Automated lookahead data migration in SSD-enabled multi-tiered storage systems

Zhang, Gong; Chiu, Lawrence; Dickey, Clem; Liu, Ling; Muench, Paul; Seshadri, S.

doi:10.1109/msst.2010.5496999

Cited by 30 publications

(13 citation statements)

References 3 publications

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“…This SAS subsystem is architected to enable the benefits of the IBM System Storage Easy Tier [10]. IBM System Storage* Easy Tier is an IBM technology that automatically and dynamically manages the placement and access of data on subsystems with SSDs and HDDs.…”

Section: Ssd and Hdd Direct-attached Storage Subsystemmentioning

confidence: 99%

IBM Power Systems built with the POWER8 architecture and processors

Cahill¹,

Nguyen²,

Vega³

et al. 2015

IBM J. Res. & Dev.

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This paper describes architectures and significant implementation features of two systems in the IBM POWER8i processor-based family of servers. Specifically, the scale-out 2-socket rack server and the enterprise scale-up 16-socket rack server are detailed. The description of these systems highlights the increase in memory bandwidth from previous POWER A systems, the enablement of coherent accelerators, the highly-extensible I/O subsystem, and the high-performance directly attached storage subsystem. In addition, reliability, availability, and serviceability features are described. These systems deliver significant increases in core count, memory, and input/output bandwidth over previous POWER systemsVwith reliability and availability enhancements commensurate with the performance improvements.

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Section: Ssd and Hdd Direct-attached Storage Subsystemmentioning

confidence: 99%

IBM Power Systems built with the POWER8 architecture and processors

Cahill¹,

Nguyen²,

Vega³

et al. 2015

IBM J. Res. & Dev.

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show abstract

“…Understanding hotspot characteristics is essential to data placement strategies, such as caching at host or storage server by utilizing the "recency" [23], [2], and tiering by exploring the "frequency" aspect [24], [3].…”

Section: B Hotspot Characterizationmentioning

confidence: 99%

“…Performance optimization in enterprise storage systems has primarily relied on the ability to isolate and control workloads that were relatively well understood [1], [2], [3], [4]. With virtualized environments and cloud implementations, enterprise storage systems have to support a mixture of a large number of disparate workloads from a variety of applications [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

GraphLens: Mining Enterprise Storage Workloads Using Graph Analytics

Zhou

Seshadri

Chiu

et al. 2014

2014 IEEE International Congress on Big Data

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Abstract-Conventional methods used to analyze storage workloads have been centered on relational database technology combined with attributes-based classification algorithms. This paper presents a novel analytic architecture, GraphLens, for mining and analyzing real world storage traces. The design of our GraphLens system embodies three unique features. First, we model storage traces as heterogeneous trace graphs in order to capture diverse spatial correlations and storage access patterns using a unified analytic framework. Second, we employ and develop an innovative graph clustering method to discover interesting spatial access patterns. This enables us to better characterize important hotspots of storage access and understand hotspot movement patterns. Third, we design a unified weighted similarity measure through an iterative learning and dynamic weight refinement algorithm. With an optimal weight assignment scheme, we can efficiently combine the correlation information for each type of storage access patterns, such as random v.s. sequential, read v.s. write, to identify interesting spatial correlations hidden in the traces. Extensive evaluation on real storage traces shows GraphLens can provide scalable and reliable data analytics for better storage strategy planning and efficient data placement guidance.

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“…The quesiton becomes how to decide the optimal lookahead length. In [4], we proposed a greedy algorithm, which can compute a near optimal lookahead length depending on the granularity used. Next, we build a formal model on the system utility optimization using lookahead migration and derive the approach to compute the optimal lookahead length precisely.…”

Section: A Lookahead Migrationmentioning

confidence: 99%

Adaptive Data Migration in Multi-tiered Storage Based Cloud Environment

Zhang

Chiu

Liu

2010

2010 IEEE 3rd International Conference on Cloud Computing

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Abstract-Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated data migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective data migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated data migration in multi-tiered storage systems is how to fully release the power of data migration while guaranteeing the migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adatpive lookahead data migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive data migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of lookahead migration efficiency in our formal model develop. Second, our data migration model can adaptively determine the optimal lookahead window size, based on several parameters, to optimize the effectiveness of lookahead migration. Third, we formally and experimentally show that the adaptive data migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive lookahead migration approach with the basic migration model and show that the adaptive migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.

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Automated lookahead data migration in SSD-enabled multi-tiered storage systems

Cited by 30 publications

References 3 publications

IBM Power Systems built with the POWER8 architecture and processors

IBM Power Systems built with the POWER8 architecture and processors

GraphLens: Mining Enterprise Storage Workloads Using Graph Analytics

Adaptive Data Migration in Multi-tiered Storage Based Cloud Environment

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