Analysis of SRPT scheduling: investigating unfairness
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Proceedings of the 2003 international conference on Compilers, architecture and synthesis for embedded systems
Online Scheduling for Block-Partitioned Reconfigurable Devices
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Operating Systems for Reconfigurable Embedded Platforms: Online Scheduling of Real-Time Tasks
IEEE Transactions on Computers
Task Scheduling in a Finite-Resource, Reconfigurable Hardware/Software Codesign Environment
INFORMS Journal on Computing
Online Hardware Task Scheduling and Placement Algorithm on Partially Reconfigurable Devices
ARC '08 Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
ARC'10 Proceedings of the 6th international conference on Reconfigurable Computing: architectures, Tools and Applications
International Journal of Applied Evolutionary Computation
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Existing operating systems can manage the execution of software tasks efficiently, however the manipulation of hardware tasks is very limited. In the research on the design and implementation of an embedded operating system that manages both software and hardware tasks in the same framework, two major issues are the dynamic scheduling and the dynamic placement of hardware tasks into a reconfigurable logic space in an SoC . The distinguishing criteria for good dynamic scheduling and placement methods include the total schedule length and the amount of fragmentation incurred while tasks are dynamically placed and replaced. Existing methods either do not take fragmentation into consideration or postpone the consideration of fragmentation to a later stage of space allocation. In our method, we try to reduce fragmentation during placement itself. The advantage of such an approach is that not only the reconfigurable space is utilized more efficiently, but the total schedule length is also reduced, that is, hardware tasks complete faster. Experimental results on large random tasks sets have shown that the proposed improvement is as much as 23.3% in total fragmentation and 2.0% in total schedule time.