# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
###### Abstract  
Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects. cloud computing; edge computing; fog computing; internet-of-things; machine learning 202222219603390/s2020101960  
communication cost, and astounding technological development. IoT warrants not just device data management, but also information exchange among multidisciplinary platforms. The huge data procured from numerous smart devices entails sharing to add value and a comprehensive understanding of the concerned domain. With collaborative IoT, heterogeneous domains and settings enable sensors, gateways, and services to collaborate at various levels, enriching the quality of human life while improving business processes. The IoT ecosystem extends in scale and complexity, encompassing a range of heterogeneous devices that stretch over several layers of IoT architecture. As IoT systems partake in critical infrastructures, they necessitate resilient service operability [4]. IoT applications are disparate, deployed in healthcare, industries, domotics, smart homes, smart cities, smart transportation, etc. The IoT devices are constituted of small, resource-constrained smart objects, ineffective at handling complex tasks, which entails task offloading to distant cloud servers [5]. The limited storage and computing potential forces IoT devices to rely on cloud data centers [6]. This ensues an increased latency, and the intermittent internet connectivity renders IoT devices inept at managing time-critical real-time applications. Thus, the IoT revolution has steered new research into decentralized models. In this context, edge computing emerged, intending to bring cloud computing capability to the network edge, addressing unfolding issues that cannot be fixed by cloud computing solely, such as latency, bandwidth, and connectivity challenges [7]. Correspondingly, numerous edge computing solutions have been suggested, including Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) [8; 9]. Fog computing surfaced as one of the highly evolved Edge computing concepts. Fog computing aspires to represent a comprehensive framework, allocating resources in sequence along the cloud to the smart devices [10]. Thus, it is not a mere cloud extension, as it actively engages in synergizing the cloud with IoT. In addition, the requisite for sustainable/green computing that aids in conserving energy is crucial to IoT devices. As IoT devices have energy limitations, it is vital to devise energy-aware solutions into the future [11]. In parallel with technological progress, it is imperative to cut back on the carbon footprint to limit environmental deterioration alongside global warming [12].