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Cloud computing has attracted increasing interest in industry and academia. However, due to the constantly expanding scale of cloud platforms, the monitoring of clouds encounters a number of critical challenges related to flexibility, scalability, efficiency, and performance. In this paper, we present JTang Cloud Monitoring System (JTangCMS), an efficient monitoring system for cloud platforms. Our contributions cover the collection, delivery, and processing of monitoring data. For data collection, a flexible and scalable agent is implemented with pluggable monitoring components to collect runtime information from different entities. For data delivery, an efficient and robust data dissemination framework is implemented to transmit the runtime information reliably with high throughput and low latency, based on the Data Distribution Service (DDS). For data processing, a cloud action platform is developed to support cloud management decision-making, based on complex event processing (CEP). Finally, detailed experimental evaluations show the feasibility and efficiency of JTangCMS. [ABSTRACT FROM AUTHOR]

Abstract: Performance analysis and prediction need a solid understanding of the system workload. As a salient workload characteristic, burstiness has critical impact on resource provisioning and performance of cloud based applications. Thus performance analysis and prediction under bursty workloads are of crucial importance to cloud based applications. However, it is yet challenging for such analysis and prediction, since no accurate and effective bursty workload generator exists, as well as the fine-grained bursty workload analysis and prediction method. In this article, to deal with these challenges, a bursty workload generator has been proposed for Cloudstone (a cloud benchmark) based on 2-state Markovian Arrival Process (MAP2). Then based on this generator, a fine-grained performance analysis method, which can be used to predict the probability density function of CPU utilization, has been suggested for cloud based applications, to support better resource provisioning decision making and system performance optimization. Finally, extensive experiments are conducted in a Xen-based virtualized environment to evaluate the accuracy and effectiveness of the two methods. By comparing the actual value of Indices of Dispersion for Count with the target value deduced from MAP2 model, the experiments show the precision of our method is superior to existing works. By comparing the real and predicted system resource utilization under a variety of bursty workloads generated by the proposed generator, the experiments also demonstrate the effectiveness and accuracy of the proposed fine-grained system resource utilization prediction method. [Copyright &y& Elsevier]