Premium
Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems
Author(s) -
Chen Junjie,
Li Kenli,
Tang Zhuo,
Liu Chubo,
Wang Yan,
Li Keqin
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3772
Subject(s) - computer science , multiprocessor scheduling , scheduling (production processes) , multiprocessing , parallel computing , two level scheduling , dynamic priority scheduling , distributed computing , fixed priority pre emptive scheduling , symmetric multiprocessor system , fair share scheduling , greedy algorithm , rate monotonic scheduling , schedule , algorithm , mathematical optimization , operating system , mathematics
Summary In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.