Selective Fair Scheduling over Fading Channels

TL;DR

This paper introduces selective fairness in wireless scheduling, improving the Price of Fairness by selectively blocking users with poor channels, and proposes an efficient, optimal online policy for resource allocation under SLA constraints.

Contribution

It proposes a novel selective fairness framework, an efficient user selection algorithm, and an optimal online policy combining drift-plus-penalty and gradient-based scheduling.

Findings

Selective fairness reduces the Price of Fairness significantly.

The proposed online policy achieves optimal Price of Fairness.

Simulations show 40% throughput improvement over previous methods.

Abstract

Imposing fairness in resource allocation incurs a loss of system throughput, known as the Price of Fairness ($PoF$). In wireless scheduling, $PoF$ increases when serving users with very poor channel quality because the scheduler wastes resources trying to be fair. This paper proposes a novel resource allocation framework to rigorously address this issue. We introduce selective fairness: being fair only to selected users, and improving $PoF$ by momentarily blocking the rest. We study the associated admission control problem of finding the user selection that minimizes $PoF$ subject to selective fairness, and show that this combinatorial problem can be solved efficiently if the feasibility set satisfies a condition; in our model it suffices that the wireless channels are stochastically dominated. Exploiting selective fairness, we design a stochastic framework where we minimize $PoF$ subject to an SLA, which ensures that an ergodic subscriber is served frequently enough. In this context, we propose an online policy that combines the drift-plus-penalty technique with Gradient-Based Scheduling experts, and we prove it achieves the optimal $PoF$. Simulations show that our intelligent blocking outperforms by 40$\%$ in throughput previous approaches which satisfy the SLA by blocking low-SNR users.