The rollout of 5G networks is already taking place globally, improving on 4G in terms of speed, latency and scale. This will enable autonomous systems, telemetry and the Internet of Things to accelerate significantly in the ability to execute.
However, legacy technology will struggle to meet the demands of speed and scale and this is where in-memory technology becomes an essential piece of solving the problem. Whereas traditional data tiers are often too expensive, complex and rigid, in-memory data grids can provide flexibility at scale and provide essential performance levels.
An in-memory data grid (IMDG) is a cluster of computers that pool their random access memory (RAM), and allow applications to share data with other applications in the cluster. Large scale applications need more RAM than they can get from a single server.
Each computer in the cluster has its own view of data and data structure in memory but shares it across all the other computers. Software keeps track of the data on individual nodes and shares it with other nodes or applications.
One of the main benefits of in-memory data grids is that it is possible to quickly analyze changes in data with low latency and offer real-time access to accurate information. This can often make the difference between a right and wrong decision.
An in-memory data grid can help to meet responsiveness expectations. It can elastically spin up and down distributed nodes to perform consistently, even during traffic spikes and peak activity times.
Data grids are able to reduce the headaches that come with deploying new applications and allow organizations to go to market more quickly.
The distributed design means new nodes can be added to the cluster to allow scalability and processing of larger transaction volumes.
IMGDs act as an intermediary layer between an application and a relational database. By reducing the load on the relational database, this means performance is quicker and service to end users is time sensitive.
Businesses today have a variety of It environments and data grids can be used in many different industries and use cases to make data tier integration more efficient.
Edge computing is all about placing compute power close to where data is being generated – either to filter and collect raw data and reduce transmission amount or to run analytics and get business insights faster.
Not relying on a central location that may be miles away means that data, especially real-time data, does not have latency issues that can affect the performance of an application. Edge devices can consist of many different things, from a smartphone or security camera to an IoT sensor.
Defeating latency at the edge is crucial as latency can cause increased costs, a loss in revenue and even some hazardous situations.
Before edge computing, a smartphone scanning someone’s face for facial recognition would have to run a facial recognition algorithm through a cloud-based service to process it and this took time. With edge computing, the algorithm runs on an edge server or gateway, or even on the smartphone itself which is way quicker.
Unfortunately, 5G only improves latency between antennas and cell towers. This leaves a problem of data processing latency on edge compute nodes. A breakthrough in edge computing is only made possible with a data architecture that uses software and memory layers. Many 5G carriers are working edge-computing strategies into 5G deployments so they can offer faster real-time processing.
Computing power at the edge is often limited by physical space. Most edge sites cannot support hardware infrastructure that consists of data center servers. A streaming engine is therefore necessary to ingest, transform, synchronize and distribute data. This engine needs a streamlined code base and must be small enough to fit into a variety of endpoints and devices.
The key to processing and analyzing data on the edge is system memory. A set of clustered nodes can pool memory, allowing applications to share data structures with other applications running in the cluster.
In-memory can deliver sub-millisecond response times and also has the benefit of enabling autonomous management of a range of distributed compute resources at scale. This makes it possible to securely deploy AI, IoT and analytics workloads and deliver real-time analytics.
Real-time data processing will undergird the next generation of business capabilities. It will have an effect on supply chain organization, risk management, waste elimination and offer greater insight into customer needs. It will lie at the heart of smart grids, autonomous vehicles, connected cities, telecommunications networks, wireless factories, and much more.
The use of in-memory technologies with next-gen chips is likely to open up a wealth of applications. Here are just a few examples.
Medical: Applications like remote surgical robots are already being tested. Robots have embedded in-memory processors connected to touch-sensitive haptic gloves and are manipulated through a high-speed cloud infrastructure. Such applications could mean that underserved, remote communities benefit from advanced healthcare.
Smart cars: A smart car would be able to self-diagnose, order parts, drive itself to a repair shop while the owner is asleep and return home in time for him or her to go to work.
Drones: Drone to drone communication will mean that there will be more coordination for uses such as livestock management, disaster relief, firefighting, inspection of remote facilities, and much more. Embedded in-memory streaming engines will enable drones to act in synchronicity, rather than relying on operators to individually manage each one.
Gaming: An embedded streaming engine in a set-top box delivering instant haptic feedback could generate huge revenue for gaming companies as this would enable an even more immersive experience for gamers.
Integrating 5G with in-memory data grid will build a platform for a whole new generation of technology. It will expand edge use cases like mobile devices and IoT and enable more efficiency in interpreting big data. Systems will be able to listen, learn, process, and respond in real-time. By freeing companies from latency constraints, rapid advances are likely to take place in business models, products and services.