1. Green Clouds – Power Consumption as a First Order Criterion Karsten Schwan, Sudhakar Yalamanchili, Ada Gavrilovska, Hrishikesh Amur, Bhavani Krishnan, Surabhi Diwan, Nikhil Sathe, Minki Lee, Saibal Mukopadhyay, … CERCS Yogendra Joshi, Pramod Kumar, Emad Samadiani CEETHERM Georgia Institute of Technology

2. Green Computing Initiative An eco-system of projects addressing multiple stack layers Multiple faculty and students involved ECE, ME, CS Circuit level Circuit level: DVFS, power states, clock gating (ECE) Chip and Package Chip and Package: power multiplexing, spatiotemporal migration (SCS, ECE) Board Board: VirtualPower, scheduling/scaling/operating system… (SCS, ME, ECE) Rack Rack: mechanical design, thermal and airflow analysis, VPTokens, OS and management (ME, SCS) Power distribution and delivery (ECE) http://img.all2all.net/main.php?g2_itemId=157 Datacenter and beyond Datacenter and beyond: design, IT management, HVAC control… (ME, SCS, OIT…)

3. 3 Modeling and Control Across Entire Stack Seek a fundamental understanding of relationships between performance, power distribution, energy consumption, heat generation and cooling technologies at all levels of the stack Develop, model, and assess (new) principles for energy and thermal management – Coordinated management across the entire stack –Example concepts Couple cooling and workload generation: thermal flow control to respond to load conditions Couple power distribution and workload generation: adapt to power capacity (time of day?) Pro-active spatio-temporal migration driven by physics of heat generation/flow rather than reactive sensor driven techniques

4. Sample Projects Understanding of power distribution opportunities at the chip level Platform-level coordination of power management methods –DVFS, scheduling, idle states –Understanding of impact of these approaches Distributed power management methods IT & environmental factor management –Temperature and air velocity, HVAC control Management architecture for virutalized platforms

5. 5 Thermal and Power Scaling Limits-On-Chip Power Limited Performance Power Limited Performance Temperature Limited Performance Temperature Limited Performance Mukhopadhyay and Yalamanchili (2008)

6. 75° 70° 45° 30 ms 75° 70° 45° 30 ms 64 on-tiles 256 total tiles 100K time slice interval@3GHz Unmanaged Thermal Behavior Managed Thermal Behavior (multiplexed power) Courtesy: Nikil Sathe Spatial gradient Temporal gradient Spatial gradient: 10.5°@0.75mm Temporal gradient: 2.5°@100Kcyles Spatial gradient Temporal gradient Spatial gradient: 2.5°@0.75mm Temporal gradient: 1.99°@100Kcyles

7. 7 The Need for Feedback Power distribution network Spatiotemporal migration Thermal profile Co-design power distribution/architecture management@chip and multi-chip Co-exploration of thermal management/architecture management Local Memory Cache core Local Memory Cache core Local Memory Cache core Local Memory Cache core

8. Towards Integrated Platform Power Management Multiple techniques exist for power management on platforms.

9. VirtualPower: Coordinated Power Management Coordinated power management (DVFS) + load management (migration) + CPU management (credit based soft scaling) -> cumulative reduction of 34% in power resources without SLA degradation of RUBis benchmark (R. Nathuji, K. Schwan, SOSP0) Hardware Hypervisor OS Application VPM States PM Policy OS Application VPM States PM Policy Dom0 VPM Channel VPM Rules VPM Mechanisms

10. Platform-level Power Management Methods: Costs and Opportunities Goals –identify and quantify the reasons for performance degradation associated with each method for power management –To be able to estimate the power savings from each methods Use this information to support better runtime management decisions Current focus: DVFS –Bounds on degradation based on system profiling and runtime performance couter information Other methods next, including idle states

11. Virtualization Support for Power Budgeting Power VM1 VM2 VM3 VM4 VM5 Group level power budget distributed amongst underlying platforms based on system utilization, static priority, etc Platform power cap directly affects VM performance impact Goal: Develop system support for improving aggregate QoS/performance of VMs in distributed power budgeted environments

12. VPMTokens Benefits Without VPM channel feedback high rate transaction application experiences QoS impact VPM input allows system to dynamically move budget from low rate to high rate VM, reducing overall performance impact within budget constraints Experiment: High rate and low rate transaction VMs running on P4 platform Both VMs have same utility value, therefore equal allocation without VPM channel input (R. Nathuji, K. Schwan, HPDC08)

13. CoolIT: Coordinated IT and environmental/thermal management CRACCRAC R5R6R7R8 R4R3R2R1 Cold Aisle Energy tradeoff Increased airflow rates provide improved ability to dissipate server heat loads Airflow required to meet maximum inlet temperature varies based upon server loads and physical distribution of heat Use of power budgeting in IT Efficient operating point for cooling infrastructure implies load constraints Power capping capabilities enable dynamic compliance when DPM alone cannot meet constraints R5R6R7R8 R4R3R2R1 Cold Aisle

14. CoolIT Approach Objectives: –VM load distribution strategy: Inlet temperature in the data center should be below 32C (or target temperature) Server power consumption and cooling power is limited –Heat load within limits based on cooling capacity –Hot-spot avoidance in the face of load and platform heterogeneity Model output: –target utilization for different platforms based on inlet temperature, air velocity, average utilization Offline profiles: –power vs CPU utilization for different architectures –other resources next Dynamic load management: –Currently first fit bin-packing algorithm –Driven by management server Input from mgt domains and thermocouples sensors –OSIsoft PI server dom0 -> PI server SNMP-based infrastructure can also drive VM migration

15. Management Architecture Management brokers –make and enforce ‘localized’ management decisions within VMs VMM-level – CPU scheduling, allocation of memory or device resources,.. at hardware level Management channels –enable inter-broker coordination through well-defined interfaces –event and shared memory based Management VMs –platform wide policies and cross-platform coordination

16. CERCS Distributed Cloud Infrastructure