An Overview of Cloud and Edge Computing Architecture and Its Current Issues and Challenges

Abstract

Edge computing is a technique of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data in the systems. And, cloud computing is a service that delivers on-demand self-service, broad network access, resource pooling, rapid elasticity or expansion, which is trending in today's technology-driven world. With the advantage of flexibility, storage, sharing, and easy accessibility, cloud is being used by major players in IT (information technology). This chapter highlights cloud/edge computing architecture and its current issues and challenges from technological and organizational aspects. A brief introduction of edge computing architecture with similar technologies along with its service models is discussed. A few counterexamples of cloud computing architecture are showed. Organizational aspects of cloud computing architecture, as well as IBM and Oracle reference cloud architecture, are briefly presented. Some emerging issues and challenges associated with cloud/edge computing on its utilization are also elaborated.

Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0

“…Cloud Computing, as a mixture of centralized, distributed and parallel system, includes virtualized and organized computers that are dynamically supplied and set or a large number of existing computing resources creates a service at the level of the connected device [27]. Cloud Computing represents a computing technology providing services of storage, sharing and processing of data through visualized and scalable resources over the networks [28], [29]. With the advantage of flexibility, storage, sharing and easy accessibility, Cloud Computing has a big role in Data Analytics process with the accent on Big Data, namely Big Data Analytics (BDA), as it offers access-based computing infrastructure oriented to subscription, data, and application services [12], [29].…”

“…Cloud Computing represents a computing technology providing services of storage, sharing and processing of data through visualized and scalable resources over the networks [28], [29]. With the advantage of flexibility, storage, sharing and easy accessibility, Cloud Computing has a big role in Data Analytics process with the accent on Big Data, namely Big Data Analytics (BDA), as it offers access-based computing infrastructure oriented to subscription, data, and application services [12], [29]. The goal of Cloud Computing is an emphasis on BDA that must use commodity hardware to build computing clusters and scale out the computing capacity for web crawling and indexing system workloads.…”

“…In that way itreducesthe distance that data must travel on the network while producing minimal delays [31], [32], [33], [34], [35]. The Edge Computing is perceived as a method of optimizing the Cloud Computing by performing Data Analytics as close to the data sources as possible [29].…”

An efficient indexing for Internet of Things massive data based on cloud‐fog computing

“…Cloud computing has become widespread and ubiquitous in recent years due to the variety of its Internet services, its large storage capacity and high processing power . Therefore, the cloud can cover the weakness of connected objects or sensors with limited storage and processing capacity, and improve systems performance with reduced costs.…”

“…Cloud computing has become widespread and ubiquitous in recent years due to the variety of its Internet services, its large storage capacity and high processing power. 2 Therefore, the cloud can cover the weakness of connected objects or sensors with limited storage and processing capacity, and improve systems performance with reduced costs. Despite all F I G U R E 1 Number of connected objects 1 F I G U R E 2 Fog computing architecture these advantages, cloud computing presents a high latency problem when transmitting raw and massive sensor data to the cloud due to the long distance between them, which negatively affects real-time applications such as video surveillance systems, patient monitoring systems and vehicle control systems, etc.…”