Reliability-driven dynamic binding via feedback control

We are concerned with software that can self-adapt to satisfy certain reliability requirements, in spite of adverse changes affecting the environment in which it is embedded. Self-adapting software architectures are heavily based on dynamic binding. The bindings among components are dynamically set as the conditions that require a self-adaptation are discovered during the system's lifetime. By adopting a suitable modeling approach, the dynamic binding problem can be formulated as a discrete-time feedback control problem, and solved with very simple techniques based on linear blocks. Doing so, reliability objectives are in turn formulated as set point tracking ones in the presence of disturbances, and attained without the need for optimization. At design time, the proposed formulation has the advantage of naturally providing system sizing clues, while at operation time, the inherent computational simplicity of the obtained controllers results in a low overhead. Finally, the formulation allows for a rigorous assessment of the achieved results in both nominal and off-design conditions for any desired operation point.

[1]  Danilo Ardagna,et al.  Adaptive Service Composition in Flexible Processes , 2007, IEEE Transactions on Software Engineering.

[2]  Gero Mühl,et al.  QoS-Aware Composition of Web Services: An Evaluation of Selection Algorithms , 2005, OTM Conferences.

[3]  Andrew Chi-Chih Yao,et al.  The complexity of nonuniform random number generation , 1976 .

[4]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[5]  Alberto Leva,et al.  PI/PID autotuning with contextual model parametrisation , 2010 .

[6]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[7]  Xindong Wu,et al.  Optimizing Service Systems Based on Application-Level QoS , 2009, IEEE Transactions on Services Computing.

[8]  M. Araki,et al.  Multivariable multirate sampled-data systems: State-space description, transfer characteristics, and Nyquist criterion , 1986 .

[9]  Yixin Diao,et al.  Comparative studies of load balancing with control and optimization techniques , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  Carlo Ghezzi,et al.  QoS Driven Dynamic Binding in-the-many , 2010, QoSA.

[11]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[12]  Maria Luisa Villani,et al.  An approach for QoS-aware service composition based on genetic algorithms , 2005, GECCO '05.

[13]  Valérie Issarny,et al.  QoS-Aware Service Composition in Dynamic Service Oriented Environments , 2009, Middleware.

[14]  Tore Hägglund,et al.  Advanced PID Control , 2005 .

[15]  Riccardo Scattolini,et al.  Self-tuning PI-PID regulators for stable systems with varying delay , 1994, Autom..

[16]  Raffaela Mirandola,et al.  Per-flow optimal service selection for Web services based processes , 2010, J. Syst. Softw..

[17]  Joseph F. Traub,et al.  Algorithms and Complexity: New Directions and Recent Results , 1976 .

[18]  Alberto Leva,et al.  PID autotuning algorithm based on relay feedback , 1993 .

[19]  Thomas Risse,et al.  Combining global optimization with local selection for efficient QoS-aware service composition , 2009, WWW '09.

[20]  Carlo Ghezzi,et al.  Self-adaptive software meets control theory: A preliminary approach supporting reliability requirements , 2011, 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011).

[21]  Rod Johnson,et al.  Professional Java Development with the Spring Framework , 2005 .

[22]  Messaoud Benidir,et al.  On the root distribution of general polynomials with respect to the unit circle , 1996, Signal Process..