Automatic Dynamic Data Structures Recognition to Support the Migration of Applications to the Cloud

The work presented in this manuscript describes a methodology for the recognition of Dynamic Data structures, with a focus on Queues, Pipes and Lists. The recognition of such structures is used as a basis for the mapping of sequential code to Cloud Services, in order to support the semi-automatic restructuring of source software. The goal is to develop a complete methodology and a framework based on it to ease the efforts needed to port native applications to a Cloud Platform and simplify the relative complex processes. In order to achieve such an objective, the proposed technique exploits an intermediate representation of the code, consisting in parallel Skeletons and Cloud Patterns. Logical inference rules act on a knowledge base, built during the analysis of the source code, to guide the recognition and mapping processes. Both the inference rules and knowledge base are expressed in Prolog. A prototype tool for the automatic analysis of sequential source code and its mapping to a Cloud Pattern is also presented.