6 MINS READ
Highlights
- An AI-powered digital replica allows you to foresee disruptions in business applications and ensure continuity.
- It identifies bottlenecks in processes within an enterprise IT system early on, addresses throughput, latency, and deployment issues, and reduces downtime.
- Digital twins use data-driven neural surrogates that mimic enterprise IT programs and help improve the simulator’s performance.
In this article
Why digital twin for an enterprise IT system
Systems need to flag possible failures and forecast risks in time for course correction.
A company’s enterprise IT system is its nerve center. This comprises individual architectural components such as business applications for transaction processing, database, networks, and so on.
While the use of AI-based digital infrastructure and hybrid deployments—the use of cloud and on-premise infrastructure—has helped businesses, they have also brought in more digital layers, and this sometimes opens the door to disruption and consequent downtime.
Time-bound software development and distributed deployment at scale expose applications to high user traffic load and increase in data, hardware failure, software bugs, memory leak, and synchronization, all of which cause the performance to slow down or lead to unavailability altogether.
Enterprise IT must, therefore, be equipped to immediately flag off possible failures and forecast risks to predict and fix disruptions before they actually strike.
Digital twin technology, which emulates the business operating model of an enterprise for individual architecture components, serves as an answer to these problems.
How a twin averts disruption
Accurate ‘what if’ scenarios
A digital twin helps us understand the behavior of an enterprise IT system in the context of business expansion activities or unforeseen situations. For instance, a spurt in user traffic on web-based applications or seasonal overload as seen on tax filing and certain e-commerce platforms. Today, it is normal to have a multi-cloud and multi-geography deployment for such applications to address the challenge of scale. But these migrations sometimes bring in many unknowns, causing disruption.
Likewise, large models such as generative pre-trained transformer (GPT3, GPT4) and Megatron-Turing are becoming prominent, with APIs sparking innovations. Within a short span post its launch, the usage of ChatGPT increased, and there were reported incidents of outages. These trends show how important it is to plan deployment at scale.
Therefore, the enterprise IT system and its corresponding digital twin must have a two-way feedback loop, facilitating ‘what if’ scenarios for stack deployment and infrastructure execution to predict possible events that may cause interruptions.
Neural surrogates elevate digital twin capabilities
Improved simulator performance with faster and more efficient data-driven models.
Modern business applications must support non-functional requirements such as throughput and latency and fulfill the service-level agreement (a contract that records the terms and conditions related to deliverables between a service provider and the customer) requisites such as quality, efficiency, and reliability.
A digital twin models the behavior of physical systems—such as boilers, turbine engines, internet of things platforms, and enterprises—and continuously learns by consuming data from multiple sources to stay updated and accurate. This helps it identify bottlenecks in current business processes and address functional as well as non-functional requirements.
Neural surrogates are data-driven models that mimic the behavior of computer programs, an emerging technology that will play an integral role in amplifying digital twin capabilities.
Neural surrogates are program models used to power digital twins for enterprise IT systems. These surrogates are data-driven and mimic the behavior of computer programs in terms of data input/output characteristics and are faster than the actual program run for enterprise IT. They have smarter analyzing capabilities to help improve the simulator’s performance.
Continual performance
Predictive and proactive maintenance
A performance digital twin mitigates deployment issues and reduces turnaround time through predictive and, thereby, proactive maintenance.
A performance digital twin of an IT system allows users to analyze issues like a sudden seasonal increase in user load, or latency when deploying a new application.
Ongoing deployment may sometimes fail to accommodate new business requirements such as increasing or reducing storage capacity, scalability, enhancing security, or integrating new features into the existing system.
In such cases, making proactive migrations enable system administrators to drive performance maintenance, ensuring a seamless functioning of the overall enterprise IT system.
A performance digital twin of an IT system allows analysis of scalability, throughput, and latency. It lets users analyze issues that could crop up, for instance, when the user load increases by tenfold or the throughput and latency that is expected when deploying a new application.
As enterprises deploy data lakes to consolidate and analyze the information and gain business insights, using digital twins will reduce business downtime. Co-designing the application and its digital twin leveraging agile frameworks helps save time and effort not only in development but also in testing data to train the digital twin.
