MCFlow: A Real-Time Multi-core Aware Middleware for Dependent Task Graphs

Abstract

Abstract-Driven by the evolution of modern computer architectures from uni-processor to multi-core platforms, there is an increasing need to provide light-weight, efficient, and predictable support for fine-grained parallel and distributed execution of soft real-time tasks with end-to-end timing constraints, modeled as directed acyclic graphs whose edges capture dependences among their subtasks. At the same time, there is a need to support state of the art programming models such as distributed components, whose ability to encapsulate functionality and allow context-specific optimizations is essential to manage the increasing complexity of modern distributed real-time and embedded systems and systems-of-systems.Real-time distributed middleware such as RT-CORBA has not kept pace with these developments, and a new generation of middleware is needed that can map these dependent subtask graphs onto distributed hosts with multi-core architectures, efficiently and within a simple, lightweight, and intuitive component programming model. To overcome these limitations, we have designed and implemented MCFlow, a novel distributed real-time component middleware for dependent subtask graphs running on multi-core platforms.MCFlow provides three new contributions to the state of the art in real-time component middleware: (1) a very lightweight component model that facilitates system integration and deployment through automatic code generation at compiletime from a deployment plan specification; (2) transparent optimization of inter-component communication; and (3) the use of interface polymorphism to separate functional correctness from data copying and other performance constraints so that they can be configured and enforced independently but in a type-safe manner. Empirical evaluations of our approach in comparison to the widely used TAO real-time middleware show that MCFlow performs comparably to TAO when only one core is used and outperforms TAO when multiple cores are involved.

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