Peer-based automatic configuration of pervasive applications

Pervasive computing envisions seamless support for user tasks through cooperating devices that are present in an environment. Fluctuating availability of devices, induced by mobility and failures, requires mechanisms and algorithms that allow applications to adapt to changing environmental conditions without user intervention. To ease the development of adaptive applications, we have proposed the peer-based component system PCOM. This system provides fundamental mechanisms to support the automated composition of applications at runtime. In this paper, we discuss the requirements on peer-based automatic configuration of pervasive applications and present an approach based on distributed constraint satisfaction. The resulting algorithm configures applications in the presence of strictly limited resources. To show the feasibility of the approach, we have integrated the algorithm into PCOM and provide an evaluation based on simulation and measurements.

[1]  Jean-Marc Jézéquel,et al.  Making Components Contract Aware , 1999, Computer.

[2]  Edsger W. Dijkstra,et al.  Termination Detection for Diffusing Computations , 1980, Inf. Process. Lett..

[3]  Armando Fox,et al.  Portability, extensibility and robustness in iROS , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[4]  David Garlan,et al.  Project Aura: Toward Distraction-Free Pervasive Computing , 2002, IEEE Pervasive Comput..

[5]  A. B. Baker.  Intelligent backtracking on constraint satisfaction problems: experimental and theoretical results , 1995 .

[6]  Stephen A. Cook,et al.  The complexity of theorem-proving procedures , 1971, STOC.

[7]  Friedemann Mattern,et al.  Global Quiescence Detection Based on Credit Distribution and Recovery , 1989, Inf. Process. Lett..

[8]  Gregor Schiele,et al.  PCOM - a component system for pervasive computing , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[9]  Dennis Heimbigner,et al.  Deployment and dynamic reconfiguration planning for distributed software systems , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[10]  Umar Saif,et al.  A Case for Goal-oriented Programming Semantics , 2003 .

[11]  Klara Nahrstedt,et al.  QoS-assured service composition in managed service overlay networks , 2003, 23rd International Conference on Distributed Computing Systems, 2003. Proceedings..

[12]  Wei-Min Shen,et al.  A Dynamic Distributed Constraint Satisfaction Approach to Resource Allocation , 2001, CP.

[13]  Gregor Schiele,et al.  BASE - a micro-broker-based middleware for pervasive computing , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[14]  Katia Sycara,et al.  Multiagent coordination in tightly coupled task scheduling , 1997 .

[15]  Makoto Yokoo,et al.  Algorithms for Distributed Constraint Satisfaction: A Review , 2000, Autonomous Agents and Multi-Agent Systems.

[16]  Bhaskaran Raman.  An architecture for highly available wide-area service composition , 2003, Comput. Commun..

[17]  Klara Nahrstedt,et al.  A Middleware Infrastructure for Active Spaces , 2002, IEEE Pervasive Comput..

[18]  Robert Grimm,et al.  One.world: experiences with a pervasive computing architecture , 2004, IEEE Pervasive Computing.

[19]  Keith S. Decker,et al.  Coordinated hospital patient scheduling , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[20]  Klara Nahrstedt,et al.  QoS and Contention-Aware Multi-Resource Reservation , 2004, Cluster Computing.

[21]  Makoto Yokoo,et al.  The Distributed Constraint Satisfaction Problem: Formalization and Algorithms , 1998, IEEE Trans. Knowl. Data Eng..

[22]  Ten-Hwang Lai,et al.  An (N-1)-Resilient Algorithm for Distributed Termination Detection , 1995, IEEE Trans. Parallel Distributed Syst..