The rapid advancement of digital transformation presents new data storage and computing challenges for enterprises across all industries. The sheer volume of data being generated today has placed a considerable strain on existing on-premises and centralised cloud infrastructure, making data processing difficult. Moreover, the use of time-sensitive data in crucial decision-making processes has created a need for low-latency solutions that can provide real-time access to data. To this end, edge computing has emerged as a transformative approach, which helps address several data management challenges that enterprises face today.
By positioning computing power and data storage at the network’s edge, edge computing brings processing capabilities closer to the data-generating devices. This approach reduces the time and bandwidth required to transfer data, resulting in lower transmission costs. It also minimises the reliance on distant data centres, thereby reducing latency and improving data access. Furthermore, edge computing addresses security-related concerns associated with centralised cloud solutions by keeping sensitive data within the local environment and minimising the need to send data to distant data centres where vulnerabilities may exist.
Industry applications of edge computing
Edge computing solutions are being increasingly leveraged in various time-sensitive industries that cannot tolerate significant delays in transmitting information from the data source to the centralised computing system. In the manufacturing space, edge computing helps enterprises with real-time monitoring of production lines, predictive maintenance and quality control, thereby improving operational efficiency and reducing downtime. Further, by analysing sensor data at the edge, manufacturers can optimise processes and swiftly respond to anomalies, resulting in cost savings and improved product quality. In the healthcare sector, edge computing is facilitating remote patient monitoring, real-time analysis of medical data, and quick response to emergencies. Edge devices and wearable sensors are now being utilised to collect patient data, which is then analysed locally to provide timely diagnosis and treatment recommendations. Edge computing is also finding applications in smart city deployments, assisting governments in processing data locally. Through the use of edge computing solutions, cities can respond to events in real time, reduce congestion, improve energy efficiency and enhance overall citizen safety. Meanwhile, in the retail sector, edge computing solutions are being leveraged to enhance both the in-store and online shopping experience of consumers. For instance, in physical stores, the adoption of edge computing is being explored to power applications that require extremely low latency, such as mixed reality mirrors in each changing room, smart shelves at counters and automated checkout options.
Evolving technological landscape
- 5G integration: The emergence of bandwidth-intensive applications facilitated by 5G technology, such as the internet of things, artificial reality and virtual reality, has amplified the demand for ultra-low latency and necessitated the decentralisation of data and computing resources to the network edge. While 5G technology itself diminishes the network latency between endpoints and mobile towers, it fails to address the challenges posed by distant data centres that may not be able to process information within the necessary time frame for latency-sensitive applications. Edge computing complements 5G’s capabilities by bringing computational power and storage capabilities closer to the network edge. Further, the integration of 5G with edge computing provides enterprises with increased scalability and flexibility for deploying applications and services. The distributed nature of edge computing allows the seamless addition of edge nodes, enabling organisations to expand their infrastructure as needed. Moreover, the combination of ultra-high-speed and low-latency capabilities of 5G enable enterprises to efficiently scale their edge applications, supporting dynamic workloads and seamlessly adjusting to shifting demand patterns. These features create a powerful synergy between edge computing and 5G, particularly beneficial for smart industrial applications such as smart grids, intelligent transportation systems, and immersive media experiences.
- Edge-to-cloud integration: A hybrid approach, combining edge and cloud computing, is gradually gaining traction. Edge-to-cloud integration allows organisations to achieve a balance between local data processing at the edge and conducting centralised data analysis in the cloud. Under this approach, edge devices perform real-time data processing, filtering and initial analysis. Simultaneously, the data is securely transmitted to and processed in the cloud for more extensive analysis, historical trend identification, and the implementation of complex machine learning (ML) algorithms. This integration ensures that the right data is processed at the right location, thereby optimising the utilisation of computing resources and further reducing network latency.
- Convergence of artificial intelligence (AI) and ML with edge computing: Combining edge computing with AI and ML capabilities can significantly enhance the processing power of edge devices to perform complex computations locally, enabling faster decision-making and reducing the reliance on centralised cloud infrastructure. Moreover, the integration of AI and ML models with edge devices allows for continuous offline operations without interruptions. The pre-trained models deployed at the edge can operate autonomously, making intelligent decisions and performing tasks without relying on continuous cloud connectivity. This offline capability is crucial for applications in remote or resource-constrained environments where continuous connectivity may not be feasible, such as agricultural monitoring in remote and rural areas, and autonomous drones operating in areas with limited network coverage.
Market uptake
According to industry estimates, the market size for edge computing is projected to grow from $53.6 billion in 2023 to approximately $111.3 billion by 2028, registering a compound annual growth rate of 15.7 per cent during this period. The growth is being driven by several factors such as advancements in edge technology, increasing enterprise adoption, the prevalence of bring your-own-devices, increasing demand for low-latency connectivity, rising awareness among small and medium enterprises regarding edge computing solutions, and the emergence of use cases like autonomous vehicles. The global edge computing market encompasses established companies and start-ups that are innovating and developing new use cases for edge computing. Among various regions, North America leads in the edge computing market due to the concentration of major technology firms and cloud service providers in the region.
Key issues hindering adoption
The integration of edge computing into core networks presents new cybersecurity challenges due to the heightened vulnerability of data processed outside the conventional corporate firewall, making it more susceptible to attacks. Additionally, given that edge devices are often deployed in uncontrolled environments, they are prone to physical tampering or damage. The deployment of edge computing devices further amplifies the attack surface, leaving room for security breaches such as distributed denial-of-service attacks. Inadequate security measures can grant attackers unauthorised access to an organisation’s network, especially since edge devices are often connected to other devices and systems.
Another significant challenge for enterprises pertains to the high costs associated with managing and maintaining an edge environment. This cost burden is primarily due to the substantial investment required to modify existing hardware and software during the transition to edge solutions. Companies frequently encounter the need to procure new devices such as routers, switches and servers, or upgrade their existing ones to support edge computing. Upgrading the network infrastructure and bandwidth becomes necessary to accommodate the increased traffic generated by edge devices. The software costs can be substantial, as businesses often need to procure or develop new applications specifically designed for edge devices.
Enterprises also find it challenging to integrate new edge components into their existing legacy network architectures. Given that edge devices are commonly deployed in remote locations, seamless communication with the rest of the organisation’s IT infrastructure becomes essential. However, many existing network architectures were not originally designed to accommodate edge devices, leading to potential obstacles. As a result, enterprises are often required to make substantial modifications to their network architecture or acquire new networking equipment to facilitate a successful edge deployment.
Conclusion
The rapid surge in data volume generated by interconnected devices and applications is exerting immense pressure on network bandwidth and data transfer. By enabling local data processing and analysis, edge computing helps reduce the requirement for extensive data transfers to centralised cloud infrastructures, resulting in lower bandwidth costs, enhanced network performance, and optimal utilisation of resources. Various industries such as manufacturing, healthcare, transportation and retail are recognising the potential of edge computing to drive innovation, improve operational efficiency and enhance customer experiences. Moreover, the deployment of 5G networks, which provide high-speed and low-latency connectivity, will further accelerate the growth of edge computing. As edge computing continues to evolve, advancements in edge AI and ML capabilities will further augment its potential. This progress will enable real-time data analysis, predictive insights, and localised intelligence, allowing businesses to extract valuable insights and make informed decisions on the edge. Going forward, owing to the multitude of advantages that edge computing holds, it is expected that a significant number of businesses will integrate edge elements into their core computing infrastructure.
