How do you balance latency and bandwidth trade-offs in edge computing?

The best way to reduce latency is not to send the data at all. For example, if you want to control the AC in a room, you can monitor the temperature from multiple sensors and get a room average. Then when the average goes above a threshold, trigger the AC to come on. The edge device might send the central server just the start and stop time of the AC but not the readings from the individual sensors. This data could either be discarded or sent in batch mode at a later time.

1. Start with description of target audience. From my (developer's) perspective, this initial content is more suitable for decision makers, team leads, managers, or non-technical people getting more involved with development, but NOT actual developers/engineers, as it gives me little practical insight/tips. The current/initial content/scope/audience is fine, but if you don't make it clear upfront, you're wasting many readers' time. Suggest (not just for this paragraph/section, but overall): 2. Shorten/split the paragraphs. 2-3x shorter (than the initial content). 3. Structure, group into lists. 4. Appreciate "you" language (initially) in the first paragraph. Please apply to the rest, too.

"...to the edge of the network, where the data is generated or consumed." would be better phrased as "... closer to the devices and sensors that generate and consume data, i.e. your laptop, phone, or watch. "

An example of technology prior to Edge Computing is Content Delivery Networks, or CDNs, which would serve cached webpages to you when there is no updated content at the original source. Edge Computing goes one step beyond that to provide active computation at a similar layer, and beyond - the definition can extend to cover conventional servers and services, IoT and home automation devices and the like.

Edge computing is not just about the technology itself, but also about the way we approach software development and data management. To fully embrace the potential of edge computing, developers need to carefully consider various factors when designing and deploying applications. Identify suitable use cases Prioritize data based on processing needs Optimize data processing Allocate resources dynamically Adopt hybrid approach (edge computing + cloud services) Ensure data privacy & security (strong encryption, access control) Design scalable edge applications (microservices, containerization) Implement monitoring & management tools for smooth operation Continuously improve applications & infrastructure

An interesting aspect of edge computing is the concept of "fog computing," which involves creating a distributed network of edge devices that can collaborate to process data and perform computational tasks. This can enable new types of applications, such as autonomous vehicles and smart cities, that require fast and reliable processing of large amounts of data in real-time.

Edge computing enhances the efficiency, reliability, and security of various applications by decentralizing data processing. It empowers real-time decision-making, conserves bandwidth, and supports remote locations with limited connectivity. Additionally, edge computing alleviates data sovereignty concerns by retaining sensitive information within local boundaries. This versatile approach paves the way for scalable, resilient, and adaptable solutions that cater to the diverse demands of our ever-evolving digital landscape.

Design to be reentrant, self-contained, and composable: Using composability allows the application to replace critical components, enabling extensibility and upgradability when needed. Self-containment addresses different hardware, software, and even power variations that are widespread at the edge, and apps must bring everything they need. Containerized applications are a great example of this. Re-entry allows for recovery from the unknown. Power, network, or data interruptions must be handled gracefully, allowing the app to pick up from where it left off without issues. It's not easy and not a science, but at least considering these three principles when designing an edge application will give you better success in the long run.

1. Balance local resources and cloud-based services for performance, energy efficiency, and adaptability 2. Employ a hybrid architecture to delegate tasks between edge and cloud intelligently 3. Analyze trade-offs between latency, cost, and data privacy for local or cloud-based operations 4. Embrace modularity and interoperability for seamless integration with various edge devices, technologies, and platforms 5. Ensure flexibility and adaptability in evolving edge ecosystems 6. Implement robust security measures to protect data and devices across diverse nodes 7. Harness machine learning and AI algorithms for improved decision-making and autonomous operation

I would agree that edge apps *can* be difficult to deploy yourself, but using a management suite (such as AWS, Azure, or GCP) can make the deployment process completely automated.

As with any emerging technology, the tools and options in this field are not considered fully developed, and standardisation might take some time to materialise. ISO/IEC TR 23188:2020 is worth paying attention to, on that topic. When aiming to manage an Edge Computing system, you should be aware of hardware, firmware, and software constraints, security updates and responses to alerts, data and network capabilities and constraints, available resources and expected usage, general quality of service and monitoring.