AI-based Cloud Load Optimizer


Duration: 4 months
Industry: Cloud Services
Location: USA


The client is a global cloud infrastructure services provider delivering a range of cloud products, infrastructure solutions, and managed IT-services



Aterise was challenged to develop a system that was designed to automatically deploy, test, and support applications in a high-loaded cloud production environment. The system was required to:

  • Be resilient and optimized in resource consumption
  • Receive a Docker image at the input, define its properties and capabilities, then launch the application and track the application’s metrics
  • Be able to define a problem, solve it, and self-learn in operation


Our dedicated team took part in each phase of the system development, starting from drawing up specifications and architecting to prototype release. We managed to solve a number of technical challenges during the project, e.g, software failures in several nodes and data centers, as well as net issues, application query growth, DDoS attacks, and application query decrease

We managed to create an AI-powered, self-learning solution with the following capabilities:

  • The logging system can collect data about other systems in real-time mode and notify in a Telegram channel. It gives notifications about errors, start/end testing, and each testing point. Users get several notification types that may be subscribed to so that nothing important is missed
  • The 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 non-linear). It interacts with the metric collection system, and it can compare app load data with app current state data during the load moment
  • The metrics collection system can gather deployed app statistics, processor resources, random access memory, net resources, latency, and hard drive resources statistics

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