Optimal and Feasible Cloud Resource Configurations Generation Method for Genomic Analytics Applications

This paper proposes a new method that efficiently generates optimal and feasible cloud resource configurations for deploying genomic analytics applications. The proposed method generates optimal and feasible configurations with respect to system requirements by employing two different problem-solving techniques: an equivalent transformation algorithm and a multi-objective genetic algorithm. The equivalent transformation algorithm first generates feasible configurations through computation based on (1) state replacement of clause sets representing various resource configurations and (2) evaluation of their equivalence in terms of declarative meaning to the given requirements. Subsequently, the multi-objective genetic algorithm identifies the optimal configurations with respect to estimated financial cost and computational performance. The input to the proposed method is a logical formula describing the system requirements, whereas the output is a set of unit clauses representing Pareto-optimal and feasible cloud resource configurations for deploying a given genomic analytics application. The results of experiments conducted using a sample genomic analytics workflow and Amazon EC2 instances verify the efficacy of the proposed method.