ARCHITECTURE-AWARE MAPPING AND SCHEDULING OF MIXED-CRITICALITY APPLICATIONS ON MULTI-CORE PLATFORMS

The desire to have enhanced and increased feature sets in embedded applications has contributed to a significant increase in the computational demands of such systems over the years. To support such demand and yet maintain reasonable power/energy budgets, the industry has begun a shift to multi-core architectures even in the embedded systems domain. Embedded real-time applications such as Avionics and Automotive systems are no exception to this trend. Such systems have strict certification requirements of subsets of their functionality, which result in strict temporal constraints on those subsets, while other subsets may have less strict requirements. Migrating such {\em mixed criticality} systems from single-core to multi-core platforms is challenging because application/component isolation and freedom from interference among them must be guaranteed. Safe and efficient, architecture-aware mapping and scheduling of system components (e.g., partitions, tasks, etc. as relevant to a particular domain) on the multiple cores is at the center of any scheme to migrate such systems from single-core to multi-core platforms. In this dissertation, we propose, develop and evaluate a unified framework to automate the mapping and scheduling process with the consideration of several architectural and application level requirements/constraints (e.g., communication and cache conflicts among system components, constraints prohibiting the allocation of certain system components on the same core, etc.)

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Campus Only Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year dc:date.available

2018

Author dc:creator

• Vasu, Aishwarya

Contributors dc:contributor

• Kagaris, Dimitrios
• Ramaprasad, Harini

• MIXED-CRITICALITY
• MULTI-CORE
• SCHEDULING

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/1523

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-2527