research-article

Task Splitting and Load Balancing of Dynamic Real-Time Workloads for Semi-Partitioned EDF

Authors:

Alessandro Biondi,

Giorgio ButtazzoAuthors Info & Claims

IEEE Transactions on Computers, Volume 70, Issue 12

Pages 2168 - 2181

https://doi.org/10.1109/TC.2020.3038286

Published: 01 December 2021 Publication History

Abstract

Many real-time software systems, such as those commonly found in the context of multimedia, cloud computing, robotics, and real-time databases, are characterized by a dynamic workload, where applications can join and leave the system at runtime. Global schedulers can transparently support dynamic workload without requiring any off-line task-allocation phase, thus providing advantages to the system designer. Nevertheless, such schedulers exhibit poor worst-case performance when compared to semi-partitioned schedulers, which instead can achieve near-optimal schedulability performance when used in conjunction with smart task splitting and partitioning techniques, and they are also lighter in terms of run-time overhead. This article proposes an approach to efficiently schedule dynamic real-time workloads on multiprocessor systems by means of semi-partitioned scheduling. A linear-time approximation scheme for the C=D splitting algorithm under partitioned EDF scheduling is proposed. Then, a load-balancing algorithm is presented to admit new real-time workloads with a limited number of re-allocations. The article finally reports on a large-scale experimental study showing that (i) the linear-time approximation is characterized by a very limited utilization loss compared with the corresponding exact approach (that has a much higher complexity), and that (ii) the whole approach allows achieving considerable improvements with respect to global and partitioned EDF scheduling.

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cover image IEEE Transactions on Computers

IEEE Transactions on Computers Volume 70, Issue 12

Dec. 2021

236 pages

ISSN:0018-9340

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0018-9340 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 December 2021

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Cited By

Liu WGeng JZhu ZZhao YJi CLi CLian ZZhou X(2024)Ace-Sniper: Cloud–Edge Collaborative Scheduling Framework With DNN Inference Latency Modeling on Heterogeneous DevicesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.331438843:2(534-547)Online publication date: 1-Feb-2024
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Li NSong XLi KJiang RLi J(2023)Multiagent Federated Deep-Reinforcement-Learning-Enabled Resource Allocation for an Air–Ground-Integrated Internet of Vehicles NetworkIEEE Internet Computing10.1109/MIC.2023.330743127:5(15-23)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/MIC.2023.3307431
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https://dl.acm.org/doi/10.1145/3489517.3530474
Cucinotta TAbeni L(2021)Migrating Constant Bandwidth Servers on Multi-CoresProceedings of the 29th International Conference on Real-Time Networks and Systems10.1145/3453417.3453441(155-164)Online publication date: 7-Apr-2021
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