1. Green cloud computing 2
Cs 595
Lecture 15

2. The Case for Energy-Proportional Computing
Barroso and Holzle (Google)

3. Introduction
Energy proportional computing primary design goal for servers
Cooling and provisioning proportional to average energy servers consume
Energy efficiency benefits all components
Computer energy consumption lowered if:
Adopt high-efficiency power supplies
Use power saving features already in equipment

4. Introduction More efficient CPUs based on multiprocessing has helped
But, generally, higher performance means increased energy usage

5. Servers Datacenter server efficiency and utilization:
Rarely completely idle
Seldom operate at maximum
10-50% of max utilization levels
100% utilization not acceptable for meeting throughput, etc. – no slack time

7. Servers Completely idle server waste of capital
Servers need to be available
Perform background tasks
Move data around
Help with crash recovery
Applications can be restructured to create idle intervals
Difficult, hard to maintain
Devices with highest energy savings, highest wake-up penalty, Ex. disk spin up, CPU throttle based on workload

8. Energy Efficiency at varying utilization levels
Utilization – measure of performance normalized to performance at peak loads
Energy efficient server still consumes ½ power when doing almost no work
Power efficiency – server utilization/power consumed
Peak energy efficiency occurs at peak utilization and drops as util. decreases
At 20-30% utilization, efficiency drops to less than ½ at peak performance

10. Toward energy-proportional machines
Mismatch between servers’ high-energy efficiency characteristics and behavior
Designers (hardware) need to address this
Design machines that consume energy in proportion to amount of work performed
No power when idle (easy)
Nearly no power when little work (harder)
More as activity increases (even harder)

11. CPU power
Small amount of total server power consumed by CPU has changed since 2005
CPU no longer dominates power at peak usage, trend will continue
Even less when idle
Processors close to energy-proportional
Consume < 1/3 power at low activity
Power range less for other components
< 50% for DRAM, 25% for disk drives, 15% for network switches

12. Dynamic Power range
Processors can run at lower voltage frequency mode without impacting performance
No other components with such modes
Only inactive modes in DRAM and disks
Inactive to active mode transition penalty (even it only idle to submilliseconds)
Servers with 90% dynamic range could cut energy by ½ in data centers
Lower peak power by 30%
Energy proportional hardware reduce the need for power management software

14. Disks - Inactive/active
Penalty for transition to active from inactive state makes it less useful
Disk penalty 1000x higher for spin up than regular access latency
Only useful if idle for several minutes (rarely occurs)
More beneficial to have smaller penalty even if higher energy levels
Active energy savings schemes are useful even if higher penalty to transition because in low energy mode for longer periods

15. Conclusions
CPUS already exhibit energy proportional profiles, other components less so
Need significant improvements in memory and disk subsystems
Such systems responsible for larger fraction of energy usage
Need energy efficient benchmark developers to report measurements at nonpeak levels for complete picture

16. Green Introspection by K. Cameron

17. History of Green Energy
In the 1970s
Energy crisis
High gas prices
Fuel shortages
Pollution
Education and action
Environmental activism
Energy awareness and conservation
Technological innovation

18. Gifts from the 70s Energy crisis subsided
In the meantime advances in computing responsible for:
Innovation for energy-efficient buildings and cars
Identified causes and effects of global climate change
Grassroots activism, distributing info about energy consumption, carbon emission, etc.

19. What happened next
Call to action within IT community (what about the 80s??)
In 1990s
General-purpose microprocessors built for performance
Competing processors
ever-increasing clock rates and transistor densities
fast processing power and exponentially increasing power consumption
Power wall at 130 watts
Power is a design constraint

20. Better, but also worse? To reduce power consumption But
Multicore architectures – higher performance, lower power budgets
But
Users expect performance doubling every 2 years
Developers must harness parallelism of multicore architectures
Power problems ubiquitous – energy-aware design needed at all levels

21. More problems
Memory architectures consume significant amounts of power
Need energy-aware design at systems level
Disks, boards, fans switches, peripherals
Maintain quality of computing devices, decrease environmental footprint

22. Trade-offs How often to replace aging systems?
2% of solid waste comes from consumer electronic components
E-waste fastest growing component of waste stream
In US 130k computers thrown away daily and 100m cell phones annually
Recycle e-waste (good luck)
Use computers as long as possible?