This report presents the architecture and the algorithms used in DIET (Distributed Interactive Engineering Toolbox), a hierarchical set of components to build Network Enabled Server applications in a Grid environment. This environment is built on top of different tools which are able to locate an appropriate server depending of the client's request, the data location (which can be anywhere on the system, because of previous computations) and the dynamic performance characteristics of the system. Some experiments are related at the end of this report, that exhibit the low cost of adding branches in the hierarchical tree of components and the performance increase induced.

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A management of multi-stacks fuel cell systems is proposed to extend systems useful life in a Prognostics and Health Management (PHM) framework. The problem consists in selecting at each time which fuel cell stacks have to run and which output power has to be chosen for each of them to satisfy a load demand as long as possible. Multi-stacks fuel cell system useful life depends not only on each stack useful life, but also on both the schedule and the operating conditions settings that define the contribution of each stack over time. As the impact of variable operating conditions on fuel cell lifetime is not well-known, a simplified representation of fuel cell behavior under wear and tear is used to estimate the available outputs over time and their associated Remaining Useful Lives (RUL). This health state prognostics model is configured to suit to Proton-Exchange Membrane Fuel Cells (PEMFC) specific characteristics. The proposed scheduling process makes use of an optimal approach based on a Mixed Integer Linear Program (MILP). Efficiency of the associated commitment strategy is assessed by comparison with basic intuitive strategies, considering constant and piecewise constant load demand profiles.

Energy consumption has become a major concern in the recent years and Green computing has arisen as one of the challenges in order to reduce CO 2 emissions in the computing domain. Many efforts have been made to make hardware less energy consuming, reduce cooling energy of data and computing centers by relocating those facilities to cool regions and other. A novel approach to make the computing domain greener is to add renewable energy sources for the power supply. The challenge of this work is to consider computing facilities which are solely run by renewable energy sources such as solar panels and wind turbines. In this work we tackle the problem of scheduling independent tasks within a predicted power envelope that varies during the time. First we evaluate different instances of the problem from a theoretical point of view. Then we propose several heuristics for the case of multi-core architectures and we assess their performance on synthetic workloads and power envelopes.

International audienceIn this paper, we study the problem of optimizing the throughput of streaming applications for heterogeneous platforms subject to failures. Applications are linear graphs of tasks (pipelines), with a type associated to each task. The challenge is to map each task onto one machine of a target platform, each machine having to be specialized to process only one task type, given that every machine is able to process all the types before being specialized in order to avoid costly setups. The objective is to maximize the throughput, i.e., the rate at which jobs can be processed when accounting for failures. Each instance can thus be performed by any machine specialized in its type and the workload of the system can be shared among a set of specialized machines. For identical machines, we prove that an optimal solution can be computed in polynomial time. However, the problem becomes NP-hard when two machines may compute the same task type at different speeds. Several polynomial time heuristics are designed for the most realistic specialized settings. Simulation results assess their efficiency, showing that the best heuristics obtain a good throughput, much better than the throughput obtained with a random mapping. Moreover, the throughput is close to the optimal solution in the particular cases where the optimal throughput can be computed

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In a post-prognostics decision context, this paper addresses the problem of maximizing the useful life of a platform composed of several parallel machines under service constraint. Application on multi-stack fuel cell systems is considered. In order to propose a solution to the insufficient durability of fuel cells, the purpose is to define a commitment strategy by determining at each time the contribution of each fuel cell stack to the global output so as to satisfy the demand as long as possible. A relaxed version of the problem is introduced, which makes it potentially solvable for very large instances. Results based on computational experiments illustrate the efficiency of the new approach, based on the Mirror Prox algorithm, when compared with a simple method of successive projections onto the constraint sets associated with the problem.

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