Runtime Resource Management with Multiple-Step-Ahead Workload Prediction

Modern embedded platforms need sophisticated resource managers to utilize their heterogeneous computational resources efficiently. Furthermore, such platforms are subject to fluctuating workloads that are unforeseeable at design time. Predicting the incoming workload could enhance the efficiency of...

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Bibliographic Details
Published inACM transactions on embedded computing systems Vol. 22; no. 4; pp. 1 - 34
Main Authors Niknafs, Mina, Eles, Petru, Peng, Zebo
Format Journal Article
LanguageEnglish
Published New York, NY ACM 24.07.2023
Subjects
Computer systems organization
Embedded systems
Hardware
Heterogeneous architecture
Integrated circuits
multiple-step-ahead prediction
Power and energy
Power estimation and optimization
resource management
resource management
Heterogeneous architecture
multiple-step-ahead prediction
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Summary:Modern embedded platforms need sophisticated resource managers to utilize their heterogeneous computational resources efficiently. Furthermore, such platforms are subject to fluctuating workloads that are unforeseeable at design time. Predicting the incoming workload could enhance the efficiency of resource management in this situation. But is that the case? And, if so, how substantial is this improvement? Does multiple-step-ahead prediction of the workload contribute to this improvement? How precise must the prediction be to improve decisions rather than cause harm? By proposing a prediction-based resource manager that aims at meeting task deadlines while minimizing energy usage, and by conducting extensive tests, we attempt to provide answers to the aforementioned questions.
ISSN:1539-9087
1558-3465
1558-3465
DOI:10.1145/3605213