使用概率模型检验的云弹性
Cloud elasticity using probabilistic model checking
摘要：
云计算由于其实现规模经济的能力，已经成为部署大规模基础设施和运行大数据应用的领先范例。在这项工作中，我们专注于云计算的最突出的优势之一，即按需资源供应，这通常被称为弹性。虽然已经投入了大量的努力来开发能够实现弹性的系统和机制，但是弹性决策策略倾向于在不保证或量化其操作质量的情况下设计。这项工作的目的是使弹性政策的发展更加正规化和可靠。我们有两个不同的贡献。首先，我们提出了一个可扩展的方法来执行弹性通过动态实例化和在线定量验证马尔可夫决策过程（MDP）使用概率模型检验。其次，我们提出了具体的弹性模型和相关的弹性政策。我们使用NoSQL数据库集群中的真实和合成数据集来评估我们的决策策略。根据实验结果，我们的方法在显着增加用户定义的效用值和减少用户定义的阈值违规的最先进的改进。
英文摘要：
Cloud computing has become the leading paradigm for deploying large-scale infrastructures and running big data applications, due to its capacity of achieving economies of scale. In this work, we focus on one of the most prominent advantages of cloud computing, namely the on-demand resource provisioning, which is commonly referred to as elasticity. Although a lot of effort has been invested in developing systems and mechanisms that enable elasticity, the elasticity decision policies tend to be designed without guaranteeing or quantifying the quality of their operation. This work aims to make the development of elasticity policies more formalized and dependable. We make two distinct contributions. First, we propose an extensible approach to enforcing elasticity through the dynamic instantiation and online quantitative verification of Markov Decision Processes (MDP) using probabilistic model checking. Second, we propose concrete elasticity models and related elasticity policies. We evaluate our decision policies using both real and synthetic datasets in clusters of NoSQL databases. According to the experimental results, our approach improves upon the state-of-the-art in significantly increasing user-defined utility values and decreasing user-defined threshold violations.
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