Uncore Frequency Tuning For Energy Efficiency in Mixed Priority Cloud Native Edge

A key challenge for telco cloud deployments is achieving a suitable balance of performance for the mix of workloads, and the power consumption of the compute infrastructure hosting those workloads. Recent generations of Intel x86 servers have a broad suite of technologies aimed at granular tuning of the multi-core processor resources: Cache Allocation Technology (CAT), Memory Bandwidth Allocation (MBA) and Speed Select Technology (SST) are key examples. Further fine-tuning is possible by exploiting frequency scaling properties of the "uncore" component of the CPU which include the last level cache and memory bandwidth control units- i.e., these are distinct from the part of the processor socket silicon footprint where "cores" reside. Such tuning capabilities can realise deterministic performance in the presence of mixed priority workloads which is a major consideration for more resource-constrained environments in the cloud compute continuum, e.g., the network edge where a mix of Containerised Network Functions (CNFs) can co-exist with other workload types. This paper explores a method of combining CAT, MBA, SST and uncore frequency tuning in tandem to achieve improved performance and power efficiency where a high priority workload co-exists with a "Noisy Neighbour" low priority workload. In one specific case, a performance increase of 29.6% is achieved in conjunction with a power reduction of 18.2% when compared with the baseline "untuned" set-up.