# SmartX Multi-View Visibility Framework
## 2 Background and Requirements of Flow-Centric Visibility
### Related Work  
In relation to the work presented in this paper, we describe the first group of monitoring approaches designed for the SDN and cloud environments. In the last few years, we have witnessed a growing interest in monitoring cloud resources. Authors in [19] discussed the key properties of a cloud monitoring system and identified open issues and future directions in the field of cloud monitoring. Nagioscore [20] is an integral monitoring tool, capable of monitoring systems, network, and infrastructure. Although Nagios was initially perceived outside the cloud, its most current versions can be easily installed in a cloud environment and it is capable of monitoring both physical and virtual resources. Global monitoring system (GMonE) is a general-purpose monitoring approach that covers the different aspects of the cloud, as the main cloud service models, client-side or service provider-side, and virtual or physical side monitoring [21]. Flexible monitoring solution at the edge (FMonE) is proposed to allow monitoring workflows that conform to the specific demands of edge computing systems [22]. Distributed architecture for resource management and monitoring in clouds (DARGOS) is a decentralized cloud monitoring tool for multitenant clouds and designed to efficiently disseminate monitoring information providing aggregate and filter functionalities [23]. To cover diverse network conditions, robust and agile defense (RAD) presented a reactive system for monitoring failures, anomalies, and attacks for high availability and reliability [24]. New Relic [25] is a famous commercial software as a service (SaaS) solution that can be deployed to monitor data in a cloud. Likewise, Dynatrace [26] also commercializes a solution for application performance monitoring, focusing on application monitoring inside a platform as a service (PaaS) cloud. PayLess also proposes a low-cost SDN monitoring framework by utilizing Floodlight controller's API [27]. For OF@TEIN Playground, tapping-based flow-layer visibility is proposed as a collection tool in [28]. Finally, improving network measurements in terms of accuracy, security, and tight time-scales are approached in [29; 30; 31].  
Several works have been published to provide flow classification and tagging mechanisms. Authors studied a comparison of flow classification and tagging methods in the distributed visibility point (i.e., single OpenFlow switch), which focuses on improving localized flow handling in that point [32]. MultiClassifier combines deep packet inspection (DPI) and machine learning (ML) to quickly classify the collected flows based on application information with an acceptable accuracy rate [33]. Authors studied a number of clustering algorithms and applied them to network traffic classification with a different set of traffic characteristics (e.g., packet size, byte counts, inter-arrival, and connection duration) for each cluster [34]. Similarly, other works have been presented to improve the network traffic classification such as the introduction of flows statistical properties and supervised traffic classification [18; 35; 36]. Most of these works mainly focus on flow clustering or classification by using machine-learning techniques and lack comprehensive details for lightweight packet-precise visibility collection and flexible integration of visibility data that is collected from multiple measurement points of the system and network infrastructure.