Addressing the challenges needs close collaboration between development and operation teams and implementing best practices
With the growing demand for faster data processing, more and more enterprises are switching from cloud to edge. Characterised by distributed, open AI architecture that features decentralised processing power, edge computing brings enterprise applications closer to data sources such as IoT devices or local edge servers. The rise of edge computing can primarily be attributed to the proliferation of IoT devices.
Edge computing is already transforming a wide range of industries, including manufacturing, energy, healthcare, agriculture, logistics, and transport. Due to the many benefits of edge computing, it is poised for further growth in the coming years. Currently, businesses are generating about 10 per cent of their data outside a traditional data centre or cloud. However, this will increase to 75 per cent by 2025, according to a study by Gartner.
Key business benefits of edge computing
Real-time data analysis and faster decision-making: Edge computing helps businesses make faster data-driven decisions in response to the needs of their consumers. Edge computing, for instance, can help retailers detect high traffic areas at the store. Hence, the staff can gain insights into fast-selling products and replenish stock on time – ultimately improving customer experience and profitability.
Reduced storage needs and enhanced cost savings: With edge computing, accumulated data does not need to go back to the central server for the device to know that a function needs to be executed. Due to reduced storage requirements, enterprises benefit from improved operational cost savings.
Removal of useless data: Conventional cloud computing architecture results in swift data accumulation in cloud storage (such as data from IoT sensors). However, a large portion of data is useless, with enterprises spending a lot on storing data that they most likely will never need. Edge computing can optimise this by sending only critical data to the cloud, which undergoes processing, which reduces latency and hence speeds up the process of time-critical decision-making.
Popular use cases of edge computing
Gartner predicts that the volume of edge computing use cases will increase dramatically in the next few years, anticipating that more than half of large enterprises will have at least six edge computing use cases deployed by 2023.
This would be a phenomenal growth compared to 2019, which saw only 1 per cent of large organisations with six or more edge computing deployments. Currently, some of the primary use cases of edge computing are:
Autonomous vehicles: As autonomous vehicles generate and aggregate a large volume of data from a wide variety of sources, their proliferation depends on their ability to instantly engage in high-volume data transmission as even milliseconds on the road matter. Edge computing enables self-driving vehicles to efficiently process and deliver high-volume data with existing communication networks and cloud computing ecosystems while also ensuring security, reliability, and scalability, thus making autonomous vehicles more viable in the long term.
Manufacturing and industrial processes: The data gathered by sensors and other connected devices in production lines, equipment, and finished goods don’t need to be managed in centralised servers. Instead, most manufacturers only need to know when the data indicates a problem. Compared to the cloud, edge computing allows manufacturers to extract and analyse the required data for real-time interventions more rapidly. In other words, by expediting the problem-solving process, edge computing helps improve operational efficiency, increase cost savings, and avoid supply chain disruptions.
Smart grids: Edge computing can transform smart grids and help enterprises optimise their energy usage. Sensors and IoT devices connected to edge platforms in manufacturing plants and offices can help monitor and analyse energy consumption in real-time. Due to real-time visibility, companies/organisations can predict their energy generation or usage more accurately and even sell excess power back to the grid, which helps them generate additional revenue.
Top challenges of adopting edge computing
Though edge computing offers more exciting possibilities than cloud computing, enterprises still face many challenges when adopting it.
Inefficient bandwidth usage: If an enterprise possesses a plethora of devices that together produce a large volume of data, it would likely want to store that data in the cloud, but transmitting the raw data to the cloud straight from edge devices can be challenging. Usually, enterprises allow higher bandwidth for data centres and lower bandwidth for the endpoints, but edge computing drives the need for more bandwidth across the network.
Speed bottlenecks: Enterprises prefer connectivity networks such as 5G or DSL that focus on throughput from cloud to edge since most applications operate this way while edge networks push data in the other direction. As a result, uplink speed can create a bottleneck. When the cloud goes down for enterprises that depend on a centralised cloud for storage, the data becomes inaccessible until resolved, causing potential loss of business.
Data-related issues: Data is a critical business asset. Gathering data at the edge creates new challenges and can even spawn liabilities if it isn’t handled in line with existing data handling rules. Moreover, it is possible that many enterprises lose out on valuable information because edge computing only processes and analyses partial sets of data.
Security risks: Studies have found that most IT teams consider edge computing a threat to their organisations. When a number of devices are managing data, it might not be as safe as a centralised or cloud-based system. Hence, it is imperative to detect the potential security vulnerabilities around these devices and ensure the systems can be fully secured.
The best approach to overcoming such obstacles is to deploy an open architecture platform that lowers technology sprawl, potential security weaknesses, exposures, and costs. Reducing system architecture complexity is crucial. The basics of an open edge architecture are modularity and openness. It is, thus, important to ensure the flexibility to connect with any network or communication device interface, such as cellular, Wi-Fi, LoRA, or GPS, and also can operate multiple unique software stacks as a homogeneous entity, inclusive of firewall, ML, telemetry, or data analysis.
Addressing edge computing challenges also needs close collaboration between development and operation teams and implementing best practices. Future-proofing an edge computing environment requires enterprises to begin with defining all pieces of the solution, ranging from the application code and building and releasing processes to ongoing monitoring and management.
If you liked reading this, you might like our other stories
Five SD-WAN Trends In The Post COVID Era
Are AI Algorithms Disrupting Our Reasoning Ability?
