1. Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter
Published on: HotPower ‘09
Presentation By: Liang Hao
Tuesday, August 03, 2010

2. Author Christopher Stewart from The Ohio State University
Kai Shen from University of Rochester
Publications:
Of Christopher Stewart
Of Kai Shen

3. Motivation
To reduce datacenters’ dependence on costly and less clean energy from the grid
Hence to maximize the use of renewable energies
To explore the possibility of evaluating the request-level power consumption

4. Problems Datacenters powered by renewable energy need backups
Applications in the datacenter must be available 24x7
But wind and solar energy are intermittent
Datacenters powered by renewable energy need backups
Primary options: Grid, generator, battery
Alternatives are either dirty and/or costly
Renewables are precious!
renewable = joule converted from solar/wind
The preferred energy source
Available only sometimes and costly to store

5. Opportunities Capacity planning Load balancing
Compute power should fluctuate with intermittent outages—i.e., turn machines off
Load balancing
Route requests to datacenters with unused renewables
Migrate services to datacenters with renewables

6. Intermittency 1.Datacenter Modeling

7. Intermittency 1.Datacenter Modeling
Automatic transfer switch (ATS)
Input 2 power sources, outputs 1 power source
Monitors power from the primary source
When power from primary dips below threshold, ATS switches to secondary
When primary exhibits power of threshold, ATS switches back to secondary

8. Intermittency 1.Datacenter Modeling
Key parameters related to the ATS
to ensure dependability or reliability, threshold equals peak consumption
to make full use of renewable energies, scale down the threshold

9. Intermittency 2.Wind Intermittency
Battery backup too costly
But if we apply renewable-aware management, there is enough supply from other datacenters

10. Intermittency 3.Renewable Utility
when threshold set to peak power, the utility of wind turbine power production drops 65% compared to when threshold set to zero.

11. Intermittency 3.Renewable Utility
Economical feasibility (metric: cost per KW-hour)
Average price for commercial electricity $0.10 KW-hour
$2.4M to erect a wind turbine that is connected (directly) to a datacenter [European Wind Energy Assc.]
$1.6M installation
2% annual maintenance fees
Lifetime of turbine: 20 years
Datacenter at CA or MT could use 24M KWh
Either high power consumption or zero threshold
Wind-powered datacenter in MT: $0.04 KWh

12. Request-level Event Profiling
To estimate the power consumption of individual requests
With quantized statistics, scheduler could possibly route some requests to datacenters with unused renewables

13. Request-level Event Profiling
Tracing the route that a request go through, including CPU usage and other hardware events.
We configured the performance counters to assemble three predictor metrics for our power model:
L2 cache requests per CPU cycle (Ccache),
memory transactions per CPU cycle (Cmem),
and the ratio of non-halt CPU cycles (Cnonhalt).

14. Request-level Event Profiling
Power consumption is calculated according to the expression below
where P’s are coefficient parameters for the linear model are constants that approximate ceiling values for the predictor metrics.

15. Request-level Event Profiling micro benchmarks
1) idle
2) CPU spinning with no access to cache or memory
3/4) Apache web server with either short requests (no more than 1KB files) or long requests (files of 100 KB– 1 MB)
5/6) OpenSSL RSA encryption/decryption using either a small key or a large key. We also use four full server workloads
7) TPC-C running on the MySQL database
8) TPC-H running on the MySQL database
9) RUBiS
10) WeBWorK .

16. Request-level Event Profiling
Request workloads executed in isolation
WattsUp power meter measures watts and joules
Processor was not adjusted during tests

17. Vision

18. Reference
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[2] Realistic nonstationary workloads.
[3] Wind power.
[4] Google solar panel project.
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