1. Supply and Demand Coordination in Energy Adaptive Computing (invited talk) Dr. Krishna Kant Intel/GMU M. Murugan, U/Minn 1

2. Outline Motivate energy adaptive computing Operation under Energy Constraints Hierarchical adaptation to energy constraints. Results and ongoing work

3. Motivation ICT Energy Issues – Soaring energy & cooling costs in Data Centers – Power/thermal issues hindering Moore’s Law – Sustainability concerns leading to use of renewable energy, chiller-less cooling, smaller capacities, etc. Consequences – Variable energy supply & smaller safety margins – Requires smarter control to cope with temporary energy deficiencies. 3

4. IT systems fed by Renewable Energy Limit or eliminate energy draw from grid – Less infrastructure & losses, but variable supply – Need to consider impact on both computing & communications Similar issues wrt unreliable grid supply 4 Need better power adaptability

5. High Temperature Operation Chiller-less data centers – Less energy/materials, but space inefficient High temperature operation of comm/computing equipment – Smaller T outlet – T inlet – Deal w/ occasionally hitting temp. limits. 5 X Need smarter thermal adaptability

6. Frugal Designs Overdesign is the norm today – Huge power supplies, fans, heat sinks, server cases, high rack capacity, UPS capacity, … – Engineered for worst case  Rarely encountered – Huge power wastage, waste of materials, energy, … 6 Better energy adaptability to deal w/ frugal design What if we right-size everything? Highly energy efficient but need smarter control

7. Energy Adaptive Computing EAC strives to do dynamic end to end adjustment to – Workload adaptation for graceful QoS degradation under energy limitations – Infrastructure adaptation to cope with temporary energy deficiencies. Requires coordinated power/thermal mgmt of computation, network & storage. Enhances sustainability of IT infrastructure 7

8. EAC Instances 8

9. Adaptation Methods Workload Adaptation – Coarse grain: Shut down low priority tasks – Fine grain: Graceful QoS degradation, e.g., Batched service, poorer resolution, … Infrastructure Adaptation – Operation at lower speeds (DVFS) – Effective use of low power modes & “width” control. Workload adaptation always done first 9

10. Infrastructure Adaptation Need a multilevel scheme – – Individual “assets” up to entire data center Need both supply & demand side adaptations

11. Supply Side Adaptation Supply side Limits – Hard caps at higher levels (true limit) vs. “soft” (artificial) caps at lower levels. – Limits may be a result of thermal/cooling issues. Load consolidation – An essential part of energy efficient operation – Load consolidation vs. soft capping Need to address workload adaptation changes as a result of supply increase & decrease.

12. Demand Side Adaptation Adaptation to fluctuating demand – Transactional workload: Migrate queries or app VMs? Issues w/ combined supply & demand side adaptations – Imbalance: One node squeezed while other has surplus power – Ping-pong Control: Oscillatory migration of workload – Error accumulation down the hierarchy.

13. A Proposed Algorithm Unidirectional control – Load migration moves up the hierarchy, from local to global. – Local migrations are temporary & do not trigger changes to “soft” caps on supply. Target Node selection – Based on bin packing (best-fit decreasing) – Allows for more imbalance, which can be exploited for workload consolidation Properties – Avoids ping-pong, attempts to minimize imbalance

14. Experimental Results Scenario – 3 levels, 18 identical servers (4+4 + 5+5) – 3 applications, total of 25 app instances – Any app can run on any server – Demand Poisson (active power ∞ utilization)

15. Migration Frequency Migration drivers: consolidation vs. energy deficiency – Low util  Consolidation, High util  Energy deficiency Other characteristics – Migration frequency low in all cases – No ping-pong observed

16. Thermal Impacts Additional Issues – Energy consumption limited by thermal/cooling issues, not energy availability – Migrations required to limit temperature Temperature & power have nonlinear relationship Need to account for both power & thermal effects

17. Results w/ Thermal Effects Imbalanced cooling – Servers 1-14: T a =25 o C, Servers 15-18: T a =40 o C – Temperature limit: 65 o C Power demand is adjusted by the alg. to account for higher temperature

18. Challenges EAC is about end-to-end control – Network & storage energy also needs to be addressed Network adaptation – More than power mgmt of ports. Need consolidation of traffic across ports – Need to deal w/ congestion created due to adaptation. Storage adaptation – More than just storage device control, need to consider storage network as well. Putting it all together is hard! Need effective means of multi-level admission control. Ultimate vision: Integrate client side as well

19. Conclusions Need to go beyond energy efficiency – Design devices/systems to minimize life-cycle energy footprint – Creatively adapt to available energy to operate “at the edge” Ongoing/future work – Coordinated server, network & storage mgmt. – Generalized workload adaptation (rule based?) – Explore tradeoffs between QoS, power savings and admission control performance 19

20. Thank you!