Why do we need meticulous planning of edge architectures?

After a decade dominated by the growing reliance on the Internet of Things devices, now edge computing is slowly transforming the way data is handled, processed, and delivered through these devices around the world. It represents a method of generating, collecting, and analyzing data at the site where data generation occurs in real-time. Unlike earlier cloud models, it doesn't rely on a centralized computing environment; neither is it similar to traditional distributed computing. It enhances applications and systems by eliminating services, components, or data from a centralized hub and putting them closer to the rational extreme. Thanks to the increasing adoption of 5G, one can expect more growth in edge computing.

Market Surge

While Edge and IoT are inseparable, IoT has a scattered network. This form of distributed architecture brings new complexity and security risks. While edge can help solve this problem by having a local source of processing and storage for many of these systems, it may not be sufficient. Meanwhile, this year, due to COVID-19, the edge has entered a new transition phase that demands accommodation of all data repositories into the network for faster deployment of solutions. The State of the Edge 2020 report from the Linux Foundation projects that edge investment will accelerate after 2024, with the deployed global power footprint of edge IT and data center facilities forecast to reach 102,000 megawatts by 2028, with annual capital expenditures of US$146 billion. Hence, understanding how to profit from this technology is becoming important more than ever.

Edge Concerns

Enterprises today are building edge as per their requirement. It can either be harboring hundreds of micro data centers in IT closets around the world or comprised of colocation data centers, managed-hosting providers, or private cloud hosting providers that bring business applications within one to 50 miles of the number of consumers. Further, as more data is exchanged with a remote workforce, network congestion and latency can impact application performance. Besides, IoT adoption across industries will continue to proliferate, thus making it possible to power AI or analytical applications via edge computing, and eventually driving a huge impact on cost and other parameters. However, edge framework has challenges like intermittent connectivity, low bandwidth, high latency along with the need for tiered strategies for data processing and storage.

IoT Processing

Hence for this, business heads should ensure bringing computation close to the data source. This can minimize transmission time, which in turn can reduce the overall latency for receiving results. This is a helpful cause with distributing computation that can increase overall system complexity, creating new vulnerabilities in various endpoints. One of the solutions for this is enabling minimal processing on IoT devices themselves. A data collection device may need to package a payload of data, add routing and authentication to the payload, then send it to another device for further processing.

Tiered Architecture

Another alternative is implementing a two-tiered data transmission strategy. This involves extracting the most useful information from raw data, such as aggregates (e.g. sums, averages), or variations from baseline predictions and then transmitting them using a low-latency network. The best part of a tiered storage strategy is reduced costs expenses. This is because tiered strategies focus on keeping new data on fast and accessible devices such as solid-state drives. In contrast, the older data is stored on slower but lower-cost cold storage devices-thus saving costs. Moreover, it is an easy way to achieve scalability.

Companies today are seeking to capitalize on a growing torrent of data from edge devices and servers. Therefore, they can't afford to be constrained by latency and bandwidth limitations. Hence having a tiered structure is highly useful.