Effective Cache Apportioning for Performance Isolation Under Compiler Guidance

TL;DR

Com-CAS is a dynamic cache apportioning system that uses compiler analysis and machine learning to adapt cache allocations in real-time, improving performance and SLA adherence in multi-core servers.

Contribution

It introduces a novel compiler-guided, machine learning-based cache partitioning approach that adapts to application phase behavior without hardware modifications.

Findings

15% average throughput improvement over unpartitioned cache

20% performance gain over state-of-the-art KPart

Maintains SLA compliance with minimal worst-case degradation

Abstract

With a growing number of cores in modern high-performance servers, effective sharing of the last level cache (LLC) is more critical than ever. The primary agenda of such systems is to maximize performance by efficiently supporting multi-tenancy of diverse workloads. However, this could be particularly challenging to achieve in practice, because modern workloads exhibit dynamic phase behaviour, which causes their cache requirements & sensitivities to vary at finer granularities during execution. Unfortunately, existing systems are oblivious to the application phase behavior, and are unable to detect and react quickly enough to these rapidly changing cache requirements, often incurring significant performance degradation. In this paper, we propose Com-CAS, a new apportioning system that provides dynamic cache allocations for co-executing applications. Com-CAS differs from the existing cache partitioning systems by adapting to the dynamic cache requirements of applications just-in-time, as opposed to reacting, without any hardware modifications. The front-end of Com-CAS consists of compiler-analysis equipped with machine learning mechanisms to predict cache requirements, while the back-end consists of proactive scheduler that dynamically apportions LLC amongst co-executing applications leveraging Intel Cache Allocation Technology (CAT). Com-CAS's partitioning scheme utilizes the compiler-generated information across finer granularities to predict the rapidly changing dynamic application behaviors, while simultaneously maintaining data locality. Our experiments show that Com-CAS improves average weighted throughput by 15% over unpartitioned cache system, and outperforms state-of-the-art partitioning system KPart by 20%, while maintaining the worst individual application completion time degradation to meet various Service-Level Agreement (SLA) requirements.