In this paper, we introduce the concept of “workload fence" into online machine rental and machine scheduling problems. With the knowledge of workload fence, online algorithms acquire the information of a finite number of first released jobs in advance. The concept originates from the frozen time fence in the domain of master scheduling in materials management. The total processing time of the jobs foreseen, corresponding to a finite number of jobs, is called workload fence, which is irrelevant to the job sequence. The remaining jobs in the sequence, however, can only become known on their arrival. This work aims to reveal whether the knowledge of workload fence helps to boost the competitive performance of deterministic online algorithms. For the online machine rental problem, we prove that the competitiveness of online algorithms can be improved with a sufficiently large workload fence. We further propose a best online algorithm for the corresponding scenario. For online parallel machine scheduling with workload fence, we give a positive answer to the above question for the case where the workload fence is equal to the length of the longest job. We also show that the competitiveness of online algorithms may not be improved even with a workload fence strictly larger than the largest length of a job. The results help one manager to make a better decision regarding the tradeoff between the performance improvement of online algorithms and the cost caused to acquire the knowledge of workload fence.

在本文中，我们将“工作量围栏”的概念引入在线机器租赁和机器调度问题中。利用工作量围栏的知识，在线算法预先获取有限数量的首次发布作业的信息。这个概念起源于物料管理中的主调度领域的冻结时间栅栏，对应于有限数量的作业，可预见的作业的总处理时间，称为工作量栅栏，这与工作顺序无关。然而，序列中的剩余工作只有在它们到达时才能被知道。这项工作旨在揭示工作负载围栏的知识是否有助于提高确定性在线算法的竞争性能。对于在线机器租赁问题，我们证明了在线算法的竞争力可以通过足够大的工作量围栏来提高。我们进一步提出了针对相应场景的最佳在线算法。对于带工作量围栏的在线并行机器调度，对于工作量围栏等于最长作业长度的情况，我们对上述问题给出了肯定的答案。我们还表明，即使工作量围栏严格大于作业的最大长度，在线算法的竞争力也可能不会提高。