Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

Automated Detection of Usage Errors in non-native English Writing

In an investigation of the use of a novelty detection algorithm for identifying inappropriate word combinations in a raw English corpus, we employ an unsupervised detection algorithm based on the one- class support vector machines (OC-SVMs) and extract sentences containing word sequences whose frequency of appearance is significantly low in native English writing. Combined with n-gram language models and document categorization techniques, the OC-SVM classifier assigns given sentences into two different groups; the sentences containing errors and those without errors. Accuracies are 79.30 % with bigram model, 86.63 % with trigram model, and 34.34 % with four-gram model

Structured Review of the Evidence for Effects of Code Duplication on Software Quality

This report presents the detailed steps and results of a structured review of code clone literature. The aim of the review is to investigate the evidence for the claim that code duplication has a negative effect on code changeability. This report contains only the details of the review for which there is not enough place to include them in the companion paper published at a conference (Hordijk, Ponisio et al. 2009 - Harmfulness of Code Duplication - A Structured Review of the Evidence)