Autoscaling with predictive analytics is a method that allows cloud applications to automatically adjust their resources based on predicted demand patterns. By combining real-time monitoring with historical data and machine learning, this approach helps optimize resource utilization and minimize costs. It's particularly useful for applications with fluctuating traffic, such as e-commerce sites, ensuring a better user experience while controlling expenses.

Resource sharding involves dividing large data sets into smaller, more manageable pieces called shards. This technique allows multiple clients to access resources concurrently across different nodes in the cloud. By distributing the load, sharding improves performance and availability, as each server handles less data, leading to faster response times and better resource utilization.

Cache invalidation is the process of removing outdated or 'stale' data from the cache to optimize resource use and improve system performance. It ensures that users receive accurate and up-to-date information. Effective cache invalidation can be achieved through various methods, such as time-based expiration or event-based triggers, which help maintain the integrity of the data being accessed.