NDLI: Memory-processor co-scheduling in fixed priority systems

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Memory-processor co-scheduling in fixed priority systems

Content Provider	ACM Digital Library
Author	Melani, Alessandra Buttazzo, Giorgio Bonifaci, Vincenzo Marchetti-Spaccamela, Alberto Bertogna, Marko
Abstract	A major obstacle towards the adoption of multi-core platforms for real-time systems is given by the difficulties in characterizing the interference due to memory contention. The simple fact that multiple cores may simultaneously access shared memory and communication resources introduces a significant pessimism in the timing and schedulability analysis. To counter this problem, predictable execution models have been proposed splitting task executions into two consecutive phases: a memory phase in which the required instruction and data are pre-fetched to local memory (M-phase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but it also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. In this paper, we take a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the M/C (memory-computation) model. We provide a theoretical framework that aims at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on a single-core systems. We identify a tight critical instant for M/C tasks scheduled with fixed priority, providing an exact response-time analysis with pseudo-polynomial complexity. We show in our experiments that a significant schedulability improvement may be obtained with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems.
Starting Page	87
Ending Page	96
Page Count	10
File Format	PDF
ISBN	9781450335911
DOI	10.1145/2834848.2834854
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-04
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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