This survey of the workshop series Consistency Problems in UML-based Software Development aims to help readers to find the guidelines of the papers. First, general considerations about consistency and related problems are discussed. Next, the approaches proposed in the workshop papers to handle the problems are categorized and summarized. The last section includes extended abstracts of the papers from the current workshop
The reactions between 6 strains of mesophilic lactic streptococci and their respective phages were studied quantitatively. Of 30 nonhomologous reactions, the bacteria were fully sensitive in 4 and restricted the phages in 23. A mathematical model was developed that was used to identify at least 4 and probably 5 modification restriction (M/R) systems of which up to 3 were found in the same strain. The model was based on 24 measured values and correctly predicted the values of 5 others. One of the 3 negative reactions was shown to be due to a restriction beyond the limit of detection, a second was due to lysogeny or lack of adsorption, but was shown to have the predicted value when the homologous phage was modified on the host of the challenging phage. In the last reaction a measurable restriction was predicted, but could not be proven by means of a modified phage. These results suggest M/R to be one of the main defenses of the lactic streptococci against their phages. They explain why host range is not a useful criterion in the classification of phages and suggest a rational approach to the definition of a starter rotation.
In any system, applications compete for a limited amount of resources. As long as there are enough resources, resource sharing based on a best effort policy is satisfactory. When resources become scarce, the system gives rise to uncontrollable degradations. From a global view of the system and according to the degrees of freedom of applications, Quality of Service (QoS) managers aim to adapt application behavior to deal with overload effects. This paper proposes a middleware for autonomic QoS management of a system in a dynamic environment. It associates a reinforcement learning technique with a control mechanism to improve and adapt the QoS management policy in an execution context that changes unexpectedly. The simulation of the QoS management of a set of heterogeneous applications illustrates our results.
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