Shared Representation Learning for Resource-Constrained Multi-Task Forecasting in Cloud Backends

This research explores a crucial problem in cloud infrastructure: efficiently forecasting resource needs across multiple tasks. The use of shared representation learning offers a promising approach to optimize resource allocation and improve performance.

• •Addresses resource contention issues within cloud environments.
• •Utilizes shared representation learning to improve forecasting accuracy.
• •Applies to cloud-native backend systems.