Thermal modeling and analysis of storage systems

Jiang, Xunfei; Alghamdi, Mohammed I.; Zhang, Ji; Assaf, Maen Al; Ruan, Xiaojun; Muzaffar, Tausif; Qin, Xiao

doi:10.1109/pccc.2012.6407725

Cited by 12 publications

(2 citation statements)

References 16 publications

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“…, where j represents small time period in the execution process, ( ) is the inlet temperature in the j th time period. We conduct some case studies and show how to apply our model to estimate the total energy cost under different workload scenarios in a previous work [37]. Experimental results show our thermal model could accurately estimate the inlet/outlet temperature difference.…”

Section: The Cop Modelmentioning

confidence: 98%

Thermal Modeling and Analysis of Cloud Data Storage Systems

Jiang¹,

Alghamdi²,

Assaf³

et al. 2014

JCM

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An explosive increment of data and a variety of data analysis make it indispensable to lower power and cooling costs of cloud datacenters. To address this issue, we investigate the thermal impact of I/O access patterns on data storage systems. Firstly, we conduct some preliminary experiments to study the thermal behavior of a data storage node. The experimental results show that disks have ignorable thermal impacts as processors to outlet temperatures of storage nodes. We raise an approach to model the outlet temperature of a storage node. The thermal models generated by our approach gains a precision error less than 6%. Next, we investigate the thermal impact of data placement strategies on storage systems. We compare the cooling cost of storage systems governed by different data placement schemes. Our study shows that evenly distributing the data leads to highest outlet temperature for the sake of shortest execution time and energy efficiency. According to the energy consumption of various data placement schemes, we propose a thermal-ware energy-efficient data placement strategy. We further show that this work can be extended to analyze the cooling cost of data centers with massive storage capacity.

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Section: The Cop Modelmentioning

confidence: 98%

Thermal Modeling and Analysis of Cloud Data Storage Systems

Jiang¹,

Alghamdi²,

Assaf³

et al. 2014

JCM

Self Cite

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“…Regardless of the methods, CPU temperatures significantly increase, whereas disk temperatures stay unchanged. Constant disk temperatures are reasonable because disks have relatively longer heat-up periods (i.e., 30 minutes) [13]. Staying in the active state for a short period (e.g., less than one minute) has no significant impact on the disk temperature.…”

Section: B Transferring a Single Text Filementioning

confidence: 99%

PEAM: Predictive Energy-Aware Management for Storage Systems

Jiang

Alghamdi

et al. 2013

2013 IEEE Eighth International Conference on Networking, Architecture and Storage

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Abstract-This paper presents a novel Predictive EnergyAware Management (PEAM) system that is able to reduce the energy costs of storage systems by appropriately selecting data transmission methods. In particular, we evaluate the energy costs of three methods (1. transfer data without archiving and compression; 2. archive and transfer data; 3. compress and transfer data) in preliminary experiments. According to the results, we observe that the energy consumption of data transmission greatly varies case by case. We cannot simply apply one method in all cases. Therefore, we design an energy prediction model that can estimate the total energy cost of data transmission by using particular transmission methods. Based on the model, our predictive energy-aware management system can automatically select the most energy efficient method for data transmission. Our experimental results show that our system performs better than simply selecting any one among the three methods for data transmission in terms of energy efficiency.

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