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The Edge computing rise of Internet of Things (IoT) over the past decade has generated enormous amounts of data from connected devices. However, centralized cloud computing infrastructure is proving inefficient for IoT applications that require low latency, real-time insights and high bandwidth. This is driving a shift towards distributed computing at the edge of networks, closer to the data sources and endpoints known as edge computing. In this article, we explore what edge computing is, the factors driving its growth, use cases and how it is reshaping IT infrastructure.
What is Edge Computing?
Edge computing refers to processing and storing data locally at or near the edge of the network rather than relying solely on centralized cloud infrastructure. It involves deploying computing power, storage and applications closer to IoT endpoints and users. By processing data locally instead of sending massive amounts of raw data to distant centralized cloud data centers, edge computing addresses latency, bandwidth and connectivity issues for applications requiring low latency or real-time responses.
Edge computing deployments complement cloud platforms by filtering, aggregating and pre-processing data before sending it to the cloud. This reduces the amount of data that needs to travel over networks and the load placed on cloud infrastructure. Edge nodes include devices like routers, cellular base stations, smart IoT gateways and mini data centers placed at the edge. Edge computing enables enterprises to leverage data generated by IoT devices that would otherwise be too difficult or expensive to ship to centralized cloud data centers.
Drivers of Edge Computing Growth
The exponential growth of IoT devices generating data continuously is one of the primary drivers for edge computing. By 2025, there are expected to be over 75 billion IoT devices globally, producing massive amounts of data that can overwhelm cloud infrastructure if sent in its raw form over networks.
Latency sensitive applications like autonomous vehicles, industrial robotics, augmented/virtual reality, telemedicine and smart grids require sub-50 millisecond response times for safety, user experience and functionality which cloud alone cannot provide. Edge computing supports real-time analytics and decisions at the network edge for such latency critical applications.
With 5G networks promising multi-Gbps bandwidths, edge computing will enable new wireless applications requiring that bandwidth closer to users and devices. 5G also supports network functions virtualization which eases deploying virtualized compute and storage at the edge.
Privacy regulations like GDPR in Europe necessitate processing and analyzing personal data locally rather than sending it to foreign cloud providers, driving edge adoption. Security is also improved by keeping sensitive IoT and operational data on localized edge infrastructure vs open public clouds.
Edge Use Cases
Smart Cities – Edge supports computer vision applications for traffic management, security/surveillance with real-time response needs. It facilitates deployment of smart lighting, parking and waste management systems.
Transportation – Use cases include autonomous vehicles, traffic infrastructure, transportation hubs for predictive maintenance of rail/subway systems through edge analytics of sensor data.
Industrial IoT -Edge enables predictive maintenance through real-time machine learning on plant data, remote operations through augmented reality and robotics automation through edge controllers.
Consumer IoT – Edge gateways power voice assistants, smart home systems, adaptive streaming and localized CDN for video. Edge will be critical for AR/VR and mixed reality experiences.
Healthcare – Latency-sensitive uses like medical imaging diagnostics, remote surgery, telemedicine, hospital operations can leverage edge infrastructure for continuous patient monitoring and real-time diagnostics/decisions.
Impact on IT Infrastructure
Edge computing is leading enterprises, telecoms and cloud providers to rethink their IT infrastructure strategies. Traditional static data center architectures will evolve into distributed, edge-native frameworks optimized for ultra-low latency, localization and data security.
Multi-access edge computing (MEC) standard is facilitating integration of IT and telecom networks. Microsoft, Amazon, Google, IBM, Dell are partnering with telcos to deliver a hybrid cloud-edge environment spanning centralized cloud and distributed edge infrastructure.
Converged edge infrastructure and edge as a service business models are emerging to simplify management and monetization of edge resources for enterprises. Edge native applications, workloads and containers will grow significantly aided by edge computing frameworks from open source platforms like Akraino, EdgeX Foundry.
Hardware acceleration and specialized Edge computing processors optimized for machine learning inference at the edge are being developed. Network functions virtualization is enabling virtualized edge infrastructure. Strategic acquisitions of edge computing startups are underway to future-proof hybrid cloud offerings around the edge. Overall, edge computing presents a major architectural shift for delivering low latency, real-time experiences in the era of IoT and 5G.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
