A look at how fog extends edge computing functionality in IoT and 5G ecosystems — and how it bridges the gap between the cloud and what needs to be analyzed locally. 
The role of fog computing as the necessary architecture to enable IoT, 5G and embedded AI is getting clearer, but questions remain about the linkage between fog and edge. The bottom line: Fog and edge are synergistic.
Fog is superset of Edge Computing.
At the OpenFog Consortium, we describe fog computing as bridging the continuum from cloud to things. It’s a continuum because fog overlaps cloud and things and fills the computing gap in between. Fog provides the missing link in what data needs to be pushed to the cloud, and what should be analyzed locally – at the edge. In the IT and IoT food chain, we describe it as edge is to fog, as apple is to fruit.
Although there is some overlap, a clear understanding of the relationship between fog and edge is necessary to design functional, efficient IoT and 5G architectures and to enable their productivity. In that spirit, here are 10 ways that fog architecture leverages and extends edge capabilities:
Although there is some overlap, a clear understanding of the relationship between fog and edge is necessary to design functional, efficient IoT and 5G architectures and to enable their productivity. In that spirit, here are 10 ways that fog architecture leverages and extends edge capabilities:
- Compute Distribution and Load Balancing. Many edge architectures employ a strategy of placing servers, apps or small clouds at the edge. Fog provides a broader system-level architecture that also incorporates tools for distributing, orchestrating, managing and securing resources and services across networks.This provides a balance of sophisticated computation, networking, and storage capabilities, and support for hetereogeneous environments on any node (e.g., CPUs, GPUs, FPGAs and DSPsfor compute).
- Hierarchical Networking. Edge is often optimized for a single type of network resource at the network edges, such as edge gateways, routers, switches, or licensed spectrum wireless networks. Fog supports a physical and logical network hierarchy of multiple levels of cooperating nodes, supporting distributed applications. Fog nodes extend the edge with support for north-south, east-west and diagonal connectivity, including interfaces between edge and cloud. This could include, for example, analytics algorithms distributed up and down a hierarchy of nodes, or massively parallel applications that concurrently run on large peer groups of processors, or highly distributed storage systems.
- Universal Orchestration & Management. Edge orchestration and management are sometimes derived from specific legacy vertical practices, such as mobile network orchestration managed by the carrier. In these situations, edge may deliver cloud capabilities but without orchestration for connecting edge nodes. Fog orchestration and management is intended to be more universal, modern, and automated. Fog orchestration enables resource pooling and permits interactions and collaborations between fog nodes at the same layer and at different layers in the hierarchy, which helps performance, fault tolerance, load distribution and load balancing. Fog network managementconsiders a life-cycle management through a distributed service orchestration layer in each fog node. The fog architecture essentially validates IT (information technology), OT (operational technology) and CT (communications technology) approaches.
- Modular Architecture with Multiple Access Modes. Edge deployments are typically based on gateways with fixed functionality. Edge architectures favor one specific access network, such as either wireless or wireline. Fog has a highly modular hardware and software architecture, permitting every fog node to be equipped with exactly the resources its applications need, that can be dynamically configured. Fog embraces both the licensed and unlicensed wireless spectrum, as well as copper or fiber wireline modes.
Side by side view of edge and fog architectures. Edge runs specific applications in a fixed logic location and provides a direct transmission service without data analysis. Fog works with edge to run applications in a multi-layer architecture that decouples and meshes the hardware and software functions, allowing for configuring / reconfiguring for different applications while performing intelligent transmission services with computing/storage/communication capabilities along the cloud to things continuum.
- Reliabilty and Resilency. Fog architectures are inherently reliable, supporting many fault tolerance, network resiliency, and fully autonomous emergency operation scenarios.If an edge devices goes down, the services it supports will often fail.
- Security and Privacy. By virtue of its vertical application-specific and multi-vendor nature, edge may offer uneven security protection. Fog, on the other hand, requires every fog node to include a high-assurance implementation of its Trusted Computing Base using secure hardware or hardware-supported security mechanisms and a mandatory mission-critical class protection of communication and computation security. Fog also requires the Trusted Execution Environments instantiated in the fog nodes within the same service domain, to form a distributed trusted computing platform enforcing a common set of information security and privacy policies. This distributed platform can provide on-demand security services to resource-constrained devices associated with the fog as well as offer trustworthy information processing, storage and transport throughout the device-fog-cloud continuum.
