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 demand of the given tasks does not exceed the total processing capacity. We then propose the flow network model-based polynomial-time scheduling algorithms. Advantageously, the flow network model allows the task workload to be collected unfairly within a certain time interval without losing the optimality. It thus leads us to designing three unfair-but-optimal scheduling algorithms on both continuous and discrete-time models. Especially, our unfair-but-optimal scheduling algorithm on a discrete-time model is, to the best of our knowledge, the first in the problem domain. We experimentally demonstrate that it significantly alleviates the scheduling overheads, i.e., the reduced number of preemptions with the comparable number of task migrations across processors, in comparison with an existing algorithm on the discrete-time model.
Highlights
Multicore or multiprocessor platforms are becoming prevalent in numerous digital devices and this advance has been accelerated by the increasing computational demands of various emerging high-quality services
Our contributions include the following: (1) We formulate the problem for online-scheduling the periodic implicit-deadline tasks on multiprocessors by specifying its constraints and we propose a flow network model to solve the formulated problem
We experimentally show that flow network-based Earliest-Deadline-First (fn-EDF) on the discrete-time model significantly reduces the number of preemptions with the comparable number of migrations against an existing boundary-fair scheduling (BF) algorithm
Summary
Multicore or multiprocessor platforms are becoming prevalent in numerous digital devices and this advance has been accelerated by the increasing computational demands of various emerging high-quality services Alongside this trend, there has been a vast amount of research into multiprocessor real-time scheduling theories [4], [9]. The simple fact that a task can use only one processor even when several processors are free at the same time adds a surprising amount of difficulty to the scheduling of multiple processors’’ This statement can be interpreted as saying that in uniprocessors, the constraint that each task is forbidden from executing simultaneously on more than one processor is implicit, because a single processor is the only processing capacity present in the system. In multiprocessors, the constraint of no intra-task parallelism becomes explicit and interrelated
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