Using Constraint Based Reasoning for Multi objective Optimisation of the ENTICE Environment
ENTICE is a set of innovative software services currently being developed to facilitate efficient operations of distributed Virtual Machine and container images (VMI/CI) repositories. Its operation necessitates various decision making for which a solver for Multi Objective Optimisation (MOO) problems is used. However, the solver is a bottleneck due to its computational complexity. In order to be able to reduce the search space for the solver, we have developed an ontology and corresponding Knowledge Base (KB) that underpins the operation of the ENTICE environment. The Knowledge Base is developed based on the Jena Fuseki technology. To address the problem of computational complexity, constraint based queries and different reasoning mechanisms are applied. The Knowledge Base services are then integrated with other ENTICE services including the MOO solver. It is shown that this approach significantly reduces the computational complexity for the MOO, thus it shortens the optimisation time, and makes it possible to use the MOO for both strategic (decisions that can be made up to one day in advance) and dynamic (decisions requiring response within one minute) decision making possible.
2016 12th International Conference on Semantics, Knowledge and Grids (SKG)