Paper Info
Title
Using Constraint-Based Reasoning for Multi-objective Optimisation of the ENTICE Environment
Abstract
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.
Year
DOI
Venue
2016
10.1109/SKG.2016.011
2016 12th International Conference on Semantics, Knowledge and Grids (SKG)
Keywords
Field
DocType
Repository,Virtual Machine Image,Container Image,Quality of Service,Reasoning
Ontology,Data mining,Bottleneck,Virtual machine,Computer science,Software,Solver,Knowledge base,Semantics,Distributed computing,Computational complexity theory
Conference
ISSN
ISBN
Citations 
2325-0623
978-1-5090-4796-3
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Sandi Gec1143.74
Dragi Kimovski23910.23
Radu Prodan32314152.27
Vlado Stankovski428330.54