Tuning Fast Memory Size based on Modeling of Page Migration for Tiered Memory

TL;DR

This paper presents Tuna, a system that models page migration to optimize fast memory size in tiered memory systems, reducing fast memory usage while maintaining performance for large applications.

Contribution

Tuna introduces a novel modeling approach for page migration impacts to determine optimal fast memory size based on workload characteristics.

Findings

Tuna achieves an average of 8.5% fast memory savings at 5% performance loss.

Tuna outperforms Microsoft Pond by saving more fast memory for the same workloads.

The system effectively balances memory utilization and performance in tiered memory architectures.

Abstract

Tiered memory, built upon a combination of fast memory and slow memory, provides a cost-effective solution to meet ever-increasing requirements from emerging applications for large memory capacity. Reducing the size of fast memory is valuable to improve memory utilization in production and reduce production costs because fast memory tends to be expensive. However, deciding the fast memory size is challenging because there is a complex interplay between application characterization and the overhead of page migration used to mitigate the impact of limited fast memory capacity. In this paper, we introduce a system, Tuna, to decide fast memory size based on modeling of page migration. Tuna uses micro-benchmarking to model the impact of page migration on application performance using three metrics. Tuna decides the fast memory size based on offline modeling results and limited information on workload telemetry. Evaluating with common big-memory applications and using 5% as the performance loss target, we show that Tuna in combination with a page management system (TPP) saves fast memory by 8.5% on average (up to 16%). This is in contrast to the 5% saving in fast memory reported by Microsoft Pond for the same workloads (BFS and SSSP) and the same performance loss target.