Search

Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies. This paper proposes an approach to leverage genetic algorithms in the decision-making process of an autonomic system. This approach enables a system to dynamically evolve target reconfigurations at run time that balance tradeoffs between functional and non-functional requirements in response to changing requirements and environmental conditions. A key feature of this approach is incorporating system and environmental monitoring information into the genetic algorithm such that specific changes in the environment automatically drive the evolutionary process towards new viable solutions. We have applied this genetic-algorithm based approach to the dynamic reconfiguration of a collection of remote data mirrors, demonstrating an effective decision-making method for diffusing data and minimizing operational costs while maximizing data reliability and network performance, even in the presence of link failures. [ABSTRACT FROM AUTHOR]

We present a study in the evolution of temporal behavior, specifically synchronization and desynchronization, through digital evolution and group selection. In digital evolution, a population of self-replicating computer programs exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. Group selection links the survival of the individual to the survival of its group, thus encouraging cooperation. Previous approaches to engineering synchronization and desynchronization algorithms have taken inspiration from nature: In the well-known firefly model, the only form of communication between agents is in the form of flash messages among neighbors. Here we demonstrate that populations of digital organisms, provided with a similar mechanism and minimal information about their environment, are capable of evolving algorithms for synchronization and desynchronization, and that the evolved behaviors are robust to message loss. We further describe how the evolved behavior for synchronization mimics that of the well-known Ermentrout model for firefly synchronization in biology. In addition to discovering self-organizing behaviors for distributed computing systems, this result indicates that digital evolution may be used to further our understanding of synchronization in biology. [ABSTRACT FROM AUTHOR]

The article discusses digital evolution in computers. Digital evolution is a form of evolutionary computation where self-replicating computer programs evolve inside a user-defined computational environment. Replicating organisms receive limited resources and instruction-level mutations which, using natural selection, gives the population a variety where complex behaviors, such as problem solving strategies, can evolve. This environment uses the Avida digital evolution platform where organisms are made up of a circular list of instructions, a genome and a virtual CPU. Several cells containing no more than one organism at a time comprise the Avida environment and when the organism replicates the system places the offspring in a random cell, killing any other inhabitant. INSET: Evolution in a Computer.

Compositional adaptation enables software to modify its structure and behavior dynamically in response to changes in its execution environment. A review of current technology compares how, when, and where recomposition occurs. Moreover, this article will also offer a review of the supporting technologies, proposed solutions, and areas that require further study. Many of the mechanisms for compositional adaptation are available now, and we expect their use to increase as programmers become more familiar with adaptive software technologies and society comes to expect computer systems to manage themselves. Compositional adaptation is powerful, but without appropriate tools to automatically generate and verify code, its use can negatively impact, rather than improve, system integrity and security. The computer science community must build development technologies and tools, well grounded in rigorous software engineering, to support compositional adaptation. This foundation will raise next generation of computing to new levels of flexibility, autonomy, and maintainability without sacrificing assurance and security.