Open Access

ARTICLE

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

Sunhwa Annie Nam¹, Kyungwoon Cho², Hyokyung Bahn^3,*

1 Department of Computer Science and Engineering, Ewha University, Seoul, 03760, Korea
2 Embedded Software Research Center, Ewha University, Seoul, 03760, Korea
3 Department of Computer Science and Engineering, Ewha University, Seoul, 03760, Korea

* Corresponding Author: Hyokyung Bahn. Email:

Computers, Materials & Continua 2023, 74(3), 6823-6833. https://doi.org/10.32604/cmc.2023.035481

Received 23 August 2022; Accepted 04 November 2022; Issue published 28 December 2022

Abstract

AI (Artificial Intelligence) workloads are proliferating in modern real-time systems. As the tasks of AI workloads fluctuate over time, resource planning policies used for traditional fixed real-time tasks should be re-examined. In particular, it is difficult to immediately handle changes in real-time tasks without violating the deadline constraints. To cope with this situation, this paper analyzes the task situations of AI workloads and finds the following two observations. First, resource planning for AI workloads is a complicated search problem that requires much time for optimization. Second, although the task set of an AI workload may change over time, the possible combinations of the task sets are known in advance. Based on these observations, this paper proposes a new resource planning scheme for AI workloads that supports the re-planning of resources. Instead of generating resource plans on the fly, the proposed scheme pre-determines resource plans for various combinations of tasks. Thus, in any case, the workload is immediately executed according to the resource plan maintained. Specifically, the proposed scheme maintains an optimized CPU (Central Processing Unit) and memory resource plan using genetic algorithms and applies it as soon as the workload changes. The proposed scheme is implemented in the open-source simulator SimRTS for the validation of its effectiveness. Simulation experiments show that the proposed scheme reduces the energy consumption of CPU and memory by 45.5% on average without deadline misses.

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Keywords

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