Explore the latest trends in Cloud, Edge and Fog computing.

"Fog computing is a computing 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. The relevant data gets stored in the cloud, while the irrelevant data can be deleted or analyzed at the fog layer for remote access or to inform localized learning models."

https://www.onlogic.com/company/io-hub/edge-computing-vs-fog-computing/#:~:text=Fog%20computing%20is%20a%20compute,relevant%20and%20what%20isn't.

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Answer:

Cloud computing was popular and well-established pre-pandemic as well but it has taken the spotlight as a move to remote working was thrust upon us. And it looks like it’s here to stay for a long time. Cloud technologies are moving away from a linear evolution to prepare for an exponential evolution. Now, as we look for recovery, the appetite for cloud solutions is still strong and ever growing. This nudges more organizations around the world to move towards a cloud-first strategy and as they do, we can expect to see new cloud computing trends in 2022.

1. Edge computing

2. Serverless functions

3. Kubernetes enabling blockchain

4. AI in cloud computing

5. The rise of cloud-gaming

6. Hybrid cloud and multi-cloud infrastructure

Edge computing trends play a key role in business because edge deployments are now essentially everywhere. With the list of edge computing devices growing – including smartphones, smartwatches, and autonomous vehicles – at an exponential rate, business professionals need to stay current with edge trends moving into 2022.

1) IoT

The Internet of Things (IoT) is easily the fastest growing umbrella of edge computing devices.

IoT devices include:

• Smart appliances
• Smartphones
• Wearables
• Gaming systems
• Printers

2) Customer Experience

3) Security

4) Edge Computing in Healthcare

5) Workplace Safety in Energy

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. Nevertheless, a series of challenging problems need to be addressed in order to fully utilize fog/edge computing for IoT. For instance, the majority of computational nodes in fog/edge computing are battery-operated, which means that fog/edge computing tends to be unreliable. Moreover, efficiently providing computing resources to latency-sensitive IoT applications (such as AR/VR, haptic technology, intelligent manufacturing, connected autonomous driving, and others), and seamless integration of fog/edge computing with cloud computing to provide scalable services needs to be further studied.

Potential topics include but are not limited to the following:

• Fog/Edge computing architectures and protocols for AR/VR
• Resource allocation in Fog/Edge computing for IoT
• Energy management techniques in mobile AR/VR devices using Fog/Edge computing
• Low-latency, Ultra-high reliability, and Ultra-responsive network protocols for haptic communications and AR/VR
• IoT application placement and offloading in Fog/Edge computing
• Workload balancing and scheduling in Fog/Edge computing for IoT
• Green Fog/Edge computing
• Fog/Edge computing applications in connected autonomous vehicles, Industry 4.0, smart homes, and smart cities
• Modelling and performance analysis of Fog/Edge computing for IoT
• The collaboration of cloud computing and Fog/Edge computing for IoT
• Mobility management in Fog/Edge computing for IoT
• Open issues and challenges in Fog/Edge computing for IoT
• Transmission and communication strategies in Fog/Edge computing for IoT