This document discusses automatic energy-aware scheduling for distributed computing. It summarizes the Green500 list which ranks supercomputers by energy efficiency. Server virtualization can improve efficiency by consolidating workloads. Automatic scheduling that places applications dynamically based on power usage could address underutilization. Current solutions include VMturbo's intelligent workload management and using machine learning to model scheduling. The conclusion is that automatic energy-based scheduling should be more widely adopted to further improve supercomputer efficiency.

1. EEDC
34330
Automatic
Execution
Environments for Energy-Aware
Distributed Scheduling
Computing
European Master in Distributed A GREEN Project
Computing – EMDC
Group members:
Maria Stylianou –
marsty5@gmail.com
Georgia Christodoulidou –
geochris71@gmail.com

2. Outline
●
Problem Statement
●
Green500 List
●
Automatic Energy-Aware Scheduling
●
Conclusions
2

3. Problem Statement
Energy-costs dominate!
Performance = Speed
Reliability
Bad Effects: Availability
Usability
→ Huge increase in total cost
for maintaining a data center
3

4. The Green500 List
●
Description
●
Top10 supercomputers
●
Trends for energy
consumption decrease
4

5. Description
●
Started in April 2005
●
Ranking of the most energy-efficient
supercomputers in the world
●
Aim
→ Raise awareness to other performance
metrics
●
Performance per watt
●
Energy efficiency for improved reliability
→ Encourage “greener” supercomputers
5

6. Top10 Supercomputers
Retrieved from http://www.green500.org/lists/2011/11/top/list.php
6

7. Trends for energy consumption
decrease
●
Aggregate many low power processors
●
Use energy-efficient accelerators from
gaming market
No use of automatic energy-based
scheduling!
7

8. Automatic Energy-Aware Scheduling
●
Problem Restatement
●
Energy Management Technologies
●
How to address the problem
●
Server Virtualization
●
Additional Help
●
What's in the market
8

9. Problem Restatement
●
Previously said: Energy-costs dominate!
●
Peaks are fronted by adding servers
→ Servers are underutilized
“the average server utilization varies between
11% and 50% for workloads from sports,
e-commerce, financial, and Internet proxy
clusters.”
9

10. Energy Management Technologies
●
Awareness
●
Energy consumption in data centers
●
Substantial carbon footprint
Solutions
Hardware Level System Level
Build energy Manage power
efficiency into consumption of
components & servers & systems
systems design adapting to changing
conditions in the
workload
10

11. How to address the problem
Power-aware dynamic app placement!
This is...
Automatic Energy-aware scheduling!
11

12. Server Virtualization
●
Appeared in 1960s
●
Disruptive business model
●
Aim: Workload consolidation
→ Reduce the energy costs
12

13. Server Virtualization
●
P1: Servers are heavily underutilized
→ Static
consolidation
of workloads
→ Reduction
of servers
Reference [1]
13

14. Server Virtualization
●
P2: Servers are underutilized for long
periods/day
→ Consolidation
of workloads
→ Servers in a
low power state
Reference [1]
14

15. Server Virtualization
●
P3: Low resource utilization of applications
●
P4: Applications have a complementary
resource behavior
→ Dynamic consolidation of workloads
15

16. Server Virtualization
Scheduling policies
● Random: assigns the tasks randomly
→ only if the task can fit into a server
● Round Robin: assigns a task to each available node
→ implies a maximization of the # of resources to a task
→ implies a sparse usage of the resources
● Backfilling: fills in turned on machines before starting offline ones
● Dynamic Backfilling: able to move tasks between machines
→ provide a higher consolidation level.
16

17. Server Virtualization
●
Benefits
●
More efficient utilization of hardware
●
Reduced floor space
●
Reduced facilities management costs
●
Hide the heterogeneity in server hardware
●
Make apps more portable/resilient to hardware
changes
17

18. Additional Help – Hardware Level
Cooling
●
Automatic Air Cooling
●
Water Cooling
“water as a coolant has the ability to capture heat
about 4,000 times more efficiently than air” ~IBM
→ Aquasar Supercomputer – IBM Research Zurich
Use of powerful chip watercoolers
→ no need of the water to be chilled
in lower temperatures
18

19. Additional Help – System Level
Machine Learning
●
Scheduling Information → use predictive methods
not available to model missing information
●
Dynamic Backfilling Scheduling Policy
1st step 2nd step
→ Change static data by estimated data
19

20. What's in the market
●
VMturbo
●
Created in 2009
●
Aim: Intelligent Workload Management real-time solution
for Cloud & Virtualized environments
●
Overall strategy:
●
replace manual partitioned management
●
with scalable, automated, and unified resource & performance
management
●
Use of economic techniques for IT resource management
●
Economic Scheduling Engine: Dynamically adjust
resource allocation
20

21. Conclusions
●
Automatic Energy-based scheduling
→ is a recent area
→ should be considered more by researchers
→ should become the target for top10
supercomputers → even better results!
→ Server Virtualization is an efficient way for
reducing energy-costs
21

22. References
1. G. Dasgupta, A. Sharma, A. Verma, A. Neogi, R. Kothari, “Workload Management for
Power Efficiency in Virtualized Data Centers”, Communication of the ACM, 54:7, July
2011.
2. The Green500, retrieved on 9th May 2012, http://www.green500.org.
3. J. Ll. Berral, Í. Goiri, R. Nou, F. Julià, J. Guitart, R. Gavaldà, J. Torres, “Towards
energy-aware scheduling in data centers using machine learning”, In Proceedings of
the 1st International Conference on Energy-Efficient Computing and Networking,
Germany, April 2010.
4. IBM builds water-cooled processor for Zurich supercomputer, retrieved on 10th May
2012, http://www.computerweekly.com/feature/IBM-builds-water-cooled-processor-for-
Zurich-supercomputer.
5. IBM's Water-Cooled Aquasar Supercomputer Uses Waste Heat to Warm Dorms,
retrieved on 10th May 2012, http://www.popsci.com/technology/article/2010-04/ibms-
water-cooled-supercomputer-could-cut-energy-costs.
6. VMturbo: Intelligent Workload Management for Cloud and Virtualized Environments,
retrieved on 10th May 2012, http://www.vmturbo.com/.
7. Operations Management in the Age of Virtualization, A Vmturbo Whitepaper.
22

23. EEDC
34330
Automatic
Execution
Environments for Energy-Aware
Distributed Scheduling
Computing
European Master in Distributed A GREEN Project
Computing – EMDC
Group members:
Maria Stylianou –
marsty5@gmail.com
Georgia Christodoulidou –
geochris71@gmail.com