1) Traditional server architectures struggle to handle growing data volumes and values that decline rapidly over time. In-memory computing is needed for performance but current approaches like sharding data reduce accuracy. 2) Software-defined servers address these issues by allowing servers to expand in size on demand using standard hardware. This provides in-memory performance at large scale with self-optimizing servers that require no changes to applications or operating systems. 3) TidalScale uses machine learning to transparently optimize resource allocation across multiple physical servers operating as a single large virtual machine. This provides up to 200x faster performance than a single server for memory-intensive workloads like machine learning.