Modeling the Linux page cache for accurate simulation of data-intensive applications

TL;DR

This paper introduces a detailed Linux page cache simulation model integrated into WRENCH, significantly improving the accuracy of data-intensive application performance predictions in simulation environments.

Contribution

The authors develop and implement a comprehensive Linux page cache model within WRENCH, enabling more accurate simulation of I/O behavior for data-intensive applications.

Findings

Model reduces simulation error by up to tenfold.

Accurately simulates both single-threaded and multithreaded applications.

Effective for local and remote I/O scenarios.

Abstract

The emergence of Big Data in recent years has resulted in a growing need for efficient data processing solutions. While infrastructures with sufficient compute power are available, the I/O bottleneck remains. The Linux page cache is an efficient approach to reduce I/O overheads, but few experimental studies of its interactions with Big Data applications exist, partly due to limitations of real-world experiments. Simulation is a popular approach to address these issues, however, existing simulation frameworks do not simulate page caching fully, or even at all. As a result, simulation-based performance studies of data-intensive applications lead to inaccurate results. In this paper, we propose an I/O simulation model that includes the key features of the Linux page cache. We have implemented this model as part of the WRENCH workflow simulation framework, which itself builds on the popular SimGrid distributed systems simulation framework. Our model and its implementation enable the simulation of both single-threaded and multithreaded applications, and of both writeback and writethrough caches for local or network-based filesystems. We evaluate the accuracy of our model in different conditions, including sequential and concurrent applications, as well as local and remote I/Os. We find that our page cache model reduces the simulation error by up to an order of magnitude when compared to state-of-the-art, cacheless simulations.