抢占调度以最小化任务特定机器网络中的作业年龄
Preemptive Scheduling for Age of Job Minimization in Task-Specific Machine Networks
AI总结：
本文提出WIMWF策略以最小化任务特定机器网络中的作业年龄，通过分析不同调度策略在不同系统规模下的性能表现，发现其在一般作业完成时间设置中表现最佳。
摘要：
我们考虑一个时间分片的作业分配系统，包括一个中央服务器、N个任务特定的机器网络以及多个用户。每个网络专门执行一种不同的任务。用户随机生成各种类型的作业并将其转发给中央服务器，该服务器将每个作业路由到适当的机器网络。由于资源限制，服务器无法同时服务所有用户作业，这促使设计具有可能抢占的调度策略。为了评估调度性能，我们引入了一个新的时效度量标准，即作业年龄，灵感来自著名的信息年龄度量标准。我们研究了最小化长期加权平均作业年龄的问题。我们首先提出了一种通过最小化一步Lyapunov漂移的max-weight策略，然后在机器网络的作业完成时间服从几何分布时推导出Whittle指数（WI）策略。对于一般的作业完成时间分布，我们引入了具有max-weight回退（WIMWF）的Whittle指数策略。我们还研究了Net-gain最大化（NGM）策略。数值上，我们显示所提出的WIMWF策略在一般作业完成时间设置中表现最佳。我们还观察到一个缩放趋势：两种不同的max-weight策略在小型系统中可以优于NGM策略，而随着系统规模的扩大，NGM策略会改善，并在渐近情况下优于max-weight策略。对于几何服务时间，WI策略在所有考虑的系统规模中产生最低的年龄。
英文摘要：
We consider a time-slotted job-assignment system consisting of a central server, $N$ task-specific networks of machines, and multiple users. Each network specializes in executing a distinct type of task. Users stochastically generate jobs of various types and forward them to the central server, which routes each job to the appropriate network of machines. Due to resource constraints, the server cannot serve all users' jobs simultaneously, which motivates the design of scheduling policies with possible preemption. To evaluate scheduling performance, we introduce a novel timeliness metric, the age of job, inspired by the well-known metric, the age of information. We study the problem of minimizing the long-term weighted average age of job. We first propose a max-weight policy by minimizing the one-step Lyapunov drift and then derive the Whittle index (WI) policy when the job completion times of the networks of machines follow geometric distributions. For general job completion time distributions, we introduce a Whittle index with max-weight fallback (WIMWF) policy. We also investigate the Net-gain maximization (NGM) policy. Numerically, we show that the proposed WIMWF policy achieves the best performance in the general job completion time setting. We also observe a scaling trend: two different max-weight policies can outperform the NGM policy in small systems, whereas the NGM policy improves as we scale the system size and becomes asymptotically better than max-weight policies. For geometric service times, the WI policy yields the lowest age across all considered system sizes.
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。
