Everything about Edge Computing

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The most mature view of edge computing is that it is offering application developers and service providers cloud computing capabilities, as well as an IT service environment at the edge of a network. The aim is to deliver compute, storage, and bandwidth much closer to data inputs and/or end users.

According to International Data Corporation (IDC) predicts global data will reach 180 zettabytes (ZB), and 70% of the data generated by IoT will be processed on the edge of the network by 2025. IDC also forecasts that more than 150 billion devices will be connected worldwide by 2025.

Cloud vs Fog vs Edge

  • Edge computing and cloud computing are not substituted relationships; rather, they are complementary. Edge computing needs powerful computing power and massive storage support of a cloud computing center, and the cloud computing center also needs the edge computing model to process massive data and privacy data
  • Fog computing is defined as a decentralized infrastructure for computing which outlines the most efficient and logical distribution of networking services, compute and storage between the data source and cloud computing
  • While in an edge computing environment, the computing occurs at the devices itself, in case of fog environment the computing takes place in a local area network


  • Reducing latency: The latency to the end user could be lower than it would be if the compute was farther away—making, for instance, responsive remote desktops possible, or successful AR, or better gaming.
  • Mitigating bandwidth limits: The ability to move workloads closer to the end users or data collection points reduces the effect of limited bandwidth at a site. This is especially useful if the service on the edge node reduces the need to transmit large amounts of data to the core for processing, as is often the case with IoT and NFV workloads. Data reduction and local processing can be translated into both more responsive applications and reduces the cost of transporting terabytes of data over long distances.
  • Data Collection and Analytics: taking the analytics closer to the source of the data on the edge can be more cost-effective by analyzing data near the source and only sending small batches of condensed information back to the centralized systems
  • Security: Edge computing offers the ability to move security elements closer to the originating source of attack, enables higher performance security applications, and increases the number of layers that help defend the core against breaches and risk
  • Compliance Requirements: Compliance covers a broad range of requirements, ranging from geofencing, data sovereignty, and copyright enforcement. Restricting access to data based on geography and political boundaries, limiting data streams depending on copyright limitations, and storing data in places with specific regulations
  • Network Function Virtualization (NFV): decoupling of network functions from proprietary hardware appliances and running them as software in virtual machines (VMs). The different functions — such as firewalls, traffic control, and virtual routing — are called virtual network functions (VNFs).
  • Real Time: Real-time applications, such as AR/VR, connected cars, telemedicine, tactile internet Industry 4.0 and smart cities, are unable to tolerate more than a few milliseconds of latency and can be extremely sensitive to jitter, or latency variation. In many scenarios, particularly where closed-loop automation is used to maintain high availability, response times in tens of milliseconds are needed, and cannot be met without edge computing infrastructure.


  • Smart Factory and Manufacturing: Edge computing solutions that retrieve data from otherwise non-connected diagnostic ports and user interfaces, convert it to a network stream, and transmit it across an industry-standard IP network can be used to quickly and easily extend legacy manufacturing devices with smart factory functionality
  • Networking, Telecommunications, and Mobile Edge Computing: Pushing content, applications and services to the edge via Multi-Access Edge Computing (MEC) can alleviate the burden placed on mobile networks and cloud data centers by increasing numbers of IoT devices. MEC places smart nodes at the edge of a mobile network to emulate parts of the core network, cache content and apps, and run edge-optimized apps
  • Military and Aerospace: Fog computing is a critical component in real-time operations, providing immediate local situational awareness through edge processing and allowing only the necessary information to be transmitted to the cloud in order to reduce latency and make real-time communication possible. Speed is ensured through a combination of edge processing and only sending necessary data and metadata into the cloud. Security at the edge is fully tamper-proof and must remain so through multiple levels of networks and clouds.
  • Autonomous Vehicles: Self-driving cars need to be able to learn things without having to connect back to the cloud to process data
  • Augmented reality (AR) and virtual reality (VR): Edge computing addresses those obstacles by moving the computation into the cloud in a way that feels seamless. It’s like having a wireless supercomputer follow you everywhere.

Other Notes

  • Cloud computing has many advantages, yet it is still essentially a hub and spoke model that is deeply reliant on network connectivity. This architecture cannot completely meet business IoT needs. The rapid and massive expansion of scale of IoT devices at the network edge threatens to push centralized computing resources and networks themselves to the breaking point.
  • New requirements for availability and cloud capability at remote sites are needed to support both today’s requirements (retail data analytics, network services) and tomorrow’s innovations (smart cities, AR/VR)
  • Organizations have an emerging need to take cloud capabilities across WAN networks and into increasingly smaller deployments out at the network edge
  • By moving some or all of the processing functions closer to the end user or data collection point, cloud edge computing can mitigate the effects of widely distributed sites by minimizing the effect of latency on the applications
  • Sales of so-called micro-modular data centers (MMDCs) may reach nearly $30 million this year, up from $18 million in 2017



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