A Cost-Driven Multi-objective Optimization Algorithm for SaaS Applications Placement

摘要

Large-scale component placement problem is a key demand of cloud computing. For optimal placement of SaaS component problem, this paper proposes a cost-driven multi-objective optimization hybrid genetic simulated annealing algorithm (GASA) in order to reduce operating costs of SaaS components. GASA is divided into two stages. In the first stage, genetic algorithm is used to optimize the hardware cost. In the second stage, the simulated annealing algorithm is used to adjust the position of the components in the virtual machine, and the communication overhead is further optimized. GASA makes full use of the global search advantage of genetic algorithm and the local search advantage of simulated annealing algorithm. GASA is a multi-objective optimization for both the hardware costs and the communication overhead. The result shows that GASA can effectively improve the efficiency and obtain the higher quality solution compare to the traditional heuristic and single genetic algorithm or single simulated annealing algorithm.