Revenue: 136B USD
Team included: Backend engineer, AI-engineer.
Lynix Software has been developing software since 2012. Our expert engineers have created innovative solutions for customers across different industry verticals from all over the world. We take you from potential to performance, help you develop your business with planning, architecture, design & unique style, development, QA, and customer support.
Within the project there is a prototype created with artificial intelligence technology to predict the behavior of the system in high loaded environment (processor resources, random access memory, net resources, hard drive resources). This is a learning system self-educating with the help of A.I. algorithms.
The development of a system which is able to automatically deploy, test, and support applications in cloud production environment. The deployed application must be resilient, and must consume optimum resources. The system receives a Docker image at the input, defines its properties and capabilities, then launches the application and tracks the application’s metrics. The system must be able to define a problem and to solve it, besides, be able to learn in operation.
A solution is a complex learning system created with A.I. technology for Customer.
- Logging system collects data about the rest systems in real time mode and notifies in telegram-channel. It gives notifications about errors, start/end testing, and each testing point. It has several notification types which may be subscribed to so that nothing important is missed.
-Testing system can deploy an application and put query load on it with the help of mathematical algorithms (load growth may be both linear and nonlinear). It interacts with the system of metrics collection, and it can compare application load data with application current state data during the load moment.
- Metrics collection system gathers deployed applications’ statistics. Processor resources, random access memory, net resources, latency, and hard drive resources statistics.
Our team took part in each phase of the system development beginning from drawing up specifications and architecting to prototype release. The team solved a number of technical challenges during the project - for instance, software failure at one or several nodes, software failure at one or several data-centers, net issues, application query growth, DDoS attacks, and application query decrease.