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Spirits: Using Virtualization and Pervasiveness to Manage Mobile Robot Software Systems

  • Himanshu Raj
  • Balasubramanian Seshasayee
  • Keith J. O’Hara
  • Ripal Nathuji
  • Karsten Schwan
  • Tucker Balch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3996)

Abstract

Management capabilities comprise a key component of any autonomous distributed system. In this work our focus is on mobile systems like teams of robots exploring and operating in some physical environment. Here, basic management goals are to adapt application behavior to prevent or mitigate reductions in the application’s quality of service. This paper presents the spirits system-level mechanisms supporting (1) behavior persistence – the ability to maintain some desirable behavior learned through online adaptation – and (2) behavior propagation – the ability to propagate a learned behavior across different physical components. With spirits, software management is enriched with a low-level mechanism, termed a spirit cache, which permits a mobile entity to cache its current state and code to realize behavior persistence. Next, a cached spirit can be acquired by a different physical component and then used, thereby propagating it. By using system-level virtualization techniques to realize spirit caching and propagation, both can be performed without the need to make any changes to application code, without requiring middleware-level support, and without changes to operating system kernels or utilities. Furthermore, any number of spirits can exist in a robot with system-level isolation guarantees. Experimental results presented in this paper highlight the types of overheads for spirit exchanges experienced on typical next generation virtualizable embedded machines, and indicate optimizations to be considered in future research.

Keywords

Sensor Network Mobile Robot Virtual Machine Logical Device Migration Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Himanshu Raj
    • 1
  • Balasubramanian Seshasayee
    • 1
  • Keith J. O’Hara
    • 1
  • Ripal Nathuji
    • 1
  • Karsten Schwan
    • 1
  • Tucker Balch
    • 1
  1. 1.College of Computing, Georgia Institute of TechnologyAtlantaUSA

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