Predictable Performance for QoS-Sensitive, Scalable, Multi-tenant Function-as-a-Service Deployments

Abstract

In this paper we present the results of our studies focused on enabling predictable performance for functions executing in scalable, multi-tenant Function-as-a-Service environments. We start by analyzing QoS and performance requirements and use cases from the point of view of End-Users, Developers and Infrastructure Owners. Then we take a closer look at functions’ resource utilization patterns and investigate functions’ sensitivity to those resources. We specifically focus on the CPU microarchitecture resources as they have significant impact on functions’ overall performance. As part of our studies we have conducted experiments to research the effect of co-locating different functions on the compute nodes. We discuss the results and provide an overview of how we have further modified the scheduling logic of our containers orchestrator (Kubernetes), and how that impacted functions’ execution times and performance variation. We have specifically leveraged the low-level telemetry data, mostly exposed by the Intel® Resource Director Technology (Intel® RDT) [1]. Finally, we provide an overview of our future studies, which will be centered around node-level resource allocations, further improving a function’s performance, and conclude with key takeaways.

Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

“…Similarly, in [67], the authors use low-level telemetry data retrieved from the CPU micro-architectural components (e.g., Level 3 caches) to inluence the scheduler's behavior. In particular, nodes not having enough available memory bandwidth 9 are discarded in the iltering process.…”