Heavy-Traffic Comparison of a Discrete-Time Generalized Processor Sharing Queue and a Pure Randomly Alternating Service Queue

This paper compares two discrete-time single-server queueing models with two queues. In both models, the server is available to a queue with probability 1/2 at each service opportunity. Since obtaining easy-to-evaluate expressions for the joint moments is not feasible, we rely on a heavy-traffic limit approach. The correlation coefficient of the queue-contents is computed via the solution of a two-dimensional functional equation obtained by reducing it to a boundary value problem on a hyperbola. In most server-sharing models, it is assumed that the system is work-conserving in the sense that if one of the queues is empty, a customer of the other queue is served with probability 1. In our second model, we omit this work-conserving rule such that the server can be idle in case of a non-empty queue. Contrary to what we would expect, the resulting heavy-traffic approximations reveal that both models remain different for critically loaded queues.

Heavy Traffic Analysis of Approximate Max-Weight Matching Algorithms for Input-Queued Switches

In this paper, we propose a class of approximation algorithms for max-weight matching (MWM) policy for input-queued switches, called expected 1-APRX. We establish the state space collapse (SSC) result for expected 1-APRX, and characterize its queue length behavior in the heavy-traffic limit.

Heavy traffic analysis for single-server SRPT and LRPT queues via EDF diffusion limits

Extending the results of Kruk (Queueing theory and network applications. QTNA 2019. Lecture notes in computer science, vol 11688. Springer, Cham, pp 263–275, 2019), we derive heavy traffic limit theorems for a single server, single customer class queue in which the server uses the Shortest Remaining Processing Time (SRPT) policy from heavy traffic limits for the corresponding Earliest Deadline First queueing systems. Our analysis allows for correlated customer inter-arrival and service times and heavy-tailed inter-arrival and service time distributions, as long as the corresponding stochastic primitive processes converge weakly to continuous limits under heavy traffic scaling. Our approach yields simple, concise justifications and new insights for SRPT heavy traffic limit theorems of Gromoll et al. (Stoch Syst 1(1):1–16, 2011). Corresponding results for the longest remaining processing time policy are also provided.

Control Policies Approaching Hierarchical Greedy Ideal Performance in Heavy Traffic for Resource Sharing Networks

We consider resource sharing networks of the form introduced in work of Massoulié and Roberts as models for Internet flows. The goal is to study the open problem, formulated in Harrison et al. (2014) [Harrison JM, Mandayam C, Shah D, Yang Y (2014) Resource sharing networks: Overview and an open problem. Stochastic Systems 4(2):524–555.], of constructing simple form rate-allocation policies for broad families of resource sharing networks with associated costs converging to the hierarchical greedy ideal performance in the heavy traffic limit. We consider two types of cost criteria: an infinite horizon discounted cost and a long-time average cost per unit time. We introduce a sequence of rate-allocation control policies that are determined in terms of certain thresholds for the scaled queue-length processes and prove that, under conditions, both type of costs associated with these policies converge in the heavy traffic limit to the corresponding hierarchical greedy ideal (HGI) performance. The conditions needed for these results are satisfied by all the examples considered in the above cited paper of Harrison et al.