Adaptive software includes the necessary mechanisms to modify its behavior in order to optimize resources utilization or for software evolution issues. Dynamic adaptation is specially important on environments which resources vary over time, and which applications manage requests from users with different preferences (e.g. language). Building such adaptive software requires applications designers to include the necessary adaptation mechanisms. Moreover, when dealing with distributed applications, designers may want to distribute the adaptation mechanisms themselves. In this paper, we propose a model for dynamic adaptation that clearly separates adaptation from business logic and that can be customized by applications designers in order to satisfy adaptation needs. The model is based on a set of mandatory functionalities that manage basic adaptation operations and an optional functionality that can be used to manage adaptation mechanisms distribution. We have used our adaptation model to build an adaptive data consistency service of an ambient assistive living application.

Since the concept of grid computing has emerged; one of the major challenges consists in making applications able to benefit efficiently from the huge amount of resources with out burdening the developer. Observing that grid computing environments are dynamic, adaptability has been proposed as a way to tackle this problem. Adaptability is in deed the ability of applications to modify themselves according to their actual environment. It guarantees that they continuously behave in the best possible way despite environmental changes. In the context of grid computing, environmental changes concern mostly processor and network availability because of resource sharing between applications, administrative tasks and failures

As grid architectures provide execution environments that are distributed, parallel and dynamic, applications require to be not only parallel and distributed, but also able to adapt themselves to their execution environment. This article presents a model for designing self-adaptable parallel components that can be assembled to build applications for grid. This model includes the definition of a consistency criterion for the dynamic adaptation of SPMD components. We propose a solution to implement this criterion. It has been evaluated on both synthetic and real codes to exhibit the behavior of the several proposed strategies

Mobile computing is a domain in great expansion. Wireless networks and Portable Information Appliances (PIAs) are developing very rapidly. More and more mobile users would like to perform their multimedia applications with the same facility as on their desktop station. Use of such applications in a mobile environment raises new challenges. These applications are interactive and extremely costly in system and network resources, whereas PIA resources are poor and wireless networks offer a very variable quality of connection. The authors propose an adaptive and dynamic distribution of applications on the local environment to overcome the poorness of available resources on PIAs, and to reduce and regulate variability effects.

A programming model that is widely approved today for large applications is parallel programming with shared variables. We propose an implementation of shared arrays on distributed memory architectures: it provides the user with an uniform addressing scheme while being efficient thanks to a logical paging technique and optimized communication mechanisms.

To efficiently program massively parallel systems we propose to use a form of parallelism known as data parallelism along with a SPMD programming model. We describe how a sequential Object Oriented Language (OOL) can embed data parallelism in a clean and elegant fashion—without language extensions— to exploit the potential power of massively parallel systems. To illustrate our ideas, we present EPEE (our OOL Parallel Execution Environment) and an application of this approach to a well known parallel paradigm. After some remarks on our implementation, we show experimental performance results and draw some conclusions on the generality of this approach.

The goal of the Pandore system is to allow the execution of parallel algorithms on DMPCs (Distributed Memory Parallel Computers) without having to take into account the low-level characteristics of the target distributed computer to program the algorithm. No explicit process definition and interprocess communications are needed. Parallelization is achieved through logical data organization. The Pandore system provides the user with a mean to specify data partitioning and data distribution over a domain of virtual processors for each parallel step of his algorithm.