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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
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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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MSN 数学媒体与信息存储
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