Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/13149
Title: Priority Based Scheduler for Asymmetric Multi-core Edge Computing
Authors: Hada, Rupendra Pratap Singh
Shrivastava, Abhishek
Keywords: Asymmetric multi-core processors;completely fair scheduler;edge computing;scheduling
Issue Date: 2023
Publisher: River Publishers
Citation: Hada, R. P. S., & Srivastava, A. (2024). A Novel Priority Based Scheduler for Asymmetric Multi-core Edge Computing. Springer Science and Business Media Deutschland GmbH
Scopus. https://doi.org/10.1007/978-3-031-50385-6_1
Abstract: Edge computing technology has gained popularity due to its ability to process data near the source or collection device, benefiting from low bandwidth utilization and enhanced security. Edge devices are typically equipped with multiple devices that employ asymmetric multi-cores for efficient data processing. To ensure optimal performance, it is crucial to carefully assign tasks to the appropriate cores in asymmetric multi-core processors. However, the current Linux scheduler needs to consider the capabilities of individual cores when assigning tasks. Consequently, high-priority tasks may be assigned to energy-efficient cores, while low-priority tasks end up on high-performance cores. This sub-optimal task assignment negatively impacts the overall system performance. To address this issue, a new algorithm has been proposed. This algorithm considers both the core’s capabilities and the task’s priority. However, due to the asymmetric nature of the cores, prior knowledge of each core’s speed is necessary. The algorithm fetches the priorities of the tasks and classifies them into high, medium, and low-priority categories. High-priority tasks are scheduled on high-performance cores, while medium and low-priority tasks are allocated to energy-efficient cores. The proposed algorithm demonstrates superior performance for high-priority tasks compared to the existing Linux task scheduling algorithm. It significantly improves task scheduling time by up to 16%, thereby enhancing the system’s overall efficiency. © 2023 River Publishers.
URI: https://doi.org/10.13052/jwe1540-9589.2262
https://dspace.iiti.ac.in/handle/123456789/13149
ISSN: 1540-9589
Type of Material: Journal Article
Appears in Collections:Department of Computer Science and Engineering

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