Achieving Utility-Delay-Reliability Tradeoff in Stochastic Network Optimization with Finite Buffers

TL;DR

This paper introduces a floating-queue algorithm for stochastic network optimization that achieves near-optimal utility with small finite buffers, balancing delay and packet drop rates efficiently.

Contribution

It proposes a novel floating-queue algorithm that reduces buffer size requirements significantly while maintaining near-optimal utility and low delay in stochastic networks.

Findings

Achieves utility within O(e^{-B}) of optimal with buffer size B.

Maintains average delay of O(B) and drop rate of O(e^{-B}).

Requires minimal modifications to existing policies like MaxWeight.

Abstract

One practical open problem is the development of a distributed algorithm that achieves near-optimal utility using only a finite (and small) buffer size for queues in a stochastic network. This paper studies utility maximization (or cost minimization) in a finite-buffer regime and considers the corresponding delay and reliability (or rate of packet drops) tradeoff. A floating-queue algorithm allows the stochastic network optimization framework to be implemented with finite buffers at the cost of packet drops. Further, the buffer size requirement is significantly smaller than previous works in this area. With a finite buffer size of $B$ packets, the proposed algorithm achieves within $O(e^{-B})$ of the optimal utility while maintaining average per-hop delay of $O(B)$ and an average per-hop drop rate of $O(e^{-B})$ in steady state. From an implementation perspective, the floating-queue algorithm requires little modification of the well-known Drift-Plus-Penalty policy (including MaxWeight and Backpressure policies). As a result, the floating-queue algorithm inherits the distributed and low complexity nature of these policies.