The fog-enabled cloud computing has received considerable attention as the fog nodes are deployed at the network edge to provide low latency. It involves various activities, such as configuration management, security management, and data management. Monitoring these activities is essential to improve performance and QoS of fog computing infrastructure. Data collection and aggregation are the basic tasks in the monitoring process, and these phases consume more communicational power as the IoT nodes generate a huge amount of redundant data frequently. In this paper, a multi-agent-based data collection and aggregation model is proposed for monitoring fog infrastructure. The data collection model adopts a hybrid push-pull algorithm that updates the data when a certain change in new data compared to old data. A tree-based data aggregation model is developed to reduce communication overhead between fog node and cloud. The experimental results show that the proposed model improves data coherency and reduces communication overhead compared to existing data collection and aggregation models.

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Due to many economical benefits cloud computing has quickly become popular and is widely used for delivering services over the Internet. As the number of cloud users increases day by day, data centers are continuing to grow in terms of hardware resources, virtual resources and traffic volume; thus making cloud operation and management more intricate. To manage complex infrastructure of cloud, an efficient and effective Cloud Monitoring System (CMS) is needed to improve the overall performance of cloud. Cloud monitoring is the process of reviewing, controlling and managing the operational workflow and processes within a cloud-based IT asset or infrastructure. This paper deals with detailed study of CMSs based on their architecture, monitoring phases, properties and functions. It describes various phases of cloud monitoring activities and presents the comparative study of the state-of-theart works of theses phases. Usage of agents for monitoring of cloud activities is also described. Finally, various challenges/issues and possible future directions of cloud monitoring is discussed. This paper helps researchers, engineers and scientists to know the state-of-the-art works for monitoring cloud activities

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