带预测的排序问题及预测失误的代价
Scheduling with Predictions and the Price of Misprediction
摘要：
在许多传统的作业调度设置中，假设人们知道作业完成服务所需的时间。在这种情况下，可以使用诸如最短作业优先之类的策略来提高性能，例如作业在系统中等待的平均时间。我们考虑服务时间未知但通过例如机器学习算法预测的设置。我们的主要结果是推导，在自然的假设下，公式的性能的几种策略，使用预测的服务时间，以调度工作的系统。作为我们分析的一部分，我们提出了“错误预测的价格”框架，它提供了一个衡量使用预测信息的成本的标准。
英文摘要：
In many traditional job scheduling settings, it is assumed that one knows the time it will take for a job to complete service. In such cases, strategies such as shortest job first can be used to improve performance in terms of measures such as the average time a job waits in the system. We consider the setting where the service time is not known, but is predicted by for example a machine learning algorithm. Our main result is the derivation, under natural assumptions, of formulae for the performance of several strategies for queueing systems that use predictions for service times in order to schedule jobs. As part of our analysis, we suggest the framework of the "price of misprediction," which offers a measure of the cost of using predicted information.
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。
