基于改进K-dominance的服务评价与优选

Improved K-dominance Based Approach for Service Ranking and Selection

在动态、开放的云计算环境中,越来越多互有竞争关系的服务提供商以不同的服务质量等级提供功能相同或类似的服务,这使得基于QoS的服务优选变得愈加重要.考虑到传统方法的不足,提出一个完整的方法解决这一多目标优化问题.首先,提出一个新的概念:partial dominance score,用于针对skyline计算得到的最优解(skyline服务集合)实现进一步更为精确的量化评价,在此基础上重新定义了服务优选问题的优化目标.随后提出一个基于BNL策略的服务优选算法高效地返回partial dominance score意义上的top-k最优skyline服务,从而为用户的最终决策和优选提供有效的支持.通过一系列实验验证了所提出算法的效率和有效性.

In the open, dynamic cloud computing environment, more and more functional-equivalent services are provided with different QoS levels by competing service providers, which makes service selection problem becoming more and more important. Considering main drawbacks of traditional approaches for service selection, a systemic method for this multi-objective optimization problem has been presented in this paper. More specifically, at first a novel concept, i.e. partial dominance score, has been proposed. By combining two kinds of ranking method, i.e. the partial dominance score as well as the skyline computation, the optimization objective for service selection has been redefined. Then, a BNL_based service selection algorithm has been presented to find top-k skyline solutions that have higher partial dominance score. Finally the efficiency and effectiveness of our proposed algorithm was evaluated through a set of experimental studies.