We present results of a series of experiments with parallel processing divisible tasks on various cluster of workstations platforms. Divisible task is a new model of scheduling distributed computations. It is assumed that the parallel application can be divided into parts of arbitrary sizes and the parts can be processed independently on distributed computers. Though practical verification of the scheduling model was the primary goal of the experiments also an insight into the behavior and performance of cluster computing platforms has been gained.

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In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models. INDEX TERMS Cloud computing, computers and information processing, deep learning, distributed computing, machine learning, serverless architectures.

In this paper, we analyze processing divisible loads in systems with a memory hierarchy. Divisible loads are computations that can be divided into parts of arbitrary sizes and these parts can be independently processed in a distributed system. The problem is to partition the load so that the total processing time, including communications and computations, is the shortest possible. Earlier works in the divisible load theory assumed distributed systems with a flat memory model. The dependence of the processing time on the size of the assigned load was assumed to be linear. A new mathematical model relaxing the above two assumptions is proposed in this article. We study distributed systems which have both the hierarchical memory model and a piecewise linear dependence of the processing time on the size of the assigned load. Performance of such systems is modeled and evaluated. Finally, we compare the efficiency of distributed processing divisible loads in multiinstallment and out-of-core modes. Multiinstallment processing consists in sending multiple small chunks of the load to processors instead of a single chunk which needs external memory. It turns out that multiinstallment is an advantageous strategy for reasonably selected load chunks sizes.

The Certification Authority Coordination Group in the European DataGrid project has created a large-scale Public Key Infrastructure and the policies and procedures to operate it successfully. The infrastructure demonstrates interoperability of multiple certification authorities (CAs) in a novel system of peer-assessment of the roots of trust. Crucial to the assessment is the definition of minimum requirements that all CAs must meet in order to be accepted. The evaluation is aided by software-generated trust matrices. Related work building on this infrastructure is described. The group's policies and experience now form the basis of the new European Policy Management Authority for Grid Authentication in e-Science.