Age of Job Completion Minimization with Stable Queues
Stavros Mitrolaris, Subhankar Banerjee, Sennur Ulukus

TL;DR
This paper studies a job-assignment system with a Markovian machine state, proposing policies to minimize the age of job completion while ensuring queue stability in a stochastic environment.
Contribution
It introduces new policies for minimizing job completion age in a Markov machine environment and analyzes their stability and performance.
Findings
Proposed policies effectively reduce the age of job completion.
Derived sufficient conditions for queue stability under the policies.
Numerical evaluation demonstrates the policies' performance benefits.
Abstract
We consider a time-slotted job-assignment system with a central server, N users and a machine which changes its state according to a Markov chain (hence called a Markov machine). The users submit their jobs to the central server according to a stochastic job arrival process. For each user, the server has a dedicated job queue. Upon receiving a job from a user, the server stores that job in the corresponding queue. When the machine is not working on a job assigned by the server, the machine can be either in internally busy or in free state, and the dynamics of these states follow a binary symmetric Markov chain. Upon sampling the state information of the machine, if the server identifies that the machine is in the free state, it schedules a user and submits a job to the machine from the job queue of the scheduled user. To maximize the number of jobs completed per unit time, we introduce…
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Taxonomy
TopicsAge of Information Optimization · Advanced Queuing Theory Analysis · IoT and Edge/Fog Computing
