AI-based Cloud Load Optimizer

HIGHLIGHTS

Duration: 4 months
Industry: Cloud Services
Location: USA

ABOUT CLIENT

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

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CALLENGE

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

SOLUTION

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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