# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 6 Confluence of ML and Fog/Edge  
The conventional cloud model falls short of fulfilling IoT application necessities due to the enormous data generated from IoT devices [166]. Transmitting the overwhelming IoT data to the cloud would cause network overhead, consuming bandwidth, and latency issues [167]. Hence, to cut back on the data transfer cost as well as network delays, service providers are steering towards the fog and edge computing [168], with an additional opportunity for enforcing security and privacy [169]. The IoT systems comprise edge equipment, sensors, and actuators with latency, bandwidth, and security necessities [166]. The fog computing technology of extending computer and storage to network's edge solves processing and networking impediments [167], enabling rapid processing close to the data source [170]. The complexity and dynamism of fog computing with its communication networks facilitating low latency makes sophisticated computation possible in a conducive environment. Fog computing confers societal benefits through its range of applications, namely, healthcare, Industry 4.0, autonomous vehicles, smart cities [171], etc. Despite that, it encounters performance as well as security setbacks. As a result, machine learning (ML), which is a subfield of artificial intelligence (AI), is catching on to assist FC in resolving its shortcomings. Using ML to enhance FC applications and deliver efficient services in terms of accuracy, latency reduction, energy consumption, security, privacy, resource, and traffic management [25; 172; 173] has been increasingly popular in recent times. Fog computing resource management involving ML enhances the computer, decision-making, and resource provisioning, along with delay prediction. Deploying ML techniques in fog computing facilitates accurate data processing and analyses in real-time while managing the network overhead as well as communication traffic, owing to fog's decentralized model. The security aspects for the device, network, and data involving fog computing accompanied by ML prove to be effective. The merging of the fog model with machine learning has evolved into robust end-user and upper-layer services, allowing for deeper analytics and intelligent answers to tasks. Machine learning (ML) is a promising option for intelligent data processing and inference and is a prime enabler to various IoT application domains [166], such as healthcare, smart home, smart agriculture, smart industry, smart grid, etc. It has a crucial part in designing the intelligent/smart setting for autonomous operations [167]. Machine learning has immense potential as a significant IoT technology gaining traction to provide insights for IoT applications [174]. IoT has excellent prospects for enhancing human life and industrial growth as innumerable sensing devices perform monitoring and increase communication potential [175]. For resource-constrained IoT devices, the confluence of machine learning with the cloud, edge, and fog is vital for IoT implementation [156; 175] to usher in efficient performance, greater controllability, productivity, and cost reduction possibilities, while managing IoT's QoS challenges. Enabling intelligence at fog and IoT improves the overall performance [100]. FC moves the cloud's potential to the edge of the network, where IoT and human users are present. Intelligence can be incorporated into FC as device-driven or human-driven. In a device-driven approach, fog and IoT are equipped with more sensing, processing, network, and storage capabilities, enabling context awareness for decision making and local resource management. In a human-driven model, human users act as the data source to the system, whose behavioral pattern is the key in shaping the network while serving them. Collectively, these two approaches can help meet IoT's demand for QoS when designing fog computing systems. The harnessing of machine learning in an IoT setting facilitates deeper analytics and helps materialize efficient and smart IoT applications [174].