Software-defined carrier wireless networks

The telecommunication industry is entering a new era. The increased traffic demands imposed by the huge number of always-on connections require a quantum leap in the field of enabling techniques. Furthermore, subscribers expect ever increasing quality of experience with its joys and wonders, while network operators and service providers aim for cost-efficient networks. These requirements require a revolutionary change in the telecommunications industry, as shown by the success of virtualization in the IT industry, which is now driving the deployment and expansion of cloud computing. Telecommunications providers are currently rethinking their network architecture from one consisting of a multitude of black boxes with specialized network hardware and software to a new architecture consisting of “white box” hardware running a multitude of specialized network software. This network software may be data plane software providing network functions virtualization (NVF) or control plane software providing centralized network management — software defined networking (SDN). It is expected that these architectural changes will permeate networks as wide ranging in size as the Internet core networks, to metro networks, to enterprise networks and as wide ranging in functionality as converged packet-optical networks, to wireless core networks, to wireless radio access networks.

DeepSoft: a vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.