By Topic

Load Balancing Aware Real-Time Task Partitioning in Multicore Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jaeyeon Kang ; Comput. Sci. Lab., Samsung R&D Center, San Jose, CA, USA ; Waddington, D.G.

Real-time applications of future IT will continue to drive the demand for performance scaling in devices ranging from sensors to servers. Parallel processing in the form of mul-ticore and manycore architectures will also continue to be the principal route to unleashing next generation performance capabilities. To fully exploit multicore processors, real-time applications are expected to provide a large degree of parallel-ism, where real-time tasks can utilize multiple cores at the same time. Guaranteeing real-time performance, while making efficient use of multicore resources, requires a scheduling method that offers both high schedulability and effective load balancing. Many existing real-time scheduling methods for multicore systems focus on schedulability or load balancing, but not both -- each coming at the expense of the other. In this work we develop an efficient scheduling algorithm that not only guarantees real-time performance but also demonstrates effective distribution of tasks across cores. Experimental re-sults show that our method significantly outperforms state-of-the-art approaches in terms of load balancing while still providing good schedulability. We also show the benefits with respect to energy reduction that result from balanced load.

Published in:

Embedded and Real-Time Computing Systems and Applications (RTCSA), 2012 IEEE 18th International Conference on

Date of Conference:

19-22 Aug. 2012