An Empirical Study of Online Packet Scheduling Algorithms

TL;DR

This paper compares online packet scheduling algorithms, introduces the MLP algorithm that balances weight and deadline considerations, and demonstrates its practical advantages through simulations.

Contribution

It presents the MLP algorithm that improves practical performance over MG, and offers new algorithms for buffer management in online packet scheduling.

Findings

MG is 1.618-competitive with agreeable deadlines

MLP performs better in practice despite lower theoretical ratio

Simulations validate the practical advantages of MLP

Abstract

This work studies online scheduling algorithms for buffer management, develops new algorithms, and analyzes their performances. Packets arrive at a release time r, with a non-negative weight w and an integer deadline d. At each time step, at most one packet is scheduled. The modified greedy (MG) algorithm is 1.618-competitive for the objective of maximizing the sum of weights of packets sent, assuming agreeable deadlines. We analyze the empirical behavior of MG in a situation with arbitrary deadlines and demonstrate that it is at a disadvantage when frequently preferring maximum weight packets over early deadline ones. We develop the MLP algorithm, which remedies this problem whilst mimicking the behavior of the offline algorithm. Our comparative analysis shows that, although the competitive ratio of MLP is not as good as that of MG, it performs better in practice. We validate this by simulating the behavior of both algorithms under a spectrum of simulated parameter settings. Finally, we propose the design of three additional algorithms, which may help in improving performance in practice.