Skip to main content Skip to navigation Skip to footer

Dynamic Resource Scaling

Build cloud architecture with limitless flexibility for AI training and dynamic workloads.

Is Your Cloud Architecture Rigid? Build With Limitless Flexibility.

Note: This is a use case example demonstrating how Rediacc can solve this problem. As a startup, these scenarios represent potential applications rather than completed case studies.

Crisis Scenario: AI training times extended 2-3 times, causing project delays. Engineers experienced significant productivity loss while waiting for resources, threatening the organization’s competitive advantage.

The Problem

The organization’s software engineers are experiencing performance issues with on-premise servers used for AI model training:

  • During office hours (08:00-17:00), server requests reach 99% capacity
  • Training requiring high processing power causes the hardware to be insufficient

Search for Solution:

  • Server upgrade cost is not considered suitable due to 6-7 hours of daily use
  • Although cloud migration is considered, data transfer cost and synchronization difficulties are obstacles

Crisis Impact

  • AI training times extend 2-3 times, projects are delayed
  • Engineers experience productivity loss while waiting for resources
  • The organization faces the risk of gradually losing its competitive advantage

Rediacc Solution

System engineer Yüksel develops a hybrid model with Rediacc:

Hybrid Cloud Scaling

1. Instant Cloud Migration

  • During office hours, on-premise services are cloned to the cloud with all data and configurations
  • 100 TB of data is synchronized in 9 minutes by transferring only the changed parts thanks to Rediacc

2. Dynamic Scaling

  • Servers in the cloud environment are rented as much as needed for AI training
  • Processing power can be increased 10 times according to demand

3. Night Synchronization

  • At the end of the workday, all changes in the cloud are automatically pulled to the on-premise environment
  • Engineers working at night continue their operations with up-to-date data

Result

Cost Advantage:

  • By renting cloud resources hourly, monthly cost was reduced by 60%
  • The need to upgrade on-premise servers was eliminated

Performance Increase:

  • AI training times were reduced from 8 hours to 1.5 hours
  • Engineer productivity increased by 40%

Flexible Working:

  • Data consistency between cloud and on-premise environments was ensured seamlessly
  • Teams on the night shift had instant access to up-to-date data