Optimising Virtual Resource Mapping in Multi-Level NUMA Disaggregated Systems

TL;DR

This paper presents a NUMA-aware resource mapping method for disaggregated systems, significantly improving application performance by optimizing virtual resource placement in complex topologies.

Contribution

It introduces a novel NUMA-aware mapping algorithm that considers application performance, resource contention, and utilization in disaggregated systems.

Findings

Significant performance improvements over default Linux scheduler.

Effective virtual core pinning and memory migration strategies.

Validated on a 288-core, 1TB system with real applications.

Abstract

Disaggregated systems have a novel architecture motivated by the requirements of resource intensive applications such as social networking, search, and in-memory databases. The total amount of resources such as memory and CPU cores is very large in such systems. However, the distributed topology of disaggregated server systems result in non-uniform access latency and performance, with both NUMA aspects inside each box, as well as additional access latency for remote resources. In this work, we study the effects complex NUMA topologies on application performance and propose a method for improved, NUMA-aware, mapping for virtualized environments running on disaggregated systems. Our mapping algorithm is based on pinning of virtual cores and/or migration of memory across a disaggregated system and takes into account application performance, resource contention, and utilization. The proposed method is evaluated on a 288 cores and around 1TB memory system, composed of six disaggregated commodity servers, through a combination of benchmarks and real applications such as memory intensive graph databases. Our evaluation demonstrates significant improvement over the vanilla resource mapping methods. Overall, the mapping algorithm is able to improve performance by significant magnitude compared the default Linux scheduler used in system.