Open Access

Design and Performance Evaluation of an Adaptive Resource Management Framework for Distributed Real-Time and Embedded Systems

  • Nishanth Shankaran1Email author,
  • Nilabja Roy1,
  • Douglas C Schmidt1,
  • Xenofon D Koutsoukos1,
  • Yingming Chen2 and
  • Chenyang Lu2
EURASIP Journal on Embedded Systems20082008:250895

DOI: 10.1155/2008/250895

Received: 8 February 2007

Accepted: 2 January 2008

Published: 8 January 2008


Achieving end-to-end quality of service (QoS) in distributed real-time embedded (DRE) systems require QoS support and enforcement from their underlying operating platforms that integrates many real-time capabilities, such as QoS-enabled network protocols, real-time operating system scheduling mechanisms and policies, and real-time middleware services. As standards-based quality of service (QoS) enabled component middleware automates integration and configuration activities, it is increasingly being used as a platform for developing open DRE systems that execute in environments where operational conditions, input workload, and resource availability cannot be characterized accurately a priori. Although QoS-enabled component middleware offers many desirable features, however, it historically lacked the ability to allocate resources efficiently and enable the system to adapt to fluctuations in input workload, resource availability, and operating conditions. This paper presents three contributions to research on adaptive resource management for component-based open DRE systems. First, we describe the structure and functionality of the resource allocation and control engine (RACE), which is an open-source adaptive resource management framework built atop standards-based QoS-enabled component middleware. Second, we demonstrate and evaluate the effectiveness of RACE in the context of a representative open DRE system: NASA's magnetospheric multiscale mission system. Third, we present an empirical evaluation of RACE's scalability as the number of nodes and applications in a DRE system grows. Our results show that RACE is a scalable adaptive resource management framework and yields a predictable and high-performance system, even in the face of changing operational conditions and input workload.

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

The Electrical Engineering and Computer Science Department, Vanderbilt University
Department of Computer Science and Engineering, Washington University


© Nishanth Shankaran et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.