# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 4 Challenges and Opportunities
### Fog Computing: Open Challenges  
The fog computing paradigm has evolved from the cloud computing utility model. With IoT proliferation, computations closer to the network edge significantly minimize the cost of computing and data offloading at the cloud. However, processing at the edge poses numerous challenges pertaining to devices, security, the network, integrating fog, and IoT, which the distributed fog system has to deal with [28; 44; 110]. The open challenges identified are pictured in Figure 13.  
* Standards and programming languages  
The fog structure is distinct from the cloud as it extends cloud services to end-user devices, warranting upgraded standards and associated programming languages, along with effective user interfaces and network protocols for IoT device management. * Scalability  
Scalability is a key issue for systems involving extensive IoT applications on fog, and exploring optimal algorithms that illustrate the fog system's complexity would be valuable. In the fog model, time-critical tasks are executed at the fog, and others are moved for processing to the cloud. Ascertaining when fog resources are utilized optimally depending on service type, user count, and resource availability are significant. * Computational challenges  
The Fog system continually interacts with the cloud servers. It intends to respond to users within a stipulated duration and forward complex computer-intensive tasks to the cloud, which may take longer. The parts of computation that are unrestricted by response time are sent to the cloud, while others are carried out at the edge for a minimum computational cost. The challenge lies with figuring out which computer tasks are to be executed at the edge and offloaded to the cloud. * Deployment challenges  
The fog system has to be precisely deployed to subdue latency. Factors such as the type and task amount performed at a particular tier, fog device capability, and reliability, and the number of sensors determine implementation decisions. As per the application requirements, resource scaling, as well as shrinking, are carried out without hindering the operation of ongoing services. OpenFog recommends the N-tier fog model from s mobilization viewpoint; however, escalating the fog layer levels may instigate delays, which require defining the number of levels for the specific application. * Decentralized framework and failure management  
The decentralized fog entails a high likeliness of fog device malfunction relating to the software, hardware, power source, mobility, as well as connectivity issues considering an unreliable wireless connection, linking the majority of fog devices. The fog system is adaptable to a minor disruption and resource shortage. The fog node failure may make its respective virtualized resources unavailable, and related issues, such as latency and migration, have to be dealt with for resource availability at downtime. The decentralized fog results in the repetition of code at edge devices, and this redundancy has to be checked. The random distribution of network resources at the edge complicates connectivity, which can be rectified by deploying a middleware that manages resources to the demanding application. The small client services are disseminated from the cloud to the edge, and acquiring such services from fog systems is quite challenging. The fog system manages billions of IoT devices; hence, provisioning services to all fog devices is arduous. The portability of the fog's edge node requisites ubiquitous fog computing. With fog being distributed, the preciseness of computation needs to be confirmed as its applications demand consistency. * Device heterogeneity and resource management  
Fog computing sets the stage for numerous heterogeneous technologies to offer IoT services with a key challenge of linking resources from diverse platforms. It is vital to examine algorithms that are competent at handling scheduling, synchronizing for the effective utilization of IoT devices that are short on resources.