Scheduling using Interactive Optimization Oracles for Constrained Queueing Networks

TL;DR

This paper introduces a flexible framework for designing low-complexity, throughput-optimal scheduling algorithms in constrained queueing networks by interacting with various oracle systems for approximate optimization.

Contribution

It presents a generic, unified framework that leverages different oracle systems to create efficient scheduling algorithms with provable throughput optimality.

Findings

Framework encompasses existing algorithms like Tassiulas's linear-time method.

Framework allows for designing new algorithms with low complexity.

Provides sufficient conditions for throughput optimality in general models.

Abstract

Ever since Tassiulas and Ephremides (1992) proposed the maximum weight scheduling algorithm of throughput-optimality for constrained queueing networks that arise in the context of communication networks, extensive efforts have been devoted to resolving its most important drawback: high complexity. This paper proposes a generic framework for designing throughput- optimal and low-complexity scheduling algorithms for constrained queueing networks. Under our framework, a scheduling algorithm updates current schedules by interacting with a given oracle system that generates an approximate solution to a related optimization task. One can utilize our framework to design a variety of scheduling algorithms by choosing an oracle system such as random search, Markov chain, belief propagation, and primal-dual methods. The complexity of the resulting scheduling algorithm is determined by the number of operations required for an oracle to process a single query, which is typically small. We provide sufficient conditions for throughput-optimality of the scheduling algorithm in general constrained queueing network models. The linear-time algorithm of Tassiulas (1998) and the random access algorithm of Shah and Shin (2012) correspond to special cases of our framework using random search and Markov chain oracles, respectively. Our generic framework, however, provides a unified proof with milder assumptions.