NDLI: Machine Learning Approach to Generate Pareto Front for List-scheduling Algorithms

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Machine Learning Approach to Generate Pareto Front for List-scheduling Algorithms

Content Provider	ACM Digital Library
Author	Aung, Khin Mi Mi Pham, Nam Khanh Kumar, Akash
Abstract	List Scheduling is one of the most widely used techniques for scheduling due to its simplicity and efficiency. In traditional list-based schedulers, a cost/priority function is used to compute the priority of tasks/jobs and put them in an ordered list. The cost function has been becoming more and more complex to cover increasing number of constraints in the system design. However, most of the existing list-based schedulers implement a static priority function that usually provides only one schedule for each task graph input. Therefore, they may not be able to satisfy the desire of system designers, who want to examine the trade-off between a number of design requirements (performance, power, energy, reliability .). To address this problem, we propose a framework to utilize the Genetic Algorithm (GA) for exploring the design space and obtaining Pareto-optimal design points. Furthermore, multiple regression techniques are used to build predictive models for the Pareto fronts to limit the execution time of GA. The models are built using training task graph datasets and applied on incoming task graphs. The Pareto fronts for incoming task graphs are produced in time 2 orders of magnitude faster than the traditional GA, with only 4% degradation in the quality.
Starting Page	127
Ending Page	134
Page Count	8
File Format	PDF
ISBN	9781450343206
DOI	10.1145/2906363.2906380
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	List-scheduling Machine learning Design space exploration
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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