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Distributed Cloud Edge Computing Network

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[Cloud, Fog, and Edge Computing - Imagimob]
 
 

 

- Distributed Cloud/Edge Networks

Edge or Network Edge is where the data resides and is collected. Scalability issues, excessive power consumption, connectivity and latency are some of the many factors that are driving the demand for edge infrastructure in the form of micro data centers or distributed computing architecture.

Cloud computing can be defined as a model for the provision and use of Information and Communication Technologies, which allows remote access over the Internet to a range of shared computing resources in the form of services. This computer system can be divided into two parts: frontend (client devices) and backend (servers); while the models for computing service delivery are divided into three: Infrastructure-as-a-Service (IaaS),  Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS).

Fog and edge computing are both extensions of cloud networks, which are a collection of servers comprising a distributed network. Such a network can allow an organization to greatly exceed the resources that would otherwise be available to it, freeing organizations from the requirement to keep infrastructure on site. The primary advantage of cloud-based systems is they allow data to be collected from multiple sites and devices, which is accessible anywhere in the world. 

 

- Edge + Cloud Architecture

The rapid development of mobile applications and Internet-of-things (IoT) paradigm-based applications has brought several challenges to the development of cloud-based solutions. These challenges are mainly due to the transfer of huge amounts of data to the cloud, high communication latencies, and the inability of this model to respond in some domains that require a rapid reaction to events. Cloud, fog, and edge computing may look very similar terms, but they have some differences, functioning as different layers on the IoT horizon that complement each other. 

Edge computing is a distributed computing paradigm which brings computer data storage closer to the location where it is needed. In contrast to cloud computing, edge computing refers to decentralized data processing at the edge of the network. The industrial Internet requires more of an edge-plus-cloud architecture rather than one based on purely centralized cloud; in order to transform productivity, products and services in the industrial world.

 

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(Hong Kong)

- Multicloud, Fog, and Edge Computing Architectures

Today, multicloud strategy - in which enterprises use public, on-premises private clouds and hybrid models - has become the most assured path to cloud success. Multicloud also applies to high-bandwidth applications and devices as well. They will increasingly benefit from edge computing architectures. The growth of new technologies such as 5G wireless technologies necessitate multicloud approaches, including edge computing architectures. 

Edge computing brings cloud resources - compute, storage and networking - closer to applications, devices and users. It does by using small power cell stations to enable data to travel at high speeds - without having to travel long distances to a cloud or data center. With edge computing architecture, complex event processing happens in the device or a system close to the device, which eliminates round-trip issues and enables actions to happen quicker.  

The trend in edge computing is to bring machine learning, artificial intelligence, Internet of Things (IoT) data processing, the ability to run containers, and even the ability to run full virtual machines directly into a wide range of devices. These devices may be as small as a camera or as large as full compute racks for complex processing. Regardless of the size and capabilities of the device, the software on these devices is connected to the cloud in some form.  

 

- Edge Computing Will Augment Cloud Computing

However, we are not looking at a complete shift. In a similar way that cloud computing has not and will not fully replace centralized data centers, edge computing will augment rather than replace cloud computing. The new paradigm of “processing anywhere“ means that data will be processed where it originates and ingested into workflows aligning with business requirements.

Edge computing will forever alter how businesses interact with the physical world. Whether you consider it revolutionary or evolutionary, it is well on its way to mainstream adoption. Edge computing provides compute and storage resources with adequate connectivity (networking) close to the devices generating traffic.

Due to the increased data collected, both the physical environment of the edge (i.e., processing power in devices), and the virtual capacities (i.e., software partitioned computing machines deployed within purpose-built edge hardware like routers), device servers, terminal servers, and gateways, will evolve.

 

- AI on Edge Services and Applications

The unprecedented growth in edge computing will pave the way for a host of services and applications at the edge, that can be optimized with the application of artificial intelligence. While cutting-edge companies like Amazon, Apple and Tesla are already betting big on Edge AI, other companies are yet to embrace it fully. 

We saw the shift from mainframe to computers to cloud, now, the cloud is moving to Edge, and so is AI. But, it doesn’t imply that cloud is becoming irrelevant. No, it is relevant, in fact, disruptive technologies like IoT will act as the smart extensions of cloud computing. And now, AI on Edge, can offer a whole lot of new possibilities. In Edge AI, the AI algorithms are processed locally on a hardware device, without requiring any connection. It uses data that is generated from the device and processes it to give real-time insights in less than few milliseconds.

Does Edge AI really exist? Yes, definitely. Say for an example, your iPhone has the ability to register and recognize your face to unlock your phone in fractions of seconds. A more complex example would include self-driving cars, where the car drives on its own. In both the examples, we see that complex algorithm are used to process data right there in the car or in your phone, because there’s no time sending this data to the cloud, process it and wait for the insights.

 

 

 [More to come ...]

 

 

 

 

 

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