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GridGain Systems 2022 Predictions: Demand Ramps Up for Processing and Acting on Data at the Edge in Real Time

vmblog predictions 2022 

Industry executives and experts share their predictions for 2022.  Read them in this 14th annual series exclusive.

Demand Ramps Up for Processing and Acting on Data at the Edge in Real Time

By Lalit Ahuja, Vice President of Solutions and Services, GridGain Systems

The increasing availability of 5G is enabling advances in a variety of use cases - from mobile gaming to medical use cases to autonomous vehicles - that require large amounts of data and processing at the edge. In the past, data-at-the-edge use cases involved mainly stateless services - where data was not stored locally. However, these new use cases require stateful services, where increasing amounts of data must be stored and acted upon as close to users as possible. 

For example, multi-person online gaming requires instantaneous reaction by the gaming server for gamers to interact in real time. This works well today as long as the gamers, even in different countries, connect to local servers that are then interconnected with other servers over fiber connections. But what happens when a gamer travels? Gamers regularly achieve a certain state in a game, earning a particular level and acquiring a number of credits or weapons, etc. A gamer playing a game while waiting for a flight out of San Francisco International Airport needs to return to the exact same game state when logging into the game after landing at Narita Airport in Japan - without experiencing any delay that could disrupt the gameplay. 

Data processing on the edge is something that can improve the gaming experience, but. let's look at other data-at-the-edge scenarios that are changing our way of life. Autonomous vehicles  are equipped with a variety of sensors which create copious amounts of data. Without the ability to process this data at the edge, these vehicles cannot be autonomous. And given the mobile nature of these vehicles, data and the ability to process this data must also move between different edge networks.  

Healthcare is another great example of edge computing, where the technology is making the difference between life and death. The medical history for any person with a health condition that requires quick response, or can influence the treatment of any ailment has to be available to paramedics or in an ER where the person is being treated. By combining data from wearable health monitoring devices with the person's health data on the edge network, such information can be retrieved and referenced quickly, analyzed and influence the decision a healthcare provider makes to help the person. Think of ambulance sensors that require instantaneous analysis of data and execution of commands to maintain physiologic states. Or devices that monitor and report on insulin levels or cardiac rhythms, using real time data about the patient's health at any given point of time plus analyzing medical history data. All of that data is stored and processed in real time on the edge, giving patients and healthcare providers the ability to make sound healthcare decisions at a moment's notice.

For all these scenarios, changes happening to relevant data has to be reflected and replicated to edge networks and data stores in real time. This will require a data management strategy that supports hybrid multi-cloud environments, data computing platforms can be deployed on any infrastructure, from bare metal to containers; and that can be run in active-active mode to allow for near-instant replication and two-way syncing of data. Today, online gaming, autonomous vehicles, or health monitoring of patients on the move may be drivers for implementing such a solution. However, as we emerge from the impact of the Coronavirus this year,  we expect to see a continued trend of working from anywhere. That shift will mean more people living in more remote areas, further away from major metro, economic, and medical hubs.  So the need for edge computing will likely increase exponentially, leading to a significant growth in reliance on stateful data in real-time anywhere, any time.



Lalit Ahuja 

Lalit Ahuja is the Vice President of Solutions and Services at GridGain Systems, provider of enterprise-grade in-memory computing solutions powered by the Apache Ignite distributed database. With more than 20 years of expertise across a wide range of IT functions, Ahuja oversees the delivery of vertically-aligned solutions and leads a global team of solution architects focused on customer success.

Published Wednesday, February 02, 2022 7:30 AM by David Marshall
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