Cloud computing improves utilization and flexibility in allocating computing resources while reducing the infrastructural costs. However, in many cases cloud technology is still proprietary and tainted by security issues rooted in the multi-user and hybrid cloud environment. A lack of secure connectivity in a hybrid cloud environment hinders the adaptation of clouds by scientific communities that require scaling-out of the local infrastructure using publicly available resources for large-scale experiments. In this article, we present a case study of the DII-HEP secure cloud infrastructure and propose an approach to securely scale-out a private cloud deployment to public clouds in order to support hybrid cloud scenarios. A challenge in such scenarios is that cloud vendors may offer varying and possibly incompatible ways to isolate and interconnect virtual machines located in different cloud networks. Our approach is tenant driven in the sense that the tenant provides its connectivity mechanism. We provide a qualitative and quantitative analysis of a number of alternatives to solve this problem. We have chosen one of the standardized alternatives, Host Identity Protocol, for further experimentation in a production system because it supports legacy applications in a topologically-independent and secure way.

A classifier based on a Feed-Forward Neural Network has been used for separating a sample of about 123,500 selected hadronic decays of the Z0, collected by DELPHI during 1991, into three classes according to the flavour of the original quark pair: [Formula: see text] (unresolved), [Formula: see text] and [Formula: see text] The classification has been used to compute the partial widths of the Z0 into b and C quark pairs.