A context-aware reflective middleware framework for distributed real-time and embedded systems

  • Authors:
  • Shengpu Liu;Liang Cheng

  • Affiliations:
  • Lehigh University 19 Memorial Drive West, Bethlehem, PA 18015, United States;Lehigh University 19 Memorial Drive West, Bethlehem, PA 18015, United States

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2011

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Abstract

Context-aware reflective middleware (CARM), which supports application reconfiguration, has been an appealing technique for building distributed real-time and embedded (DRE) systems as it can adapt their behaviors to changing environments at run time. However, existing CARM frameworks impose dependence restrictions and reconfiguration overhead, which makes the reconfiguration time of these frameworks too long (normally in the range of seconds or more) to satisfy the stringent real-time requirements of DRE systems. To improve the reconfiguration efficiency for supporting DRE systems, we have designed a new CARM framework - MARCHES (Middleware for Adaptive Robust Collaborations across Heterogeneous Environments and Systems), which offers an original structure of multiple component chains to reduce local behavior change time and a novel synchronization protocol using active messages to reduce distributed behavior synchronization time. MARCHES uses a layered architecture and provides both component-level and system-level reflection to incorporate standard components, a hierarchical event notification model to evaluate contexts, and a lightweight XML-based script language to describe and manage adaptation policies. The MARCHES framework and supported applications have been implemented on PC and PDA platforms. Based on a novel theoretical model, we have analyzed the reconfiguration efficiency of MARCHES and compared it with those of peer CARM frameworks: MobiPADS and CARISMA. Quantitative empirical results show that the reconfiguration time of MARCHES is reduced from seconds to hundreds of microseconds. Evaluations demonstrate that MARCHES is robust, scalable and generates a small memory footprint, which makes it suitable for supporting DRE systems.