Infrastructure-as-a-Service cloud providers sell virtual machines that are only specified in terms of number of CPU cores, amount of memory, and I/O throughput. Performance-critical aspects such as cache sizes and memory latency are missing or reported in ways that make them hard to compare across cloud providers. It is difficult for users to adapt their application’s behavior to the available resources. In this work, we aim to increase the visibility that cloud users have into shared resources on public clouds. Specifically, we present
CacheInspector
, a lightweight runtime that determines the performance and allocated capacity of shared caches on multi-tenant public clouds. We validate
CacheInspector
’s accuracy in a controlled environment, and use it to study the characteristics and variability of cache resources in the cloud, across time, instances, availability regions, and cloud providers. We show that
CacheInspector
’s output allows cloud users to tailor their application’s behavior, including their output quality, to avoid suboptimal performance when resources are scarce.
Multi-Agent Deep Reinforcement Learning Based Cooperative Edge Caching for Ultra-Dense Next-Generation Networks
