Flow Network Models for Online Scheduling Real-time Tasks on Multiprocessors
Hyeonjoong Cho, Arvind Easwaran

TL;DR
This paper introduces a novel flow network-based online scheduling method for real-time multiprocessor systems, enabling unfair workload collection while maintaining optimality and reducing overheads.
Contribution
It proposes the first online flow network model for real-time scheduling that guarantees optimality with unfair workload collection, along with polynomial-time algorithms.
Findings
Reduces preemptions significantly
Maintains task deadlines under utilization constraints
First discrete-time unfair-but-optimal scheduling algorithm
Abstract
We consider the flow network model to solve the multiprocessor real-time task scheduling problems. Using the flow network model or its generic form, linear programming (LP) formulation, for the problems is not new. However, the previous works have limitations, for example, that they are classified as offline scheduling techniques since they establish a flow network model or an LP problem considering a very long time interval. In this study, we propose how to construct the flow network model for online scheduling periodic real-time tasks on multiprocessors. Our key idea is to construct the flow network only for the active instances of tasks at the current scheduling time, while guaranteeing the existence of an optimal schedule for the future instances of the tasks. The optimal scheduling is here defined to ensure that all real-time tasks meet their deadlines when the total utilization…
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Taxonomy
TopicsReal-Time Systems Scheduling · Interconnection Networks and Systems · Distributed systems and fault tolerance
