Cloud, Fog, and Edge Computing Infrastructures
- (Industrial IoT Data Processing Layer Stack - Winsystems)
- Data Center/Cloud Layer
Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. Large clouds, predominant today, often have functions distributed over multiple locations from central servers. If the connection to the user is relatively close, it may be designated an edge server.
With the huge interest in digitalization across all industry verticals - 5G is a key technology. Edge computing or Mobile Edge Computing, is a crucial part of the 5G platform and provides a first-mover advantage for communication service providers in grabbing new business opportunities. By 2023, 5G will make up around one-fifth of all mobile data traffic, where 25% of the use-cases will depend on edge computing capabilities.
Cloud computing is naturally combined with mobile devices to enable the active functionalities. Mobile cloud computing is a combination of mobile computing and cloud computing. Mobile cloud computing is the most influential section of cloud computing and it's expected to expand the mobile ecosystem. Mobile devices evolved from voice calls enabled devices to smart devices which enabled the user to access services at anytime from anywhere. The main aim of mobile cloud computing is to provide rich mobile applications with rich user experience of mobile devices.
- [Lower Manhattan - onetwotwee]
- Fog Layer
Fog computing is a new computing mode. As a derivative of cloud computing, fog computing can solve the problems of high latency, overloaded center server and overloaded bandwidth of network. The term fog computing was coined by Cisco in 2014. In nature, fog is closer to the earth than clouds. In the technological world, it is just the same, fog is closer to end-users, bringing cloud capabilities down to the ground. Fog is the extension of cloud computing that consists of multiple edge nodes directly connected to physical devices.
Fog is the extension of cloud computing that consists of multiple edge nodes directly connected to physical devices. Such nodes are physically much closer to devices if compared to centralized data centers, which is why they are able to provide instant connections. The considerable processing power of edge nodes allows them to perform the computation of a great amount of data on their own, without sending it to distant servers.
Fog can also include cloudlets - small-scale and rather powerful data centers located at the edge of the network. Their purpose is to support resource-intensive IoT apps that require low latency. The main difference between fog computing and cloud computing is that cloud is a centralized system, while the fog is a distributed decentralized infrastructure.
Realizing fog computing and networking imposes many new challenges. For example, how to compose, deploy, and manage distributed fog services, how to enable highly scalable and manageable fog networking and computing, how to secure fog computing systems, how should the fog interact with the cloud, and how to enable users to control their fog services provided by fog operators. Addressing these challenges necessitates rethinking of the end-to-end network and computing architecture. " -- [FOG WORLD CONGRESS]
- Edge Layer
Edge computing refers to computing happening at the edge of a network. Various access points define the network edge, hence the name for its architectural standard, Multi-access Edge Computing (MEC). Multi-access edge computing (or Edge Computing) is essentially a cloud-based IT service environment at the edge of the network. Edge computing is a network architecture that brings real-time, high-bandwidth, low-latency access to radio network information, allowing operators to open their networks to a new ecosystem and value chain. Edge computing permits multiple types of access at the edge, including wireline. Edge access points include cell phone towers, routers, WiFi, and local data centers.
"Edge computing is the conversion of IoT data to usable information using microprocessors collocated with the sensor, or at the edge of the network. Edge computing reduces network bandwidth, data storage, and analysis requirements. The price is increased power at the mobile device, requiring innovations in energy harvesting and storage. Innovations in edge computing will accelerate new developments across a wide array of applications." -- [IEEE Computer Society]
The benefit of edge computing is the ability to provide new services with high requirements on e.g. latency or on local break-out possibilities to save bandwidth in the network – data should not have to travel far in the network to reach the server. Regulatory compliance and network scalability are also important edge computing drivers.
"There are several interesting use cases related to manufacturing, automotive, and the media and entertainment industries for edge computing and distributed cloud. As an example - the automotive industry with connected vehicles is an industry demanding edge computing. The creation and distribution of advanced maps with real-time data, and advanced driving assistance using cloud-based analytics of video streams are all examples of emerging services." -- [Ericsson]
- Things, Sensors & Controllers (Data Origination) Layer
As the basis for every IoT system, connected devices are responsible for providing the essence of the Internet of Things which is the data. To pick up physical parameters in the outside world or within the object itself, they need sensors. These can be either embedded in the devices themselves or implemented as standalone objects to measure and collect telemetry data. For an example, think of agricultural sensors whose task is to measure parameters such as air and soil temperature and humidity, soil pH levels or crop exposure to sunlight.
Another indispensable element of this layer are the actuators. Being in close collaboration with the sensors, they can transform the data generated by smart objects into physical action. Let’s imagine a smart watering system with all the necessary sensors in place. Based on the input provided by the sensors, the system analyses the situation in real time and commands the actuators to open selected water valves located in places where soil humidity is below the set value. The valves are kept open until the sensors report that the values are restored to default. Obviously, all of this happens without a single human intervention.
What is also important is that the connected objects should not only be capable of communicating bidirectionally with their corresponding gateways or data acquisition systems, but also being able to recognise and talk to each other to gather and share information and collaborate in real time to leverage the value of the whole deployment. In case of resource-constrained and battery-operated devices particularly, achieving this is not an easy task since such communication requires lots of computing power and consumes precious energy and bandwidth. Therefore, a robust architecture can only enable effective device management when it uses fit-for-purpose, secure and lightweight communication protocols, such as Lightweight M2M which has become a leading standard protocol for the management of low power lightweight devices which are typical for many IoT use cases
With rapid technological advancements within the domain of Internet of Things (IoT), strong trends have emerged which indicate a rapid growth in the number of smart devices connected to IoT networks and this growth cannot be supported by traditional cloud computing platforms.
In response to the increased capacity of data being transferred over networks, the edge and fog computing paradigms have emerged as extremely viable frameworks that shift computational and storage resources towards the edge of the network, thereby migrating processing power from centralized cloud servers to distributed LAN resources and powerful embedded devices within the network. These computing paradigms, therefore, have the potential to support massive IoT networks of the future and have also fueled the advancement of IoT systems within industrial settings, leading to the creation of the Industrial Internet of Things (IIoT) technology that is revolutionizing industrial processes in a variety of domains.
