Recently researchers use skyline techniques to optimize service selection procedure, where they can filter those low-quality web services from the large amount of candidates and return a much smaller high-quality service set. The skycube concept is adopted for quickly responding to the skyline queries with different combinations of Quality of Web Service (QoWS) parameters. As the skycube computation is quite time-consuming, it is a compelling challenge to accelerate this procedure. However, the current solutions usually have a number of redundant computations which will significantly affect the efficiency. To address such drawbacks, after an in-depth analysis of skycube computation procedure, we introduce a partial skycube, which only consists of the skylines with frequently used combinations of QoWS. Then the computational relationships between the skyline on one subspace and its parent-space are studied. Based on the relationships, we develop ${\sf {ParCube}}$ParCube algorithm to speedup partial skycube computation by reusing the intermediate comparison results. Meanwhile, at the execution phase, ${\sf {ParCube}}$ParCube can be further optimized with parallel execution mode and optimized scheduling strategy. Finally, we evaluate the efficiency and scalability of ${\sf {ParCube}}$ParCube on both single machine and cluster environment. The results show that ${\sf {ParCube}}$ParCube can efficiently compute partial skycube and scale well in cluster environment.

中文翻译：

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通过部分和并行处理来加速 Skycube 计算以进行服务选择

最近研究人员使用 Skyline 技术来优化服务选择过程，他们可以从大量候选服务中过滤出那些低质量的 Web 服务，并返回一个小得多的高质量服务集。Skycube 概念用于快速响应具有不同 Web 服务质量 (QoWS) 参数组合的 Skyline 查询。由于天空立方体的计算非常耗时，因此加速这个过程是一个引人注目的挑战。然而，当前的解决方案通常具有许多冗余计算，这将显着影响效率。为了解决这些缺点，在对 Skycube 计算过程进行深入分析后，我们引入了部分 Skycube，它仅由具有常用 QoWS 组合的天际线组成。然后研究了一个子空间上的天际线与其父空间之间的计算关系。基于关系，我们开发${\sf {ParCube}}$ParCube通过重用中间比较结果来加速部分天空立方体计算的算法。同时，在执行阶段，${\sf {ParCube}}$ParCube可以通过并行执行模式和优化的调度策略进一步优化。最后，我们评估了效率和可扩展性${\sf {ParCube}}$ParCube在单机和集群环境中。结果表明${\sf {ParCube}}$ParCube 可以有效地计算部分天空立方体并在集群环境中很好地扩展。