Scheduling Stochastic Real-Time Jobs in Unreliable Workers
Yu-Pin Hsu, Yu-Chih Huang, and Shin-Lin Shieh

TL;DR
This paper develops and analyzes scheduling algorithms for real-time jobs in distributed systems with unreliable workers, aiming to maximize completed jobs within deadlines while balancing computational complexity.
Contribution
It introduces a feasibility-optimal scheduling algorithm and a computationally efficient approximate algorithm with performance guarantees.
Findings
Feasibility-optimal algorithm maximizes application requirements.
Approximate algorithm offers a guaranteed worst-case performance ratio.
Simulations validate the effectiveness of the approximate scheduling method.
Abstract
We consider a distributed computing network consisting of a master and multiple workers processing tasks of different types. The master is running multiple applications. Each application stochastically generates real-time jobs with a strict job deadline, where each job is a collection of tasks of some types specified by the application. A real-time job is completed only when all its tasks are completed by the corresponding workers within the deadline. Moreover, we consider unreliable workers, whose processing speeds are uncertain. Because of the limited processing abilities of the workers, an algorithm for scheduling the jobs in the workers is needed to maximize the average number of completed jobs for each application. The scheduling problem is not only critical but also practical in distributed computing networks. In this paper, we develop two scheduling algorithms, namely, a…
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