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In this article, we present a novel algorithmic method for the calculation of thresholds for a metric set. To this aim, machine learning and data mining techniques are utilized. We define a data-driven methodology that can be used for efficiency optimization of existing metric sets, for the simplification of complex classification models, and for the calculation of thresholds for a metric set in an environment where no metric set yet exists. The methodology is independent of the metric set and therefore also independent of any language, paradigm or abstraction level. In four case studies performed on large-scale open-source software metric sets for C functions, C++, C# methods and Java classes are optimized and the methodology is validated.

Carrier-grade networks of the future are currently being standardized and designed under the umbrella name of Next Generation Network (NGN). The goal of NGN is to provide a more flexible network infrastructure that supports not just data and voice traffic routing, but also higher level services and interfaces for thirdparty enhancements. Within this paper, opportunities to integrate grid and cloud computing