Edge computing represents an emerging paradigm in computing, bringing cloud computing services closer to end users to provide faster processing speeds and rapid application response.
Edge computing represents an emerging paradigm in computing, bringing cloud computing services closer to end users to provide faster processing speeds and rapid application response.
Modern internet-based applications, such as surveillance systems, virtual reality experiences, and real-time traffic monitoring, depend heavily on quick processing and low response latency. Typically, end users access these applications through their mobile devices, which have limited resources, while primary computing tasks are executed on remote cloud servers running on cloud computing infrastructure.
Cloud computing is a computing model that provides users with on-demand access to various computing resources, including storage and processing capabilities. The primary offerings of cloud computing encompass Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Each of these services enables users to conveniently access and utilize computing functionalities, such as data storage and processing, whenever required.
However, utilizing cloud services from mobile devices can introduce significant latency and complications related to mobility. Edge computing addresses these challenges by relocating computational tasks closer to the edge of the network, thus satisfying the demanding performance requirements of these applications.
Edge computing shifts data processing, applications, and services from centralized cloud servers to points closer to the users at the network’s edge. This enables content providers and developers to deliver their services more directly and efficiently to users.
Edge computing represents a refined version of cloud computing, designed specifically to lower latency by positioning services closer to end-users. By offering computing resources and services directly within the edge network, edge computing effectively reduces the workload placed on cloud infrastructure.
Nevertheless, it doesn’t replace cloud computing; instead, it enhances user experiences by supporting latency-sensitive applications. Similar to cloud providers, edge computing providers offer data storage, computation, and application services directly to users.
Despite these similarities, edge and cloud computing differ substantially in several ways:
Introduced by the European Telecommunications Standards Institute (ETSI), Mobile Edge Computing (MEC) allows mobile users to utilize the computing services from the base station.
MEC is a technology designed to give mobile users access to cloud services and other IT resources close to the Radio Access Network (RAN). Its primary aim is to reduce latency by relocating storage and computational resources from centralized networks to the network edge.
MEC provides a business-centric cloud computing platform integrated within the RAN, placing it near mobile users to effectively support delay-sensitive and context-aware applications.
MEC has emerged as a key advancement in cellular base station development, enabling cellular base stations to operate alongside edge servers seamlessly. Additionally, MEC provides real-time RAN data, such as network load, user location, and congestion status, to application and content developers.
Utilizing this real-time information, MEC facilitates context-specific services, enhancing user satisfaction and improving the overall Quality of Experience (QoE). By empowering the edge network to manage computational tasks and services locally, MEC significantly reduces latency and bandwidth usage.
Fog computing combines the strengths of both cloud computing and edge devices to deliver high-quality services, reduce latency, and support mobility, multi-tenancy, and various functionalities essential for modern computing systems.
A fog computing environment consists of three primary layers:
The terminal layer includes geographically distributed end devices responsible for collecting data and transmitting it to higher layers for processing and storage. Typical devices at this layer include sensors, wearable gadgets, mobile phones, and smart vehicles.
The fog layer, positioned between the terminal devices and the cloud, operates at the network edge.
Devices in this layer, called fog nodes, handle data storage, computation, and transmission tasks. Fog nodes can be either mobile or stationary, located strategically throughout the network. Common examples are routers, access points, switches, fog servers, and base stations.
Due to their computational capabilities, fog nodes enhance real-time data processing and analytics, optimizing services for latency-sensitive applications. Their close proximity to end devices enables efficient communication. Furthermore, by extending cloud resources into the fog layer, fog nodes gain additional computing and storage capacities.
The cloud layer contains powerful storage devices and servers capable of high-performance computing. It handles computational tasks that are not latency-sensitive and have been delegated from the fog layer.
The cloud provides infrastructure, platforms, and software services. Examples include server hosting services (IaaS and PaaS) from providers like DigitalOcean and Google App Engine, network storage such as Apple iCloud, and virtual IT solutions like Amazon EC2.
Cloudlets represent a form of edge computing designed to deliver cloud-based services and applications close to end users, typically positioned just one network hop away. They can operate independently and continuously offer services even if the central enterprise cloud becomes temporarily unavailable, synchronizing later when cloud connectivity is restored.
Cloudlets provide significant benefits, including:
This article provided an overview of edge computing, highlighting its key concepts, technologies, and application scenarios.
It discussed related paradigms such as cloudlets, fog computing, and mobile-edge computing (MEC), emphasizing their roles in addressing latency, connectivity, and data processing challenges.
Information presented here integrates insights from recent academic research to offer accurate, current perspectives. As edge computing technologies continue to evolve rapidly, ongoing research and development will likely refine and expand these concepts, further shaping the future landscape of distributed computing.
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