CAJ | 학술논문

当处理分布式、大规模的服务选择时，传统服务选择方法存在着效率不高和全局QoS性能低下的问题。基于MapReduce框架，设计了一种云环境下的海量服务选择方法以解决此问题。首先，基于MapReduce框架，利用Skyline算法，筛选海量候选服务，生成Skyline服务库；其次，基于迭代式MapReduce框架，运用多目标模拟退火算法，从所生成的Skyline服务库中优选Skyline服务，产生一组Pareto最优的组合服务；最后，依据用户的个性化和多样性需求，执行Top-k查询，优选出满足用户偏好的k个组合服务。该方法适应于具有分布式环境、高维QoS的海量服务选择，能快速返回组合服务，且其全局QoS较优。
당처리분포식、대규모적복무선택시，전통복무선택방법존재착효솔불고화전국QoS성능저하적문제。기우MapReduce광가，설계료일충운배경하적해량복무선택방법이해결차문제。수선，기우MapReduce광가，이용Skyline산법，사선해량후선복무，생성Skyline복무고；기차，기우질대식MapReduce광가，운용다목표모의퇴화산법，종소생성적Skyline복무고중우선Skyline복무，산생일조Pareto최우적조합복무；최후，의거용호적개성화화다양성수구，집행Top-k사순，우선출만족용호편호적k개조합복무。해방법괄응우구유분포식배경、고유QoS적해량복무선택，능쾌속반회조합복무，차기전국QoS교우。
When dealing with distributed and massive services selection, traditional approaches of service selection have lower efficiency and poorer performance of global QoS. We present an approach of massive services selection based on MapReduce framework in cloud environment to solve the problem. Firstly, we screen massive candidate services and generate a library of Skyline services by using Skyline algorithm based on the MapReduce framework; Secondly, we select the preferred Skyline services form the generated Skyline services library to generate a set of Pareto optimal composite services using multi-objective simulated annealing algorithm based on iterative MapReduce framework; Finally, according to the user's personalized and diverse demand, we execute Top-k queries to select preferablyk composite services which meet the user's preference. The proposed approach is adapted to service selection with services of large-scale, high-dimensional QoS in distributed environment, it can quickly return to composite services, and its global QoS are optimum.