Security and capacity constraints allocation in distributed object oriented databases

• Authors: Jonathan M. Graham
• Venue: CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
• Year: 2007

Efficient distribution of data is a major challenge in distributed databases. The problem is even more severe for distributed object oriented databases because of inheritance, encapsulation and the more complex problem involved when methods invoke other methods. This problem, the Object Allocation Problem (OAP), is a harder version of the relational database allocation problem (DAP), a problem known to be NP-hard. The additional constraint of restricting sensitive data to secure sites, results in a problem which is at least as hard as the OAP. In this research we investigated the cost efficient distribution of a fragmented object oriented database to the sites of a network. We investigated allocation algorithms for distributing the fragments in the presence of both capacity and security constraints. We assumed that each site had a fixed capacity and that there are two types of fragments, sensitive fragments containing sensitive data, and non-sensitive fragments containing general data. Likewise we considered two types of sites, secure and non-secure. A secure site is one which was designed to minimize the likelihood of the data at that location being compromised. We looked at the two conflicting goals of minimizing the number of external accesses and at the same time, minimizing the number of sensitive fragments placed at non-secure sites. We implemented three algorithms a Genetic algorithm (GA) a simulated annealing algorithm (SA) and a graphical partitioning algorithm (KL). We took two multiobjective approaches, the penalty and the pareto approaches and we looked at two types of initial allocations, heuristic and random. Of the three algorithms implemented the Genetic algorithm (GA), Simulated annealing (SA) and Kernighan-Lin (KL), the SA and GA algorithms were clearly superior. In the presence of both capacity and security constraints, the various versions of the GA performed consistently better in minimizing the number of security violations while the SA algorithm versions typically had lower costs. The noted exception was the GA penalty version algorithm with random initial allocation, which performed very well in both cost minimization and reducing the number of security violations.