IT infrastructure moved from a huge array of decentralized networks and computers to a centralized network with the development of data centers. Large enterprise data centers handled the increased data requirements, but they’re being replaced by cloud computing as more applications and workloads are moved to the cloud. Edge computing, with its low latency and rapid response owing to its close proximity to the application site, can address the current increased demand for faster data processing by autonomous cars, augmented and mixed reality, AI, and automation.

So what’s the problem with current data centers and the cloud computing environments? 

The centralized environment of cloud computing and massive data centers is effective for a wide range of applications and is fast enough for day-to-day needs. What they can’t overcome are the laws of physics – the data can’t travel at a speed more than the speed of light. Latency is more critical now as self-driving cars cannot wait to make a decision while the data from it travels to data centers located thousands of miles away and then transmit a response. They require quick responses in order to make quick decisions; augmented and mixed reality have a similar case.

Consider a smart car that needs to make a decision quickly – it cannot wait 100 milliseconds for data to be sent to a server farm located far away to get a solution. The distance traveled between the data processing unit and the application site should be as short as possible for an instant solution. Edge computing has the potential to achieve single-digit millisecond latency, in business terms this means –

  • Enhanced performance 
  • Lowest response time
  • Real-time analytics and insights 
  • Scalability 

With huge colocation facilities, cloud computing funnels everything from our web searches to social networks and streaming media content to billions of users. This model is useful for applications where timing is not an issue, but for latency-sensitive applications such as financial services, healthcare, autonomous cars network, mixed reality, drones, etc, edge computing is essential. 

Toyota stated that the amount of data volume for smart vehicles will reach 10 exabytes per month around 2025, which is 10000 times greater than the existing demand. According to Grand View Research, the healthcare IoT industry will rise to $534.3 billion by 2025, while IDC forecasts 64.1 million AR/VR headsets in the market by 2023. To accommodate this increased demand for low latency data, network providers must reduce network round trip time.

Meeting the demands of today’s IT infrastructure with Edge computing

1. Edge for the healthcare setting

Edge computing will benefit healthcare professionals at multiple levels: it will be simple to establish remote monitoring and patient care at the endpoint utilizing edge devices. On an organization level, the majority of patient data is hosted on the cloud, which amplifies the risk of data breaches and consequently limits its usage. Data security and compliance can be maintained by using on-premise edge technologies to process and store data locally.

The connected ambulance will be the most revolutionizing use of edge computing for healthcare, wherein:

  • Hospitals will get live streaming of patient data for a heads-up through connected ambulances.
  • Patient information analysis for real-time diagnosis and first-aid recommendations.
  • AR glasses to instantly display patient history and treatment protocols.
  • Remote diagnostics by specialists using haptics.   

2. Surveillance & traffic management

When a surveillance system is placed at the edge, 60-70% of bandwidth costs can be saved. Edge computing can help design the following solutions: 

  • Facial Recognition System (FRS)
  • Automatic Number Plate Recognition (ANPR)
  • Intelligent Video Analytics (IVA)

Since a huge volume of data doesn’t have to be transmitted to the cloud, traffic management will be highly effective with edge computing. Edge enables bus frequency optimization based on demand patterns, vehicle flow control, and lane management in real-time.

3. Edge for industrial manufacturing

For years the manufacturing sector has relied on conventional technology where the decisions were not data-based. Implementing edge in the manufacturing sector will be highly beneficial for predicting maintenance, machine breakdowns, and getting closer to energy efficiency goals. The analysis will also allow producers to customize products based on consumer requirements.

Offshore oil rigs using edge will no longer be dependent on distant data centers for processing their data, they can gather, monitor, and process the required data on-site.

4. Edge computing for financial services

Speed is now the decisive attribute in gaining a competitive advantage over other financial service providers. Edge computing can help banks to deliver fast and more secure services while assuring regulatory compliance and fraud detection since there is a growing demand among customers for enhanced speed of financial transactions. 

Apart from retail finance, investment institutions such as hedge funds and High-Frequency Algorithmic Trading (HFT) businesses are seeking ways to lower their existing last-mile data latency. Processing data locally will assist these companies in speedier data dissemination by decreasing data delays between colocated server farms in remote places, hence optimizing profitability.

5. Augmented reality devices

We all enjoy interactive filters on Instagram and Snapchat, AR places digital elements in the real world, unlike VR where an entire virtual world is created. Smartphones or wearable devices are used to create augmented reality. Visual elements are displayed in a real environment after being processed, without an edge-computing architecture this data will travel to a centralized cloud server where these digital elements will be rendered before appearing on the device resulting in lag and glitches with augmented visuals. 

Due to the low latency of edge computing, data can be processed locally, and augmented data will be displayed seamlessly. AR is not limited to entertainment applications now, organizations around the world are utilizing augmented reality to display product information instantly to the consumers or show machine information while working. 

Edge computing has reached its inflection point due to huge computing demand at the edge and a massive increase in data usage. This doesn’t mean that cloud usage will go down, albeit the future of network infrastructure will be balanced between the edge and cloud computing systems. If your organization relies on data and analytics, working with a data center that uses edge capabilities will be a better deal than going with conventional service providers.

If you’re looking for a service provider with multiple edge data centers and capable of meeting cloud storage needs to be tailored to your application-specific requirements, get in touch with the Rahi team today.

Shreyans Desai is a Solutions Architect at Rahi Systems, on the Networking PSE team. Prior to Rahi Systems, he was a Solutions Engineer focusing on networking automation and systems. His experience includes enterprise data centre and service provider routing, switching, and security solutions for multiple vendors (Juniper Networks, Cisco, Palo Alto Networks, Arista, and Huawei), and also has a deep understanding of cloud computing solutions from Amazon Web Services (AWS) and OpenStack.

About Rahi

Rahi is a subsidiary of Wesco Distribution, a Fortune 200 Company with operations in 50+ countries and annual revenues over USD 19B. Rahi delivers comprehensive data centre solutions for global enterprises, hyperscalers, and multi-tenant data centres. Rahi provides IOR, local currency billing, and RMA services, enabling businesses to operate efficiently anywhere.
Since being acquired in Nov. 2022, Rahi’s global presence and analytical expertise help clients achieve their business and IT requirements.

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