Analysis in the Data Path of an Object-Centric Data Management System

Warren, Richard; Soumagne, Jérôme; Mu, Jingqing; Tang, Houjun; Byna, Suren; Dong, Bo; Koziol, Quincey

doi:10.1109/hipc.2019.00020

Cited by 1 publication

(1 citation statement)

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“…The ActiveSpaces framework [6] is an example: a storage system capable of moving (and executing) code where the data is being staged. Warren et al [40] discuss an object/array centric approach that is able to use shared libraries or application executables in order to perform what we will be calling active operations. Those approaches show the advantages of active strategies; however they provide a low-level interface and expect extensive knowledge (from the application developer) regarding the architecture in terms of network and computation resources.…”

Section: Active Object Storesmentioning

confidence: 99%

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

Barceló,

Queralt,

Cortes

2021

Preprint

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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.More specifically, having NVM devices presents additional opportunities, such as in-place execution. Being able to use the NVM from within the storage system while taking advantage of in-place execution (thanks to the byte-addressable nature of the NVM), in conjunction with the computing capabilities of hyper-converged nodes, can lead to active object stores that greatly outperform their non-active counterparts.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.

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Section: Active Object Storesmentioning

confidence: 99%