- Virtualization Support. Fog supports virtualization and uses enterprise and web-scale models. This provides hardware virtualization at each node level and allows loads to be moved from one node to an adjacent node if the node is down or overloaded. Edge computing looks at virtualization mainly from the perspective of distributing computing resources in a local manner per server.
- Workload Agility. Edge architectures are not particularly agile in environments with stringent or highly dynamic performance requirements. Fog is architected from the ground up to be a horizontal platform, capable of serving applications from all vertical markets and also applications that may fall between or span across multiple markets. As such, fog is designed to be highly agile, allowing applications to tailor their performance attributes to their specific needs. Application workloads can be moved up or down the hierarchy, and scale the degree of parallelism to exactly match the requirements, without wasting resources unnecessarily.
- Application Right-Sizing and Scalability. Fog nodes and fog networks can be right-sized for their applications more precisely than edge nodes, which are often stripped-down cloud servers. Edge devices scale by adding more compute resources at a given location, almost like a mini-cloud, which can be problematic when scaling to support networks supporting millions of things. Fog is capable of dynamically moving computation, networking or storage tasks up and down levels of a hierarchy, or across peer nodes on the same level, or between the cloud and fog. It also enables resource pooling for enhanced scalability. This can help fog extend the capabilities of edge for IoT applications that have environmental, power, size, or weight constraints.
- Mobile IoT Application Support. Fog delivers strong support for mobile IoT elements and applications; mobile fog nodes can be found on vehicles, drones, pedestrians, livestock, or portable devices and sensors of various kinds. Fog nodes compute & communicate on devices in motion; infrastructure fog nodes can provide the first line of network functions; and deeper nodes can provide more core network functions. Through the fog hierarchy, the nodes can be orchestrated at a system-level view. This can greatly reduce the bandwidth that must be transmitted deeply in the network. Edge typically doesn’t support this degree of network element placement diversity, limiting its use in remote and bandwidth constrained applications.
Why does this matter?
It’s clear that edge and fog are complementary. It is time for us to move the conversation from “how are they different”to “how should they work together” to enable advancements in industry
For fog architects, the answer lies in where, when and how to distribute computation, communication, control and storage along the continuum from cloud to things – not only at the edge but from where data is generated to where communications, decisions, monitoring and actions take place.
At the OpenFog Consortium, our technical working groups are addressing all aspects of this continuum. The OpenFog Reference Architecture (RA), released in February 2017, provides an overview of system architectures for fog nodes and networks, and lends insight into fog-edge collaboration.
Our goal is to enable all of the architectural benefits of the cloud and to leverage the superset of elements in the fog continuum. If an implementation is run on pure edge, it may be reliant to a commodity gateway market. Fog architecture opens up a much richer architectural canvas: It’s an architecture that potentially can extend hardware and software leaderships well into the future of IoT and 5G.
FAQs
How fog computing is related to edge computing? ›
Fog computing is a compute layer between the cloud and the edge. Where edge computing might send huge streams of data directly to the cloud, fog computing can receive the data from the edge layer before it reaches the cloud and then decide what is relevant and what isn't.
What is a benefit of edge fog computing? ›There are four broad benefits to both fog and edge computing: Reduced latency and improved response time. The time needed to move large volumes of data is dramatically reduced or even eliminated, enabling better time to value for large data sets.
What is fog computing give 3 advantages of utilizing it? ›Fog computing reduces the volume of data that is sent to the cloud, thereby reducing bandwidth consumption and related costs. Improved response time. Because the initial data processing occurs near the data, latency is reduced, and overall responsiveness is improved.
Is fog computing a part of edge computing? ›Fog computing is a computing layer between the cloud and the edge. Edge computing can send large data streams directly to the cloud. Fog computing, on the other hand, can receive data from the edge layer before it reaches the cloud. Then, only relevant data is stored in the cloud.
What is the correlation between edge and fog computing architectures? ›Both fog computing and edge computing involve pushing intelligence and processing capabilities down closer to where the data originates—at the network edge. The key difference between the two architectures is exactly where that intelligence and computing power is placed.
What is edge fog cloud computing? ›Fog computing – a term created by Cisco – refers to extending cloud computing to the edge of an enterprise's network. It pushes intelligence down to the local area network (LAN) level of network architecture, processing data in a fog node or IoT gateway.
What are the two most useful benefits of edge computing? ›Edge computing provides more data security and privacy protection because data is processed within the edge rather than from central servers. However, this does not suggest that edge devices are not vulnerable by any means.
