Delegating Data Management to the Cloud

Transcription

1 Delegating Data Management to the Cloud Qing Gu Patricia Lago Simone Potenza presented by: Giuseppe Procaccianti

2 Up in the Clouds... Cloud Migration is said to bring many benefits.

3 Up in the Clouds... Energy Efficiency is one of the less investigated.

4 Up in the Clouds... Data as an asset is often a candidate for cloud migration.

5 Problem statement In terms of energy efficiency, when is it convenient for a company to delegate data management to the Cloud?

6 Value of Energy Energy consumption for storage devices: idle vs. active: no significant difference full vs. empty: no significant difference Solution: Value of Energy framework relate energy consumption with data value find energy wastes due to storage of valueless data

7 Data Classification: Approach Literature Study on data classification schemas 55 schemas found 17 related to data archiving 3* criteria identified * Position criterion considered irrelevant for the purpose of this presentation

8 Data Classification Criterion: Retention Definition: 2 variables, 1 constant retainment_period is_retainment_set retainment_threshold Long Retention time (LR) if: is_retainment_set = TRUE AND retainment_period > retainment_threshold

9 Data Classification Criterion: Frequency of usage Definition: 1 variable, 1 constant access_frequency_index [1] frequency_threshold High frequency access (H) if: access_frequency_index > frequency_threshold [1] Chen, Ying. "Information valuation for information lifecycle management." Autonomic Computing, ICAC Proceedings. Second International Conference on. IEEE, 2005.

10 Data Classification Criterion: Modification Definition: 1 variable, 1 constant last_modification_period last_modified_period_threshold Young file (Y) if: last_modification_period > last_modified_period_threshold

11 Data Classification Schema Frequency of Usage Modification Retention Class H Y LR Hot H Y SR Hot H O LR Cold H O SR Cold L Y LR Cold L Y SR Cold L O LR Cold L O SR Frozen

12 Value of Energy: metrics Metric WpGB IdlePower / raw_size EffectivePower Hot*WpGB * PUE WastedPower (Frozen+Cold) *WpGB*PUE PotentiallyEffectivePower FreeSpace * WpGB * PUE ServiceLevelPower raw_size*capacity *WpGB*PUE Description Amount of power needed to keep available a GB of storage space Unit W/GB Amount of power used to store hot data Watt (W) Amount of power used to store cold/frozen data Watt (W) Power used to provide free storage space that can be used for hot data Watt (W) Power used for RAID and data redundancy in general Watt (W)

13 Industrial Case Study Partner: Dutch telecommunication provider Motivation: demonstrate economic/environmental benefits of Cloud archiving service

14 Industrial Case Study /2 Test storage system: 24 disks Raw space: GB Usable capacity: GB Transfer Rate: MB/s PUE: 1.5 Hot data size 980 GB Cold data size 2640 GB Frozen data size 5280 GB

15 Industrial Case Study /3 Before Migration After Migration EffectivePower W W WastedPower W 0W PotentiallyEffectivePower W W ServiceLevelPower W W

16 Industrial Case Study /4 Too much free space may result in energy wastes as well This waste can be quantified using data growth metrics Assuming an annual growth rate of 30% for all data classes, after 2 years: Future Hot data size 1656 GB Future Cold data size 4462 GB Future Frozen data size 8923 GB

17 Industrial Case Study /5 Before Migration After Migration EffectivePower 93 W 93 W WastedPower 518 W 1034 W PotentiallyEffectivePower 580 W 64 W ServiceLevelPower 170 W 170W

18 Conclusions We evaluate data cloud migration from the Customer perspective. Migration to the Cloud is beneficial....under certain circumstances!

19 Conclusions Useful energy and dynamicity (e.g. Data Growth) must be taken into account.

20 For the future... More precise energy calculations Extend data classification criteria for different contexts [2] Investigate environmental and economic impacts [2] Masanet, E., Shehabi, A., Ramakrishnan, L., Liang, J., Ma, X.,Walker, B., Hendrix, V., and P. Mantha (2013 ). The Energy Efficiency Potential of CloudBased Software: A U.S. Case Study. Lawrence Berkeley National Laboratory, Berkeley, California.

21 Thank you!

22 Hidden...Cost comparison (weekly costs)

23 Hidden...Cost comparison (with Data Growth)