Dynamic cache size management of multi-tenant caching systems

Abstract

Cache memory requirements between normal and peak operation may vary by two orders of magnitude or more. A cache memory management system for multi-tenant computing environments monitors memory requests and uses a pattern matching classifier to generate patterns which are then delivered to a neural network. The neural network is trained to predict near-future cache memory performance based on the current memory access patterns. An optimizer allocates cache memory among the tenants to ensure that each tenant has sufficient memory to meet its required service levels while avoiding the need to provision the computing environment with worst-case scenario levels of cache memory. System resources are preserved while maintaining required performance levels.