We present an innovative approach for solving Four Dimensional Variational Data Assimilation (4D-VAR DA) problems. The approach we consider starts from a decomposition of the physical domain; it uses a partitioning of the solution and a modified regularization functional describing the 4D-VAR DA problem on the decomposition. We provide a mathematical formulation of the model and we perform a feasibility analysis in terms of computational cost and of algorithmic scalability. We use the scale-up factor which measure the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Shallow Water Equations)
The minimisation of the total cost of ownership is hard to be faced by the owners of large scale computing systems, without affecting negatively the quality of service for the users. Modern datacenters, often included The minimisation of the total cost of ownership is hard to be faced by the owners of large scale computing systems, without affecting negatively the quality of service for the users. Modern datacenters, often included in distributed environments, appear to be “elastic”, i.e., they are able to shrink or enlarge the number of local physical or virtual resources, also by recruiting them from private/public clouds. This increases the degree of dynamicity, making the infrastructure management more and more complex. Here, we report some advances in the realisation of an adaptive scheduling controller (ASC) which, by interacting with the datacenter resource manager, allows an effective and an efficient usage of resources. In particular, we focus on the mathematical formalisation of the ASC’s kernel that allows to dynamically configure, in a suitable way, the datacenter resources manager. The described formalisation is based on a probabilistic approach that, starting from both a hystorical resources usage and on the actual users request of the datacenter resources, identifies a suitable probability distribution for queue time with the aim to perform a short term forecasting. The case study is the SCoPE datacenter at the University of Naples Federico II
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