Open Internet by MindsNet

Optimize Multi-Cloud Deployment for Global Performance and Cost

Organizations use multiple cloud providers to avoid vendor lock-in and optimize performance, yet determining optimal workload placement across multiple clouds while minimizing costs and maximizing performance remains computationally intractable. Current approaches use simple rules or manual decisions that result in suboptimal placements and missed cost savings. The challenge requires developing optimization systems that can consider all cloud options simultaneously, predict performance and costs for different placement strategies, and automatically migrate workloads as conditions change. Major obstacles include the complexity of multi-cloud optimization, different pricing models across providers, performance variations between clouds, and data transfer costs between providers. Without optimal multi-cloud strategies, organizations will continue overpaying for cloud services while experiencing suboptimal performance. Success would enable organizations to leverage multiple cloud providers optimally, achieving better performance at lower costs while maintaining flexibility and avoiding vendor lock-in.

Computing & Technology, Computer Science, Cloud Computing