Providing Quality of Service Support in Object-Based File System

Wu, Kan; Brandt,

doi:10.1109/msst.2007.4367971

Cited by 19 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This optimal value is important because it is the basis for allocating bandwidth to clients. Current traffic control solutions either assume a certain fixed value as the top performance of the system [14,40,44], use a time-sharing scheduler [10,12,17,24,29,34,45,49], or employ a simple control-theoretic heuristic: slow-start, fast fallback [37,44,50]. Some of them require that the storage system's performance conforms to a mathematical model [18,39].…”

Section: Related Workmentioning

confidence: 99%

ASCAR: Automating contention management for high-performance storage systems

Miller

et al. 2015

2015 31st Symposium on Mass Storage Systems and Technologies (MSST)

View full text Add to dashboard Cite

Abstract-High-performance parallel storage systems, such as those used by supercomputers and data centers, can suffer from performance degradation when a large number of clients are contending for limited resources, like bandwidth. These contentions lower the efficiency of the system and cause unwanted speed variances. We present the Automatic Storage Contention Alleviation and Reduction system (ASCAR), a storage traffic management system for improving the bandwidth utilization and fairness of resource allocation. ASCAR regulates I/O traffic from the clients using a rule based algorithm that controls the congestion window and rate limit. The rule-based client controllers are fast responding to burst I/O because no runtime coordination between clients or with a central coordinator is needed; they are also autonomous so the system has no scale-out bottleneck. Finding optimal rules can be a challenging task that requires expertise and numerous experiments. ASCAR includes a SHAred-nothing Rule Producer (SHARP) that produces rules in an unsupervised manner by systematically exploring the solution space of possible rule designs and evaluating the target workload under the candidate rule sets. Evaluation shows that our ASCAR prototype can improve the throughput of all tested workloadssome by as much as 35%. ASCAR improves the throughput of a NASA NPB BTIO checkpoint workload by 33.5% and reduces its speed variance by 55.4% at the same time. The optimization time and controller overhead are unrelated to the scale of the system; thus, it has the potential to support future large-scale systems that can have millions of clients and thousands of servers. As a pure client-side solution, ASCAR needs no change to either the hardware or server software.

show abstract

Section: Related Workmentioning

confidence: 99%

ASCAR: Automating contention management for high-performance storage systems

Miller

et al. 2015

2015 31st Symposium on Mass Storage Systems and Technologies (MSST)

View full text Add to dashboard Cite

show abstract

“…One of the key differences in our concept of storage lockers is support for QoS to guarantee performance among competing workloads. Initial work to support QoS in Ceph [32] focused on designing a new object-aware disk scheduler for use in object storage servers. Our QoS scheme is derived from existing work [28,26,33] however we add a distributed reservation and coordination scheme to support distributed clients.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous object storage with QoS for scientific and commercial big data

Brim

Dillow

Oral

et al. 2013

Proceedings of the 8th Parallel Data Storage Workshop

View full text Add to dashboard Cite

This paper presents our design for an asynchronous object storage system intended for use in scientific and commercial big data workloads. Use cases from the target workload domains are used to motivate the key abstractions used in the application programming interface (API). The architecture of the Scalable Object Store (SOS), a prototype object storage system that supports the API's facilities, is presented. The SOS serves as a vehicle for future research into scalable and resilient big data object storage. We briefly review our research into providing efficient storage servers capable of providing quality of service (QoS) contracts relevant for big data use cases.

show abstract

“…Some of them are presented in this section. In [7] a framework supporting QoS built upon the Ceph object based distributed storage system is proposed. The framework, which is called Bourbon, allows for isolating of storage performance between different classes of workloads.…”

Section: State Of the Artmentioning

confidence: 99%

Management Methods in Sla-Aware Distributed Storage Systems

Nikołow

Słota

Lakovic

et al. 2012

csci

View full text Add to dashboard Cite

Traditional data storage systems provide access to user’s data on the “besteffort” basis. While this paradigm is sufficient in many use cases it becomesan obstacle for applications with Quality of Service (QoS) constraints. ServiceLevel Agreement (SLA) is a part of the contract agreed between the serviceprovider and the client and contains a set of well defined QoS requirementsregarding the provided service and the penalties applied in case of violations.In the paper we propose a set of SLA parameters and QoS metrics relevantto data storage processes and the management methods necessary for avoidingSLA violations. A key assumption in the proposed approach is that the underlyingdistributed storage system does not provide functionality for resource orbandwidth reservation for a given client request

show abstract

Providing Quality of Service Support in Object-Based File System

Abstract: Bourbon is a quality of service framework designed to work with the Ceph object-based storage system. Ceph is a highly scalable distributed file system that can scale up to tens of thousands of object-based storage devices (OSDs)

Cited by 19 publications

References 22 publications

ASCAR: Automating contention management for high-performance storage systems

ASCAR: Automating contention management for high-performance storage systems

Asynchronous object storage with QoS for scientific and commercial big data

Management Methods in Sla-Aware Distributed Storage Systems

Contact Info

Product

Resources

About