What are the best practices for designing edge and fog computing?

Best practices for designing edge and fog computing involve optimizing network bandwidth, prioritizing data processing at the edge, ensuring security measures, implementing redundancy, and leveraging edge-specific hardware for efficient and decentralized processing.

Totally agree on defining the use case upfront. In my experience, understanding the data flow and processing needs early on was crucial. It helped tailor the architecture precisely, balancing performance with cost. The initial groundwork in identifying the right tech and components paid off big time in the long run.

Prior to embarking on the design of an edge or fog system, a meticulous definition of the use case and its requirements is imperative. Considerations such as data sources, types, and volumes, processing and communication needs, performance and reliability goals, and security and privacy risks should be thoroughly addressed. This meticulous analysis enables the identification of the most fitting architecture, components, and technologies for the system, ensuring alignment with the specific demands of the intended application.

So basically, if we understand fog computing or fog systems. We can understand that it is basically a decentralized networ. We’re systems come together in farm great neural challenges. Looks like the Godfather they are. I would understand that his machines may use the fog system decentralized learning process. As an example! I defined my case. Now I rest! So my conclusion blockchain methodology for security and organizational reasons would be a good applicant upon the needed bottlenecks in the fog machine/process of computing power methodology. For those who don’t know fog computing is basically l.e.d. By l.e.d. Nueral networks…that’s all. I hope u understand. Yes! I’m communicative most people don’t have this gift! “Gift of Gab”!!!

More than even defining the use case, you need to identify which "edge" you are designing for - Network, Telco, or Compute, as they are all significantly different. Architecture designed for Network edge has precious little in common with edge compute on a factory floor or on board a ship

Often, engineers forget that there is a dearth of skilled technical talent at the far edge. There arent many VCP's and CCNe's in the oil field or on the factory floor. To be successful, automation of both platform and workloads is the sole path, and that means abstracting away everything above the physical hardware. This is where an entire HCI stack that fits on tiny edge computing devices really shines. Bringing datacenter levels of high availability and fault tolerance into an AI and ML driven underlying physical architecture with total automation as a primary focus is absolutely critical, regardless of whether the workloads in question are VMs or containers.

Selecting the appropriate level of abstraction is pivotal in implementing effective edge and fog computing solutions. Tailor your approach based on the intricacy and diversity of tasks and devices. Opt for device-level edge computing for straightforward local tasks like sensor data filtering. Employ node-level edge computing for collaborative endeavors such as data aggregation. Utilize network-level edge computing for distributed tasks like load balancing, and resort to cloud-level fog computing for centralized global tasks including data storage and management. This nuanced selection ensures optimal alignment with the specific requirements of each computing tier.

Picking the right level of abstraction is crucial. I remember a project where we chose node-level edge computing for data aggregation, and it worked out well with the diverse devices we had. It really matched the task complexity, making the operations smoother. The right level can indeed significantly impact efficiency and scalability

That is very pleasant technology for support us, the more flexible system with AI , to answer the dynamic development. That we know, technology must be easy not make people frustation to learn new technology. It must interesting, and direct impact daily.

Efficient resource allocation is a pivotal challenge in edge and fog computing. Striking the right balance among devices, nodes, networks, and clouds requires careful consideration of latency, bandwidth, energy, cost, and quality of service. Account for the diverse nature, variability, and mobility of resources by employing techniques like resource discovery, scheduling, allocation, migration, and adaptation. This strategic optimization ensures a harmonious synergy, enhancing the overall performance and responsiveness of your edge and fog computing infrastructure.

In a project I worked on, managing resources was key. Balancing latency, bandwidth, and cost wasn't easy, but using techniques like scheduling helped. Getting this balance right really impacts efficiency and service quality

As we know and understand, there is not unlimited resources. We must pick the managable resources and dynamic resources that we can't handle. The on hand resources should be maximize and optimise in use and collaboration. The other un managable resources, we could use as back up resources, and offcourse maximise them. With this system management, resources could be handle better

Guaranteeing system scalability in edge and fog computing is crucial, particularly with evolving device, node, network, and cloud dynamics. Design your system to adeptly manage dynamic and unpredictable workloads and resource fluctuations, ensuring both reliability and fault tolerance. Implement techniques like modularization, decentralization, replication, and load balancing to fortify your system's resilience in the face of varying demands and resource scenarios. This approach establishes a robust foundation for sustained performance in the dynamic landscape of edge and fog computing.

That is, important thing, scalability and could imitating for new working units or even for others. But do not forget, the more big and complex a system, the more hard to maintain, and in the force major condition, the system must be still working even in the minimum support.

Scalability was a focal point in a project I was involved in. As devices multiplied, the workload challenge ramped up. Using modularization and load balancing proved helpful. They helped in maintaining reliability even as the demands increased

Edge computing flips the traditional thought processes on scalability on it's head. At the compute edge, it is far less about "adding more nodes" at each location and far more about flexibility and lifecycle. Making sure that adding resource and growing with endpoint needs has to both be made simple, remote-enabled, and not be boat anchored to the initial hardware implemented. It has to be able to easily "Scale In" with new hardware of different generations without rolling a truck or downtime

Encryption and authentication of not just people but accessing devices is table stakes at the edge and is absolutely mandatory when you are talking about devices that can physically fit in a pocket.

Securing your edge and fog computing system is paramount due to the diverse entities involved, each susceptible to potential attacks or breaches. Safeguard your system against unauthorized access, data manipulation, or service disclosure, prioritizing integrity and availability. Employ advanced techniques like encryption, authentication, authorization, auditing, and anomaly detection to fortify your system's security posture. This comprehensive security approach ensures robust protection against evolving threats, establishing a foundation of trust for your edge and fog computing ecosystem.

Spot on regarding security. From my experience, encryption and authentication are non-negotiable. With a myriad of devices and networks involved, keeping unauthorized access at bay is a daily grind

Security and safety for system, could be divide in three major item : hardware, software (coding) and culture of people in that system. Hardware and software are easy to create, but culture is not so easy. But culture could be the most strong barrier from the attacks. Do you understand what I mean?

Having been working in the Embedded / Edge space long before it was a buzz word - I can wholeheartedly say that one must prepare and plan for fleet management and factor that into whatever they build. (before they deploy) Applications evolve. Operating systems get patched. Data management / Data loss prevention. Remote recovery. Telemetry. And one can never forget that the solution must be flexible enough to evolve around new security threats. So having a rock solid methodology built-in to the solution, will definitely add to the longevity and ROI of the solution.

One of the biggest considerations for edge implementations is that the edge is not the monolithic data-center or the cloud. It is thousands of locations, each running not that differently than an SMB (with all that that entails), and requires centralized management for thousands of locations. This means simplification and automation throughout the chain from deployment of hardware and configuration to application deployment and management, to lifecycle management and patching everywhere from the BIOS and firmware up through the rest of the tech stack (without needing to put technical people on site). Always look for a cloud based Fleet Management plane that still allows each site to run autonomously when connectivity fails.