One major service provided by cloud computing is Software as a Service (SaaS). As competition in the SaaS market intensifies, it becomes imperative for a SaaS provider to design and configure its computing system properly. This paper studies the application placement problem encountered in computer clustering in SaaS networks. This problem involves deciding which software applications to install on each computer cluster of the provider and how to assign customers to the clusters in order to minimize total cost. Given the complexity of the problem, we propose two algorithms to solve it. The first one is a probabilistic greedy algorithm which includes randomization and perturbation features to avoid getting trapped in a local optimum. The second algorithm is based on a reformulation of the problem where each cluster is to be assigned an application configuration from a properly generated subset of configurations. We conducted an extensive computational study using large data sets with up to 300 customers and 50 applications. The results show that both algorithms outperform a standard branch-and-bound procedure for problem instances with large sizes. The probabilistic greedy algorithm is shown to be the most efficient in solving the problem.

中文翻译：

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软件即服务（SaaS）网络中计算机群集中的应用程序放置

云计算提供的一项主要服务是软件即服务（SaaS）。随着SaaS市场竞争的加剧，SaaS提供商必须正确设计和配置其计算系统。本文研究了SaaS网络中计算机集群中遇到的应用程序放置问题。此问题涉及确定要在提供商的每个计算机群集上安装哪些软件应用程序，以及如何将客户分配给群集，以最大程度地降低总成本。考虑到问题的复杂性，我们提出了两种算法来解决。第一个是概率贪婪算法，该算法包括随机和扰动特征，以避免陷入局部最优状态。第二种算法基于问题的重新表述，其中将从正确生成的配置子集中为每个群集分配应用程序配置。我们使用多达300个客户和50个应用程序的大数据集进行了广泛的计算研究。结果表明，对于较大的问题实例，两种算法均优于标准的分支定界程序。概率贪婪算法被证明是解决问题最有效的方法。