An Experimental Comparison of Speed Scaling Algorithms with Deadline Feasibility Constraints

TL;DR

This paper experimentally compares four online speed scaling algorithms under deadline constraints using web server trace data, confirming that competitive analysis accurately predicts their real-world performance.

Contribution

It validates the predictive power of competitive analysis for online algorithms in speed scaling with deadline constraints through extensive experiments.

Findings

Experimental ranking matches theoretical predictions

Performance consistent across various power functions

Algorithms perform well even with temperature-based power objectives

Abstract

We consider the first, and most well studied, speed scaling problem in the algorithmic literature: where the scheduling quality of service measure is a deadline feasibility constraint, and where the power objective is to minimize the total energy used. Four online algorithms for this problem have been proposed in the algorithmic literature. Based on the best upper bound that can be proved on the competitive ratio, the ranking of the online algorithms from best to worst is: $\qOA$, $\OA$, $\AVR$, $\BKP$. As a test case on the effectiveness of competitive analysis to predict the best online algorithm, we report on an experimental "horse race" between these algorithms using instances based on web server traces. Our main conclusion is that the ranking of our algorithms based on their performance in our experiments is identical to the order predicted by competitive analysis. This ranking holds over a large range of possible power functions, and even if the power objective is temperature.