SD: A Divergence-Based Estimation Method for Service Demands in Cloud Systems

Salvatore Dipietro, G. Casale
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Abstract

Estimating performance models parameters of cloud systems presents several challenges due to the distributed nature of the applications, the chains of interactions of requests with architectural nodes, and the parallelism and coordination mechanisms implemented within these systems. In this work, we present a new inference algorithm for model parameters, called state divergence (SD) algorithm, to accurately estimate resource demands in a complex cloud application. Differently from existing approaches, SD attempts to minimize the divergence between observed and modeled marginal state probabilities for individual nodes within an application, therefore requiring the availability of probabilistic measures from both the system and the underpinning model. Validation against a case study using the Apache Cassandra NoSQL database and random experiments show that SD can accurately predict demands and improve system behavior modeling and prediction.
基于散度的云系统服务需求估计方法
由于应用程序的分布式特性、与体系结构节点的请求交互链以及在这些系统中实现的并行性和协调机制,估计云系统的性能模型参数会带来一些挑战。在这项工作中,我们提出了一种新的模型参数推理算法,称为状态发散(SD)算法,以准确估计复杂云应用程序中的资源需求。与现有方法不同,SD试图最小化应用程序中单个节点的观察和建模边缘状态概率之间的差异,因此需要系统和基础模型的概率度量的可用性。使用Apache Cassandra NoSQL数据库和随机实验进行的案例研究验证表明,SD可以准确预测需求,并改进系统行为建模和预测。
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