Resource Augmentation

TL;DR

This paper explores resource augmentation, comparing algorithm performance with limited resources across three case studies, and discusses bounds that show near-optimal performance for most resource levels.

Contribution

It introduces resource augmentation bounds and applies them to online paging, selfish routing, and scheduling, highlighting their implications for algorithm performance.

Findings

Resource augmentation bounds provide near-optimal performance guarantees.

Application to online paging, selfish routing, and scheduling demonstrates broad relevance.

Loosely competitive bounds imply algorithms perform well across most resource levels.

Abstract

This chapter introduces resource augmentation, in which the performance of an algorithm is compared to the best-possible solution that is handicapped by less resources. We consider three case studies: online paging, with cache size as the resource; selfish routing, with capacity as the resource; and scheduling, with processor speed as the resource. Resource augmentation bounds also imply "loosely competitive" bounds, which show that an algorithm's performance is near-optimal for most resource levels.