Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85847
Title: MC-Fluid: Multi-core Fluid-based Mixed-Criticality Scheduling
Authors: Lee, Jaewoo
Ramanathan, Saravanan
Phan, Kieu-My
Easwaran, Arvind
Shin, Insik
Lee, Insup
Keywords: Real-time and Embedded Systems
Scheduling
Issue Date: 2017
Source: Lee, J., Ramanathan, S., Phan, K.-M., Easwaran, A., Shin, I., & Lee, I. (2017). MC-Fluid: Multi-core Fluid-based Mixed-Criticality Scheduling. IEEE Transactions on Computers, in press.
Series/Report no.: IEEE Transactions on Computers
Abstract: Owing to growing complexity and scale, safetycritical real-time systems are generally designed using the concept of mixed-criticality, wherein applications with different criticality or importance levels are hosted on the same hardware platform. To guarantee non-interference between these applications, the hardware resources, in particular the processor, are statically partitioned among them. To overcome the inefficiencies in resource utilization of such a static scheme, the concept of mixedcriticality real-time scheduling has emerged as a promising solution. Although there are several studies on such scheduling strategies for uniprocessor platforms, the problem of efficient scheduling for the multiprocessor case has largely remained open. In this work, we design a fluid-model based mixed-criticality scheduling algorithm for multiprocessors, in which multiple tasks are allowed to execute on the same processor simultaneously. We derive an exact schedulability test for this algorithm, and also present an optimal strategy for assigning the fractional execution rates to tasks. Since fluid-model based scheduling is not implementable on real hardware, we also present a transformation algorithm from fluid-schedule to a non-fluid one. We also show through experimental evaluation that the designed algorithms outperform existing scheduling algorithms in terms of their ability to schedule a variety of task systems.
URI: https://hdl.handle.net/10356/85847
http://hdl.handle.net/10220/44062
ISSN: 0018-9340
DOI: 10.1109/TC.2017.2759765
Schools: School of Computer Science and Engineering 
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TC.2017.2759765].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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