Provisioning Differentiated Last-Level Cache Allocations to VMs in Public Clouds - Shahrad, Mohammad; Elnikety, Sameh; Bianchini, Ricardo (Proceedings of the Symposium on Cloud Computing (SoCC), 2021)
Public cloud providers offer access to hardware resources and users rent resources by choosing among many VM sizes. While users choose the CPU core count and main memory size per VM, they cannot specify last-level cache (LLC) requirements. LLC is typically shared among all cores of a modern CPU causing cache contention and performance interference among co-located VMs. Consequently, a user’s only way to avoid this interference is purchasing a full-server VM to prevent co-tenants. Although researchers have studied LLC partitioning and despite its availability in commodity processors, LLC QoS has not been offered to public cloud users today. Existing techniques rely mostly on performance profiling, which is not feasible in public cloud settings with opaque VMs. Moreover, prior work does not address how to deliver differentiated LLC allocations at scale. In this work, we develop CacheSlicer, the first system that provides cluster-level support for LLC management in a public cloud. We show how to provide LLC allocations in a major public cloud provider to enable differentiated VM categories, from which users select VMs that match their workloads. We integrate it into the Azure VM scheduler and show its effectiveness through extensive evaluations.