Revisiting Active Object Stores: Bringing Data Locality to the Limit With NVM

TL;DR

This paper proposes an active object store architecture optimized for NVM, demonstrating significant performance improvements in data-intensive applications by enhancing data locality and computation placement.

Contribution

It introduces a novel active object store design tailored for NVM, evaluating its performance and implications on data locality and execution times.

Findings

Execution times reduced by up to 90% in various applications

Active object store improves data locality and performance on NVM

System benefits from hyper-converged nodes and increased compute capabilities

Abstract

Object stores are widely used software stacks that achieve excellent scale-out with a well-defined interface and robust performance. However, their traditional get/put interface is unable to exploit data locality at its fullest, and limits reaching its peak performance. In particular, there is one way to improve data locality that has not yet achieved mainstream adoption: the active object store. Although there are some projects that have implemented the main idea of the active object store such as Swift's Storlets or Ceph Object Classes, the scope of these implementations is limited. We believe that there is a huge potential for active object stores in the current status quo. Hyper-converged nodes are bringing more computing capabilities to storage nodes --and viceversa. The proliferation of non-volatile memory (NVM) technology is blurring the line between system memory (fast and scarce) and block devices (slow and abundant). More and more applications need to manage a sheer amount of data (data analytics, Big Data, Machine Learning & AI, etc.), demanding bigger clusters and more complex computations. All these elements are potential game changers that need to be evaluated in the scope of active object stores. In this article we propose an active object store software stack and evaluate it on an NVM-populated node. We will show how this setup is able to reduce execution times from 10% up to more than 90% in a variety of representative application scenarios. Our discussion will focus on the active aspect of the system as well as on the implications of the memory configuration.