Agility and Performance in Elastic Distributed Storage

Abstract

Elastic storage systems can be expanded or contracted to meet current demand, allowing servers to be turned off or used for other tasks. However, the usefulness of an elastic distributed storage system is limited by its agility: how quickly it can increase or decrease its number of servers. Due to the large amount of data they must migrate during elastic resizing, state of the art designs usually have to make painful trade-offs among performance, elasticity, and agility.This article describes the state of the art in elastic storage and a new system, called SpringFS, that can quickly change its number of active servers, while retaining elasticity and performance goals. SpringFS uses a novel technique, termed bounded write offloading, that restricts the set of servers where writes to overloaded servers are redirected. This technique, combined with the read offloading and passive migration policies used in SpringFS, minimizes the work needed before deactivation or activation of servers. Analysis of real-world traces from Hadoop deployments at Facebook and various Cloudera customers and experiments with the SpringFS prototype confirm SpringFS's agility, show that it reduces the amount of data migrated for elastic resizing by up to two orders of magnitude, and show that it cuts the percentage of active servers required by 67-82%, outdoing state-of-the-art designs by 6-120%.

Mapping and scheduling HPC applications for optimizing I/O

“…Other works such as those of Lim et al [21] or Cheng et al [9] present elastic solutions using Hadoop Distributed File System (HDFS). SpringFS [30] presents an elastic filesystem for Cloud computing platforms.…”

Autoscaling tiered cloud storage in Anna

“…This is particularly important for writes to blocks mastered at the inactive node. SpringFS [60] optimizes this work by finding a minimum number of machines needed for offloading. By contrast, Anna supports multi-master updates and selective key replication.…”

Combining malleability and I/O control mechanisms to enhance the execution of multiple applications

“…Another solution is IKAROS ( Filippidis et al, 2016 ), that permits the dynamic creation of clusters of storage nodes per job including both local and remote storage resources, based on application characteristics. In the context of cloud computing, some solutions for elasticity in I/O have been proposed, such as SpringFS ( Xu et al, 2014 ) as well as solutions based on the Hadoop Distributed File System (HDFS) ( Lim et al, 2010;Cheng et al, 2012 ). Nonetheless, by and large, existing malleability solutions ( Casanova et al, 2014;Kalé et al, 2002;Klein and Pérez, 2011;Hungershofer, 2004;Cirne and Berman, 2002;Prabhakaran et al, 2015 ) are mostly confined to the elasticity of compute and memory allocations.…”