Preemptive Scheduling for Age of Job Minimization in Task-Specific Machine Networks

2026-02-02

AI Summary

This work addresses the challenge of limited resources in task-specific machine networks that prevent parallel processing of all user jobs. To capture timeliness, the paper introduces "Age of Job" as a novel performance metric and aims to minimize the long-term weighted average job age. By leveraging Lyapunov drift theory, a Max-Weight policy is constructed, and under geometric service times, an optimal Whittle index policy is derived using Whittle index theory. For general service time distributions, a hybrid WIMWF (Whittle Index–Max-Weight Fusion) policy is proposed. Theoretical analysis and simulations demonstrate that WIMWF achieves superior performance under general service time distributions, while the Whittle index policy remains optimal under geometric service times. Moreover, as system scale increases, the NGM policy asymptotically outperforms Max-Weight.

Technology Category

• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• preemptive scheduling
• age of job
• task-specific machine networks
• job assignment
• timeliness metric

Innovation

• Age of Job
• Whittle Index
• Preemptive Scheduling
• Max-Weight Policy
• Task-Specific Networks