Memory-Disaggregated In-Memory Object Store Framework for Big Data Applications

TL;DR

This paper introduces a memory disaggregated in-memory object store framework for big data applications, enabling efficient distributed data management with minimal performance penalty, leveraging ThymesisFlow and Apache Arrow Plasma.

Contribution

It extends Apache Arrow Plasma to support distributed systems using memory disaggregation, facilitating easier and more efficient big data processing across multiple nodes.

Findings

Modest performance penalty (~6.5 vs ~5.75 GiB/s) for remote memory access.

Framework enables efficient data sharing across nodes in big data applications.

Open-source implementation available for further research and development.

Abstract

The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes can directly access memory on adjacent nodes and are therefore able to overcome local memory restrictions, introducing a new data management paradigm for distributed computing. This paper proposes and demonstrates a memory disaggregated in-memory object store framework for big data applications by leveraging the newly introduced ThymesisFlow memory disaggregation system. The framework extends the functionality of the pre-existing Apache Arrow Plasma object store framework to distributed systems by enabling clients to easily and efficiently produce and consume data objects across multiple compute nodes. This allows big data applications to increasingly leverage parallel processing at reduced development costs. In addition, the paper includes latency and throughput measurements that indicate only a modest performance penalty is incurred for remote disaggregated memory access as opposed to local (~6.5 vs ~5.75 GiB/s). The results can be used to guide the design of future systems that leverage memory disaggregation as well as the newly presented framework. This work is open-source and publicly accessible at https://doi.org/10.5281/zenodo.6368998.