MSN 数学媒体与信息存储 1/27 Zhuo Liu, Fei Wu, Xiao Qin, Changsheng Xie, Jian Zhou, and Jianzong Wang TRACER: A Trace Replay Tool to Evaluate Energy-Efficiency of

MSN 数学媒体与信息存储

1/27

Zhuo Liu, Fei Wu , Xiao Qin, Changsheng Xie, Jian Zhou, and Jianzong Wang

TRACER: A Trace Replay Tool to Evaluate Energy-Efficiency of

Mass Storage Systems

WNLO, Huazhong University of Science and Technology; Computer Science Department, Auburn University

[email protected]


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Outline

I. Motivation Why do we need TRACER?

II. Architecture What does TRACER consist of? How does TRACER work and look like?

III. Trace-replay control scheme How does TRACER control load intensity?

IV. Measurement How well does TRACER work?

V. Conclusion and future work


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I Motivation

New energy conservation techniques in storage systems constantly spring up: such as MAID, DRPM, PDC…

Lack of systematic and uniform way to evaluate them: present benchmarks and standards are most

for CPU-intensive applications


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Current power-aware techniques


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Energy-efficiency Standards

SPEC (CPU-intensive) Metric: Java_ops/Watt

SUN swap (CPU-intensive) Metric: Performance/(Space*Watt)

Joule Sort (CPU-intensive) Metric: Sort_ops/Joule

Energy Star SNIA green

Classification of storage devices Standards mainly for idle-mode tests


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Power increases with load intensity

SNIA-Green


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What should TRACER do?

Test the power consumptions

Produce different modes of IO load

Regulate IO load intensity: 10%-100%

Metrics: to evaluate energy-efficiency

TRACER


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II. Architecture of TRACER

TRACER consists of four parts:• Evaluation host• Workload generator• Power analyzer• Storage systems under test


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What does TRACER consist of?

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How is TRACER implemented?

Storage Device

Evaluation Host

Power Analyzer

Workload Generator

Ethernet Connects

Workload Generator

Ethernet LAN

Power Sensor

FC swicth

FC Connects

Evaluation Host

Power Analyzer


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GUI of TRACER


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III. Trace-replay control scheme

The structure of a blocktrace file


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Load-control algorithm for trace replay


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IV. Measurement: environment


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Measurement: hardware connects


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Measurement : traces

Three types of traces: 125 traces we connected using IOmeter

• Request size: 512B, 4KB, 16KB, 64KB, 1MB• Random/sequential%: 0% ,25%, 50%, 75%,100%• Read/write%: 0%, 25% ,50% ,75%, 100%

HP lab: cello 96 and cello 99 The web server trace (FAST’09-BORG)


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Measurement : evaluation metrics

IO Throughput/Power Consumption IOPS/Watt MBPS/Kilowatt


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V. Measurement: results

1. Accuracy of load control for 125 traces Accuracy % =Measured intensity% / configured%

2. How Energy-efficiency is influenced By load intensity By random% By read%

3. Accuracy for Real traces HP lab cello99 cello96, web server traces


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Accuracy of load control


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E-E influenced by load%


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E-E influenced by random%


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E-E influenced by read%


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Load control for real trace


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Accuracy of load control for real trace


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Conclusion and Future Work

1 TRACER is accurate, efficient and useful.

2 Storage system is more energy-efficient under higher load intensity, under lower random rate.

3 As temperature influences system’s performance and power, it’s necessary to add temperature as part of evaluation metrics.

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Add Temperature metrics

Storage Device

Evaluation Host

Power Analyzer

Workload Generator

Ethernet Connects

Workload Generator

Ethernet LAN

Power Sensor

FC swicth

FC Connects

Evaluation Host

Power Analyzer Temp sensor


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Any question?

[email protected]

Documents

MSN 数学媒体与信息存储 1/27 Zhuo Liu, Fei Wu, Xiao Qin, Changsheng Xie, Jian Zhou, and Jianzong Wang TRACER: A Trace Replay Tool to Evaluate Energy-Efficiency of