What are some of the benefits of using edge storage? ›- Quicker response times.
- Greater security and privacy.
- No data storage fees.
Benefits of Fog
It may not be obvious, but fog does have some environmental benefits. In coastal areas such as the sequoia forests in California, ferns, trees and other plants have adapted to “collect” the small water droplets in fog for their water needs.
The cloud delivers more flexibility and reliability, increased performance and efficiency, and helps to lower IT costs. It also improves innovation, allowing organizations to achieve faster time to market and incorporate AI and machine learning use cases into their strategies.
Which is better edge or fog computing? ›
Edge computing can process data for business applications and transmit the results of these processes to the cloud, making Edge computing possible without fog computing. On the other hand, Fog computing cannot produce data, making it inoperative without Edge computing.
What is the difference between fog and edge computing? ›With a name coined from meteorological origins, fog computing focuses on the space of data area between the source and the cloud. Edge computing, on the other hand, centers where data is collected. The key difference between edge and fog is in the location of intelligence and computing power.
What is an example of edge computing? ›Edge computing is already in use all around us – from the wearable on your wrist to the computers parsing intersection traffic flow. Other examples include smart utility grid analysis, safety monitoring of oil rigs, streaming video optimization, and drone-enabled crop management.
What are the main features of edge computing? ›- High performing data communication to/from the highly distributed Edge environment. Edge environment and infrastructure performance monitored to support the end-to-end IT service delivery metrics. ...
- Edge equipment fully supported without requiring customization. ...
- High availability, no single point of failure.
In that case, some of the standard edge types include sensor edge, device edge, router edge, branch edge, local area network edge, enterprise edge, datacenter edge, cloud edge, and mobile edge.
What is edge and fog computing examples? ›A smartphone connected to a cloud network is an example of an edge computer. Fog computing is more like a 'gateway' of intelligence and processing power. A fog computer connects to a batch of edge computers simultaneously, thus creating a localized network of devices for more efficient data processing and storage.
Why fog and edge computing are necessary in Internet of things? ›Fog/edge computing enables IoT applications to improve the scalability and energy efficiency of IoT systems, exploit computational node resources to analyse the collected data, and meet latency requirements.
Which are some of the key factors driving edge computing? ›- Latency. More industries are implementing applications that require rapid analysis and response. ...
- Bandwidth. Adding transmission bandwidth or more processing power could overcome latency issues. ...
- Security and privacy. ...
- Connectivity. ...
- AI.
Therefore, fog computing features three layers: end devices, fog nodes, and clouds. End devices have small capacity but they are present in huge numbers. Fog nodes offer higher, but still limited capacity, and they are less numerous. Finally, clouds offer large capacity, while their number is low.
What is an example for fog computing? ›The most prevalent example of fog computing is perhaps video surveillance, given that continuous streams of videos are large and cumbersome to transfer across networks. The nature of the involved data results in latency problems and network challenges. Costs also tend to be high for storing media content.
What are the advantages of fog computing over cloud computing? ›
Fog provides low latency; cloud — high latency. A cloud system collapses without an Internet connection. Fog computing uses various protocols and standards, so the risk of failure is much lower. Fog is a more secure system than the cloud due to its distributed architecture.
What are the impacts of edge computing? ›Edge computing further enhances resiliency by reducing a central point of failure -- as is the case with centralized servers; a failure at one edge device won't affect the performance of other edge devices in the ecosystem, thereby improving the reliability of the entire connected environment.
How can edge computing be used to improve? ›Edge computing helps optimize energy usage by reducing the amount of data traversing the network. In addition, by running applications at the user edge, data can be stored and processed close to the end user and their devices instead of relying on centralized data centers that are often hundreds of miles away.
What are the uses of edge computing? ›- Autonomous vehicles. ...
- Remote monitoring of assets in the oil and gas industry. ...
- Smart grid. ...
- Predictive maintenance. ...
- In-hospital patient monitoring. ...
- Virtualised radio networks and 5G (vRAN) ...
- Cloud gaming. ...
- Content delivery.
Edge computing allow data from Internet of things device to be analysed edge of network before being send to a data center or cloud. Advantages : It offers high speed, reduced latency better reliability which allows for quicker data processing and content delivery.
What are the pros and cons of edge computing? ›- Response Time and Latency. A company's every millisecond is critical to its success. ...
