**Postdoc positions at Inria Bordeaux****Periodic service allocation for PaaS Clouds**With the increased use of PaaS Cloud Computing platforms to easily deploy elastic online services, the energy and resource usage of the very large datacenters which host these platforms is getting massive. Consolidation and co-allocation are possible solutions to alleviate this, but this requires efficient allocation algorithms to cope with the complexity of the problem. In particular, the dynamicity of the demand from the users of these online services and the quality of service constraints required by the operators are important factors which need to be taken into account. Due to the sheer size of the platforms, the available solutions are mostly simple greedy strategies which can not provide long-term vision of the allocation. A very promising direction to improve the efficiency of allocations is to make use of the observed periodicity of the resource demand, which exhibit very strong daily and weekly patterns. The objective of this postdoc is to propose and evaluate efficient allocation algorithms in this context of periodic demands, with the objective to minimize the energy and resource usage of the platforms, while also keeping the amount of dynamic modifications (through migrations / reallocations) as low as possible. The solutions proposed will be analyzed analytically and/or with extensive simulations using the SimGrid framework.**Modeling and Analysis of dynamic Map Reduce schedulers**The tremendous increase in the size and heterogeneity of supercomputers and cloud platforms makes it very difficult to predict the performance of a scheduling or a resource allocation algorithm. Therefore, dynamic solutions, where scheduling/alllocation decisions are made at runtime have overpassed static strategies. The simplicity and efficiency of dynamic schedulers such as Hadoop are a key of the success of the MapReduce framework. In this context, it is crucial to understand the influence of the parameters of the dynamic scheduler (in the case of Hadoop for instance the number of replicas, their placement, the dynamic allocation strategy) through the analysis of mathematical models. The first part of the postdoc will therefore be devoted to the modeling of Hadoop algorithm and its influence on the design of the algorithms. Up to now, the use of MapReduce is restricted to applications that can be written as a sequence of map and reduce operation and therefore who operate at each step on independent tasks and data. In a second step, our goal will be, following [HPDC14] to propose a more general model (in particular suitable to linear algebra operations) and to understand how data placement and task allocation strategies can be adapted to this new context. Throughout all the postdoc, conclusions will be assessed through an extensive use of simulations using the SimGrid framework [HPDC14] O. Beaumont and L. Marchal. Analysis of Dynamic Scheduling Strategies for Matrix Multiplication on Heterogeneous Platforms. 23rd ACM Symposium on High-Performance Parallel and Distributed Computing

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