Fragmented ARES: Dynamic Storage for Large Objects

TL;DR

This paper presents a dynamic, consistent distributed storage system for large objects, integrating ARES with COBFS, and evaluates its performance and tradeoffs through extensive experiments.

Contribution

It introduces a novel combination of ARES with COBFS using block fragmentation and erasure coding to improve large object storage in distributed systems.

Findings

Enhanced storage efficiency with erasure coding

Improved operation latency demonstrated in experiments

Robustness and consistency validated on Emulab and AWS

Abstract

Data availability is one of the most important features in distributed storage systems, made possible by data replication. Nowadays data are generated rapidly and the goal to develop efficient, scalable and reliable storage systems has become one of the major challenges for high performance computing. In this work, we develop a dynamic, robust and strongly consistent distributed storage implementation suitable for handling large objects (such as files). We do so by integrating an Adaptive, Reconfigurable, Atomic Storage framework, called ARES, with a distributed file system, called COBFS, which relies on a block fragmentation technique to handle large objects. With the addition of ARES, we also enable the use of an erasure-coded algorithm to further split our data and to potentially improve storage efficiency at the replica servers and operation latency. To put the practicality of our outcomes at test, we conduct an in-depth experimental evaluation on the Emulab and AWS EC2 testbeds, illustrating the benefits of our approaches, as well as other interesting tradeoffs.