394 research outputs found
A Robust Fault-Tolerant and Scalable Cluster-wide Deduplication for Shared-Nothing Storage Systems
Deduplication has been largely employed in distributed storage systems to
improve space efficiency. Traditional deduplication research ignores the design
specifications of shared-nothing distributed storage systems such as no central
metadata bottleneck, scalability, and storage rebalancing. Further,
deduplication introduces transactional changes, which are prone to errors in
the event of a system failure, resulting in inconsistencies in data and
deduplication metadata. In this paper, we propose a robust, fault-tolerant and
scalable cluster-wide deduplication that can eliminate duplicate copies across
the cluster. We design a distributed deduplication metadata shard which
guarantees performance scalability while preserving the design constraints of
shared- nothing storage systems. The placement of chunks and deduplication
metadata is made cluster-wide based on the content fingerprint of chunks. To
ensure transactional consistency and garbage identification, we employ a
flag-based asynchronous consistency mechanism. We implement the proposed
deduplication on Ceph. The evaluation shows high disk-space savings with
minimal performance degradation as well as high robustness in the event of
sudden server failure.Comment: 6 Pages including reference
Exploring Scientific Application Performance Using Large Scale Object Storage
One of the major performance and scalability bottlenecks in large scientific
applications is parallel reading and writing to supercomputer I/O systems. The
usage of parallel file systems and consistency requirements of POSIX, that all
the traditional HPC parallel I/O interfaces adhere to, pose limitations to the
scalability of scientific applications. Object storage is a widely used storage
technology in cloud computing and is more frequently proposed for HPC workload
to address and improve the current scalability and performance of I/O in
scientific applications. While object storage is a promising technology, it is
still unclear how scientific applications will use object storage and what the
main performance benefits will be. This work addresses these questions, by
emulating an object storage used by a traditional scientific application and
evaluating potential performance benefits. We show that scientific applications
can benefit from the usage of object storage on large scales.Comment: Preprint submitted to WOPSSS workshop at ISC 201
Checkpointing as a Service in Heterogeneous Cloud Environments
A non-invasive, cloud-agnostic approach is demonstrated for extending
existing cloud platforms to include checkpoint-restart capability. Most cloud
platforms currently rely on each application to provide its own fault
tolerance. A uniform mechanism within the cloud itself serves two purposes: (a)
direct support for long-running jobs, which would otherwise require a custom
fault-tolerant mechanism for each application; and (b) the administrative
capability to manage an over-subscribed cloud by temporarily swapping out jobs
when higher priority jobs arrive. An advantage of this uniform approach is that
it also supports parallel and distributed computations, over both TCP and
InfiniBand, thus allowing traditional HPC applications to take advantage of an
existing cloud infrastructure. Additionally, an integrated health-monitoring
mechanism detects when long-running jobs either fail or incur exceptionally low
performance, perhaps due to resource starvation, and proactively suspends the
job. The cloud-agnostic feature is demonstrated by applying the implementation
to two very different cloud platforms: Snooze and OpenStack. The use of a
cloud-agnostic architecture also enables, for the first time, migration of
applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201
Assise: Performance and Availability via NVM Colocation in a Distributed File System
The adoption of very low latency persistent memory modules (PMMs) upends the
long-established model of disaggregated file system access. Instead, by
colocating computation and PMM storage, we can provide applications much higher
I/O performance, sub-second application failover, and strong consistency. To
demonstrate this, we built the Assise distributed file system, based on a
persistent, replicated coherence protocol for managing a set of
server-colocated PMMs as a fast, crash-recoverable cache between applications
and slower disaggregated storage, such as SSDs. Unlike disaggregated file
systems, Assise maximizes locality for all file IO by carrying out IO on
colocated PMM whenever possible and minimizes coherence overhead by maintaining
consistency at IO operation granularity, rather than at fixed block sizes.
We compare Assise to Ceph/Bluestore, NFS, and Octopus on a cluster with Intel
Optane DC PMMs and SSDs for common cloud applications and benchmarks, such as
LevelDB, Postfix, and FileBench. We find that Assise improves write latency up
to 22x, throughput up to 56x, fail-over time up to 103x, and scales up to 6x
better than its counterparts, while providing stronger consistency semantics.
Assise promises to beat the MinuteSort world record by 1.5x
An Experiment on Bare-Metal BigData Provisioning
Many BigData customers use on-demand platforms in the cloud, where they can get a dedicated virtual cluster in a couple of minutes and pay only for the time they use. Increasingly, there is a demand for bare-metal bigdata solutions for applications that cannot tolerate the unpredictability and performance degradation of virtualized systems. Existing bare-metal solutions can introduce delays of 10s of minutes to provision a cluster by installing operating systems and applications on the local disks of servers. This has motivated recent research developing sophisticated mechanisms to optimize this installation. These approaches assume that using network mounted boot disks incur unacceptable run-time overhead. Our analysis suggest that while this assumption is true for application data, it is incorrect for operating systems and applications, and network mounting the boot disk and applications result in negligible run-time impact while leading to faster provisioning time.This research was supported in part by the MassTech
Collaborative Research Matching Grant Program, NSF
awards 1347525 and 1414119 and several commercial
partners of the Massachusetts Open Cloud who may be
found at http://www.massopencloud.or
Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service
An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1
Evaluating the benefits of key-value databases for scientific applications
The convergence of Big Data applications with High-Performance Computing requires new methodologies to store, manage and process large amounts of information. Traditional storage solutions are unable to scale and that results in complex coding strategies. For example, the brain atlas of the Human Brain Project has the challenge to process large amounts of high-resolution brain images. Given the computing needs, we study the effects of replacing a traditional storage system with a distributed Key-Value database on a cell segmentation application. The original code uses HDF5 files on GPFS through an intricate interface, imposing synchronizations. On the other hand, by using Apache Cassandra or ScyllaDB through Hecuba, the application code is greatly simplified. Thanks to the Key-Value data model, the number of synchronizations is reduced and the time dedicated to I/O scales when increasing the number of nodes.This project/research has received funding from the European Unions Horizon
2020 Framework Programme for Research and Innovation under the Speci c
Grant Agreement No. 720270 (Human Brain Project SGA1) and the Speci c
Grant Agreement No. 785907 (Human Brain Project SGA2). This work has also
been supported by the Spanish Government (SEV2015-0493), by the Spanish
Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat
de Catalunya (contract 2017-SGR-1414).Postprint (author's final draft
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