- High Security and Less Risk. The data stored in the cloud has a high risk of being hacked. ...
- Lesser Transmission Costs. ...
- Scalability and Versatility. ...
- Infrastructure costs. ...
- Data Loss.
Edge computing decreases the vulnerable attack surface of applications by terminating connections to these devices locally and putting more typical encryption and access security protection on the link between the edge and the cloud or data center.
What are the 5 main types of fog? ›- A. Fog Types. ...
- (1) Advection fog. ...
- (2) Radiation fog (ground or valley fog). ...
- (3) Upslope fog (Cheyenne fog). ...
- (4) Steam fog (arctic sea smoke). ...
- (5) Frontal fog. ...
- (6) Ice fog. ...
- B.
Fog lights are designed to aid visibility when bad weather conditions reduce your ability to see the road ahead. Front fog lights can cut through mist, fog, rain or even dust as they are mounted lower down than headlights in the front of the car.
What are the 4 types of fog? ›There are several different types of fog, including radiation fog, advection fog, valley fog, and freezing fog. Radiation fog forms in the evening when heat absorbed by the Earth's surface during the day is radiated into the air.
What are the 10 10 key characteristics of cloud computing? ›
- Resources Pooling.
- On-Demand Self-Service.
- Easy Maintenance.
- Scalability And Rapid Elasticity.
- Economical.
- Measured And Reporting Service.
- Security.
- Automation.
Edge computing and fog computing allow processing data within a local network rather than sending it to the cloud. That benefit decreases latency and increases security.
Why edge computing is faster? ›Edge is about processing data closer to where it's being generated, enabling processing at greater speeds and volumes, leading to greater action-led results in real time. It offers some unique advantages over traditional models, where computing power is centralized at an on-premise data center.
Is fog computing and edge computing the same True or false? ›A fog environment places intelligence at the local area network (LAN). This architecture transmits data from endpoints to a gateway, where it is then transmitted to sources for processing and return transmission. Edge computing places intelligence and processing power in devices such as embedded automation controllers.
Is edge computing an extension of cloud? ›3. Edge is an extension of cloud and requires a common platform-based approach: Adding new technologies like edge to existing cloud platforms makes it much easier to manage and optimize applications.
Which technology is edge computing an extension of? ›Industrial edge computing can be seen as an extension of cloud computing. Cloud computing services are set-up and managed either by the user or by an external service provider and hosted on servers connected to the Internet.
What are some of the examples of device edge? ›An edge device is a device that provides an entry point into enterprise or service provider core networks. Examples include routers, routing switches, integrated access devices (IADs), multiplexers, and a variety of metropolitan area network (MAN) and wide area network (WAN) access devices.
Does Netflix use edge computing? ›Edge computing comes into play for really any app but especially for streaming services, such as Hulu or Netflix.
What is the difference between fog cloud and edge? ›The main difference between cloud, fog and edge computing is defined by where data from edge devices is processed and stored. Cloud servers are placed away from the edge, while fog is pulled closer to reduce the time needed to process data and respond to events faster.
Is dew computing the same as edge computing? ›The main difference between these two concepts is the location of the IoT device, if it is on the edge of the Internet network, then the solution is treated as an edge computing architecture, while if it is outside of the Internet perimeter edge, then it is a dew computing architecture [20].
What is called the relationship between edge computing and cloud computing? ›
Edge computing is a subsection of cloud computing. While cloud computing is about hosting applications in a core data centre, edge computing is about hosting applications closer to end users, either in smaller edge data centres or on the customer premises instead.
What is edge computing examples? ›Edge computing is already in use all around us – from the wearable on your wrist to the computers parsing intersection traffic flow. Other examples include smart utility grid analysis, safety monitoring of oil rigs, streaming video optimization, and drone-enabled crop management.
Why fog is better than cloud? ›Fog provides low latency; cloud — high latency. A cloud system collapses without an Internet connection. Fog computing uses various protocols and standards, so the risk of failure is much lower. Fog is a more secure system than the cloud due to its distributed architecture.
What are 3 reasons that drive edge networking? ›- Bandwidth. The first reason for edge computing is bandwidth. ...
- Cost. ...
- Reliability. ...
- Security. ...
- Compliance. ...
- Latency.
This small storage and computation of data before sending it over to the cloud is fog computing. Fog computing involves the usage of devices with lower processing capabilities to share some of the cloud's load. The goal of fog computing is to use the cloud only for long-term and resource-intensive analytics.