Laxity-Based Opportunistic Scheduling with Flow-Level Dynamics and Deadlines

TL;DR

This paper introduces a laxity-based scheduling policy for cellular networks that ensures deadline guarantees for flow-level data transmissions, demonstrating its asymptotic optimality and practical effectiveness through simulations.

Contribution

It proposes the L$^2$HPR policy, proving its asymptotic optimality in certain systems, and develops heuristics for practical implementation, advancing deadline-aware scheduling.

Findings

L$^2$HPR is asymptotically optimal in underloaded systems.

Proposed heuristics outperform greedy policies in simulations.

Framework provides insights for designing deadline-aware schedulers.

Abstract

Many data applications in the next generation cellular networks, such as content precaching and video progressive downloading, require flow-level quality of service (QoS) guarantees. One such requirement is deadline, where the transmission task needs to be completed before the application-specific time. To minimize the number of uncompleted transmission tasks, we study laxity-based scheduling policies in this paper. We propose a Less-Laxity-Higher-Possible-Rate (L$^2$HPR) policy and prove its asymptotic optimality in underloaded identical-deadline systems. The asymptotic optimality of L$^2$HPR can be applied to estimate the schedulability of a system and provide insights on the design of scheduling policies for general systems. Based on it, we propose a framework and three heuristic policies for practical systems. Simulation results demonstrate the asymptotic optimality of L$^2$HPR and performance improvement of proposed policies over greedy policies.