资源强化
Resource Augmentation
摘要：
本章介绍了资源增强，其中算法的性能与最佳可能的解决方案进行比较，该解决方案受到较少资源的限制。我们考虑三个案例研究：在线分页，与缓存大小作为资源;自私的路由，与容量作为资源;和调度，与处理器速度作为资源。资源扩充界限也意味着“松散竞争”界限，这表明算法的性能在大多数资源水平下接近最优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
