Precise Turbo Frequency Tuning and Shared Resource Optimisation for Energy-Efficient Cloud Native Workloads

Abstract

As an increasing number of software-oriented telecoms workloads are run as Containerised Network Functions (CNFs) on cloud native virtualised infrastructure, performance tuning is vital. When compute infrastructure is distributed towards the edge of networks, efficient use of scarce resources is key meaning the available resources must be fine-tuned to achieve deterministic performance; another vital factor is the energy consumption of such compute which should be carefully managed. In the latest generation of Intel x86 servers, a new capability called Speed Select Technology Turbo Frequency (SST-TF) is available, enabling more targeted allocation of turbo frequency settings to specific CPU cores. This has significant potential in multi-tenant edge compute environments increasingly seen in 5G deployments and is likely to be a key building block for 6G. This paper evaluates the potential application of SST-TF for competing CNFs – a mix of high and low priority workloads - in a multi-tenant edge compute scenario. The targeted application of SST-TF is shown to yield performance benefits compared to the legacy turbo frequency capability in earlier generations of processor (by up to 35%), and when combined with other intelligent resource management tooling can also achieve a net reduction in server power consumption (of 1.7%).