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Section: Scientific Foundations


Due to the dynamic nature of large-scale distributed systems in general, and the Grid in particular, it is very hard to design an application that fits well in any configuration. Moreover, constraints such as the number of available processors, their respective load, the available memory and network bandwidth are not static. For these reasons, it is highly desirable that an application could take into account this dynamic context in order to get as much performance as possible from the computing environment.

Dynamic adaptation of a program is the modification of its behavior according to changes of the environment. This adaptivity can be achieved in many different ways, ranging from a simple modification of some parameters, to the total replacement of the running code. In order to achieve adaptivity, a program needs to be able to get information about the environment state, to make a decision according to some optimization rules, and to modify or replace some parts of its code.

Adaptivity has been implemented by designing ad hoc applications that take into account the specificities of the target environment. For example, this was done for the Web applications access protocol on mobile networks by defining the WAP protocol  [78] . A more general way is to provide mechanisms enabling dynamic self-adaptivity by changing the program's behavior. In most cases, this has been achieved by embedding the adaptation mechanism within the application code. For example, the AdOC compression algorithm  [89] includes such a mechanism to dynamically change the compression level according to the available resources.

However, it is desirable to separate the adaptation engine from the application code, in order to make the code easier to maintain, and to easily change or improve the adaptation policy. This was done for wireless and mobile environments by implementing a framework  [85] that provides generic mechanisms for the adaptation process, and for the definition of the adaptation rules.


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