arrival rates grow to infinity. (Here α>0, and $X_k$ denotes the number of servers with configuration $k$.) In particular, when parameter αis small, Greedy approximately solves the problem of minimizing $\sum_k X_k$, the number of occupied hosts. In this paper we introduce the algorithm called Greedy with sublinear Safety Stocks (GSS), and show that it asymptotically solves the exact problem of minimizing $\sum_k X_k$. An important feature of the algorithm is that sublinear safety stocks of $X_k$ are created automatically - when and where necessary - without having to determine a priori where they are required. Moreover, we also provide a tight characterization of the rate of convergence to optimality under GSS. The GSS algorithm is as simple as Greedy, and uses no more system state information than Greedy does.

math.OC↗