@Article{Kimovski_Prodan_2019, author = {Gec, Sandi and Kimovski, Dragi and Pascinski, Uros and Prodan, Radu Aurel and Stankovski, Vlado}, journal = {Concurrency and Computation: Practice and Experience}, title = {{Semantic approach for multi-objective optimisation of the ENTICE distributed Virtual Machine and container images repository}}, year = {2019}, month = {Februar}, number = {3}, volume = {31}, abstract = {New software engineering technologies facilitate development of applications from reusable software components, such as Virtual Machine and container images (VMI/CIs). Key requirements for the storage of VMI/CIs in public or private repositories are their fast delivery and cloud deployment times. ENTICE is a federated storage facility for VMI/CIs that provides optimisation mechanisms through the use of fragmentation and replication of images and a Pareto Multi‐Objective Optimisation (MO) solver. The operation of the MO solver is, however, time‐consuming due to the size and complexity of the metadata, specifying various non‐functional requirements for the management of VMI/CIs, such as geolocation, operational cost, and delivery time. In this work, we address this problem with a new semantic approach, which uses an ontology of the federated ENTICE repository, knowledge base, and constraint‐based reasoning mechanism. Open Source technologies such as Protégé, Jena Fuseki, and Pellet were used to develop a solution. Two specific use cases, (1) repository optimisation with offline and (2) online redistribution of VMI/CIs, are presented in detail. In both use cases, data from the knowledge base are provided to the MO solver. It is shown that Pellet‐based reasoning can be used to reduce the input metadata size used in the optimisation process by taking into consideration the geographic location of the VMI/CIs and the provenance of the VMI fragments. It is shown that this process leads to reduction of the input metadata size for the MO solver by up to 60% and reduction of the total optimisation time of the MO solver by up to 68%, while fully preserving the quality of the solution, which is significant.}, doi = {10.1002/cpe.4264}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4264} }