Preemptive Scheduling for Age of Job Minimization in Task-Specific Machine Networks
Subhankar Banerjee, Sennur Ulukus

TL;DR
This paper introduces novel scheduling policies to minimize the age of jobs in task-specific machine networks, employing a new timeliness metric and analyzing their performance through theoretical and numerical methods.
Contribution
It proposes new max-weight, Whittle index, and Net-gain policies for age minimization, including extensions for general job completion time distributions.
Findings
WIMWF policy outperforms others in general settings.
Whittle index policy minimizes age for geometric service times.
Max-weight policies excel in small systems, NGM improves with scale.
Abstract
We consider a time-slotted job-assignment system consisting of a central server, 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…
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Taxonomy
TopicsAge of Information Optimization · IoT and Edge/Fog Computing · Advanced Queuing Theory Analysis
