Cloud and cloud-native technologies have become the pillars of the modern Internet. Users and organizations now rely on cloud applications for their daily needs. However, outages and quality of service degradations can have disastrous impacts on our society. In addition, web applications have become complex distributed systems that are difficult to understand and operate, and therefore more prone to failure if not managed accordingly. Therefore, it is crucial to understand, observe, prevent, detect and correct any problem that may lead to failures. In this thesis, we propose a framework for achieving observability in native cloud environments. Observability envisions a deeper understanding of the complex distributed system that web applications have become. The proposed observability framework is demonstrated via a proof of concept and deployment in production environments. Furthermore, following the principle of autonomic computing, we also propose an architecture for automatic scaling based on observability in cloud-native environments. This architecture allows us to correlate auto-scaling to the application's workload. We also go further by leveraging machine learning and enabling proactive auto-scaling. We focus on using automation and observability to increase QoS metrics during scaling events. In addition, we explore and demonstrate the benefits and feasibility of porting cloud-native architecture and principles to the Internet of Things. We propose to leverage technologies such as software-defined networking and software-defined radio to provide flexible and reconfigurable generic Internet of Things devices.