Edge computing and Fog computing: When to leverage a thought

Introduction

Over past few years IoT has gained a lot of momentum, with the explosion of data, devices and interactions. IT Cloud on its own can't handle the influx of information. The Cloud does provide us access to compute, storage and even connectivity that we can access easily and cost-effectively. These centralized resources can create delays and performance bottlenecks for devices and data that are far from a centralized public cloud or data center regions.

Edge computing also referred to as “edge”, brings processing close to the data source. Edge computing pushes the intelligence, processing power and communication capabilities of an edge gateway or appliance directly into devices. In most cases the data does not need to be sent to a remote cloud or other centralized systems for processing. By eliminating the distance and time it takes to send data to centralized sources one can improve the speed and performance of data transport, as well as devices and applications on the edge.

Fog computing pushes intelligence down to the local area network level of network architecture, processing data in a fog node or IoT gateway. It’s a standard that defines how edge computing should work, and it facilitates the operation of compute, storage and networking services between end devices and cloud computing data centers.

Possible when to use one or both

It is the required response to data gathered that determines which computing or networking method best meets the need.

Reinforcement machine learning

Reinforcement learning is all about taking suitable action to maximize reward in a particular situation. In such situation it would really be useful to process the data at closest proximity, rather than send it over the network. This is where edge computing will be very useful.

Optionally fog network could later be involved in perhaps deep data analyzing or visualizing and learning from past data.

Latency sensitive applications

Audio/Video calling, augmented reality and virtual reality applications would greatly benefit by processing the data at closest proximity (edge computing), this will improve user experience when using such applications. 

Unreliable Networks Connection

Unreliable Networks connection could be unavoidable circumstances or intentional for security reasons such as network isolation. When the network is isolated or disconnected from the production Cloud. An edge computing device could take care of about 90% of most required IoT-based processing and perhaps could only send critical info to main centralized processing Cloud for archiving or auditing.

Distributed Ledger technologies

Edge computing is well suited for Distributed ledger technology like blockchain that requires decentralized computing models. Perhaps for performing mining and verifying type of operations the Fog network could be leveraged to bring in High Performance Compute. 

Data Filtering

This a scenario where the sensors are sending large amounts of data, most of it is good to have data however not always relevant or required data for current goal. In this case Edge computing node can serve as IoT data filtering gateway and reduce the so called “noise” in data before it’s sent over the fog for relevant usage. Some of this noise data is retained for a while on edge devices local storage before it purged from edge compute nodes.

Summary

Eliminating the limits of centralized cloud infra equates to IoT being much more distributed and flexible in the services that can be offered. Fog and edge are enabling technologies and standards that give IoT users and technology providers with more options. Understanding Fog and edge computing is the key. The next step is to leverage Edge and Fog computing in solutions to address your business needs. This way edge and fog computing can make end user’s lives better