DRPM: dynamic speed control for power management in server class disks

Gurumurthi, Sudhanva; Sivasubramaniam, Anand; Kandemir, Mahmut; Franke, Hubertus

doi:10.1109/isca.2003.1206998

Cited by 152 publications

(289 citation statements)

References 26 publications

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“…Each node of this system runs a copy of PVFS2 and MPICH2. To measure disk power consumption per I/O call, we used the disk energy model [9] based on the data sheets of the IBM Ultrastar 36Z15 disk [25]. Table 3 gives the important metrics used to calculate power consumption.…”

Section: Discussionmentioning

confidence: 99%

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Automated Tracing of I/O Stack

Kim

Zhang

Son

et al. 2010

Recent Advances in the Message Passing Interface

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Abstract. Efficient execution of parallel scientific applications requires high-performance storage systems designed to meet their I/O requirements. Most high-performance I/O intensive applications access multiple layers of the storage stack during their disk operations. A typical I/O request from these applications may include accesses to high-level libraries such as MPI I/O, executing on clustered parallel file systems like PVFS2, which are in turn supported by native file systems like Linux. In order to design and implement parallel applications that exercise this I/O stack, it is important to understand the flow of I/O calls through the entire storage system. Such understanding helps in identifying the potential performance and power bottlenecks in different layers of the storage hierarchy. To trace the execution of the I/O calls and to understand the complex interactions of multiple user-libraries and file systems, we propose an automatic code instrumentation technique, which enables us to collect detailed statistics of the I/O stack. Our proposed I/O tracing tool traces the flow of I/O calls across different layers of an I/O stack, and can be configured to work with different file systems and user-libraries. It also analyzes the collected information to generate output in terms of different user-specified metrics of interest.

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Section: Discussionmentioning

confidence: 99%

“…Figure 4 illustrates the computation of these metrics. To compute the I/O power, we use the power model described in [9].…”

Section: Technical Details Of Automated Instrumentationmentioning

confidence: 99%

Automated Tracing of I/O Stack

Kim

Zhang

Son

et al. 2010

Recent Advances in the Message Passing Interface

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“…Energy consumption of single disks can be reduced at either I/O level (e.g., dynamic power management [7] [17] and multi-speed disks [10] [11] [15]) or operating system level (e.g., power-aware cache management strategies [31], power-aware prefetching schemes [24] Buffer management has been widely used to boost performance of parallel disk systems [1] [27]. Previous studies showed that data buffers significantly reduce the number of disk accesses in parallel disk systems [30].…”

Section: Related Workmentioning

confidence: 99%

“…In addition to emerging high-performance disk drives with high power needs, increasing storage requirements imposed by data-intensive applications make it desirable to design energy-efficient cluster storage systems. Several novel techniques proposed to conserve energy in storage systems include dynamic power management schemes [7] [17], power-aware cache management strategies [31], power-aware prefetching schemes [24], software-directed power management techniques [25], redundancy techniques [22], and multi-speed settings [10] [11] [15]. A few innovative techniques have been developed to substantially reduce energy dissipation in traditional server clusters [21] [13] [5] [4] [8] [9].…”

Section: Introductionmentioning

confidence: 99%

ECOS: An energy-efficient cluster storage system

Ruan

Yin

Manzanares

et al. 2009

2009 IEEE 28th International Performance Computing and Communications Conference

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Abstract-Cluster storage systems are essential building blocks for many high-end computing infrastructures. Although energy conservation techniques have been intensively studied in the context of clusters and disk arrays, improving energy efficiency of cluster storage systems remains an open issue. To address this problem, we describe in this paper an approach to implementing an energyefficient cluster storage system or ECOS for short. ECOS relies on the architecture of cluster storage systems in which each I/O node manages multiple disks -one buffer disk and several data disks. Given an I/O node, the key idea behind ECOS is to redirect disk requests from data disks to the buffer disk. To balance I/O load among I/O nodes, ECOS might redirect requests from one I/O node into the others. Redirecting requests is a driving force of energy saving, and the reason is two-fold. First, ECOS makes an effort to keep buffer disks active while placing data disks into standby in a long time period to conserve energy. Second, ECOS reduces the number of disk spin downs/ups in I/O nodes. The idea of ECOS was implemented in a Linux cluster, where each I/O node contains one buffer disk and two data disks. Experimental results show that ECOS improves the energy efficiency of traditional cluster storage systems where buffer disks are not employed. Adding one extra buffer disk into each I/O node seemingly has negative impact on energy saving. Interestingly, our results indicate that ECOS equipped with extra buffer disks is more energy efficient than the same cluster storage system without the buffer disks. The implication of the experiments is that using existing data disks in I/O nodes to perform as buffer disks can achieve even higher energy efficiency.

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“…Energy consumption is emerging as a critical issue in the design of computing systems [5,14,20,21,28,32,41]. The goals of energy-aware system design include saving energy without sacrificing performance, and supporting flexible, dynamic trade-offs between energy consumption and performance.…”

Section: Introductionmentioning

confidence: 99%

On the energy consumption and performance of systems software

Grosu

Sehgal

et al. 2011

Proceedings of the 4th Annual International Conference on Systems and Storage

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Models of energy consumption and performance are necessary to understand and identify system behavior, prior to designing advanced controls that can balance out performance and energy use. This paper considers the energy consumption and performance of servers running a relatively simple file-compression workload. We found that standard techniques for system identification do not produce acceptable models of energy consumption and performance, due to the intricate interplay between the discrete nature of software and the continuous nature of energy and performance. This motivated us to perform a detailed empirical study of the energy consumption and performance of this system with varying compression algorithms and compression levels, file types, persistent storage media, CPU DVFS levels, and disk I/O schedulers. Our results identify and illustrate factors that complicate the system's energy consumption and performance, including nonlinearity, instability, and multi-dimensionality. Our results provide a basis for future work on modeling energy consumption and performance to support principled design of controllable energy-aware systems.

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DRPM: dynamic speed control for power management in server class disks

Cited by 152 publications

References 26 publications

Automated Tracing of I/O Stack

Automated Tracing of I/O Stack

ECOS: An energy-efficient cluster storage system

On the energy consumption and performance of systems software

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