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22.04.2017 | Ausgabe 5/2017

Real-Time Systems 5/2017

Attacking the one-out-of-m multicore problem by combining hardware management with mixed-criticality provisioning

Zeitschrift:
Real-Time Systems > Ausgabe 5/2017
Autoren:
Namhoon Kim, Bryan C. Ward, Micaiah Chisholm, James H. Anderson, F. Donelson Smith
Wichtige Hinweise
Work supported by U.S. National Science Foundation Grants CNS 1115284, CNS 1218693, CPS 1239135, CNS 1409175, and CPS 1446631, U.S. Air Force Office of Scientific Research Grant FA9550-14-1-0161, U.S. Army Research Office Grant W911NF-14-1-0499, and a Grant from General Motors. The second author was also supported by an U.S. National Science Foundation graduate fellowship.

Abstract

The multicore revolution is having limited impact in safety-critical application domains. A key reason is the “one-out-of-m” problem: when validating real-time constraints on an m-core platform, excessive analysis pessimism can effectively negate the processing capacity of the additional \(m-1\) cores so that only “one core’s worth” of capacity is utilized even though m cores are available. Two approaches have been investigated previously to address this problem: mixed-criticality allocation techniques, which provision less-critical software components less pessimistically, and hardware-management techniques, which make the underlying platform itself more predictable. A better way forward may be to combine both approaches, but to show this, fundamentally new criticality-cognizant hardware-management tradeoffs must be explored. Such tradeoffs are investigated herein in the context of a new variant of a mixed-criticality framework, called \(\textsf {MC}^\textsf {2} \), that supports configurable criticality-based hardware management. This framework allows specific DRAM memory banks and areas of the last-level cache (LLC) to be allocated to certain groups of tasks. A linear-programming-based optimization framework is presented for sizing such LLC areas, subject to conditions for ensuring \(\textsf {MC}^\textsf {2} \) schedulability. The effectiveness of the overall framework in resolving hardware-management and scheduling tradeoffs is investigated in the context of a large-scale overhead-aware schedulability study. This study was guided by extensive trace data obtained by executing benchmark programs on the new variant of \(\textsf {MC}^\textsf {2} \) presented herein. This study shows that mixed-criticality allocation and hardware-management techniques can be much more effective when applied together instead of alone.

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