Introducing Novel Crossover and Mutation Operators into Data Replication Strategies for Distributed Systems

This is the authors' abstract. We don't add key points for this paper.

In a distributed paradigm, data replication plays a vital role in achieving high availability and fault tolerance of a system. In a connected network, faults are inevitable, replication masks those faults at run-time while users are unaware of it and the system continues to work as expected. There are different strategies to enforce such fault-tolerant behavior on a system. However, there are numerous scenarios reflecting different trade-offs between several quality metrics and to identify a relevant strategy for a specific scenario is quite cumbersome since there could exist potentially infinite scenarios and solutions are limited. This requires designing new solutions satisfying the constraints of such scenarios, which may be left unaddressed otherwise. In this regard, this paper develops a mechanism to automatically design new replication strategies (up-to-now unknown), optimized for given scenarios. The paper uses genetic programming to explore unknown replication strategies. It evolves the population of replication strategies (representing each a computer program) gradually, but consistently to make them optimized to eventually meet the desired criteria. Furthermore, it introduces strong multi-crossover and multi-mutation operators into replication, which strengthens our machine learning framework, at the same time guaranteeing consistency of the solutions, to generate innovative hybrid replication strategies.