Energy Aware Grid: Global Workload Placement based on Energy Efficiency

1. Energy Aware Grid: Global Workload Placement based on Energy Efficiency Chandrakant Patel, Ratnesh Sharma, Cullen Bash, Sven Graupner HP Laboratories Palo Alto

2. Grid Computing • New paradigm in distributed and pervasive computing for scientific as well as commercial applications. • Based on coordinated resource sharing and problem solving in dynamic, multi institutional virtual organizations.

3. Energy Aware Grid • Objective: • Build an energy aware grid. • Problem: • Thermal and energy management issues due to aggregation of computing, networking and storage hardware. • Solution: • Data center energy efficiency coefficients: Workload placement decisions will be made across the Grid, based on these coefficients. • Provide a global utility infrastructure explicitly incorporating energy efficiency and thermal management among data centers.

4. Cooling Issues • Example : • A data center, with 1000 racks, approximately 25,000 square feet. • Requires 10 MW of power for the computing infrastructure. • An additional 5 MW would be required to remove the dissipated heat. • At $100/MWh, cooling would cost $4 million per annum.

5. Data Center thermo-mechanical architecture • Rows of racks with multiple air-cooled hardware. • Presence of multiple air-conditioning units. • Higher airflow rates required due to “slim servers” and “blade servers”.

6. Problems in Data Center Cooling: • Cooling at Chip Level • 10% of total power • Cooling at Data Center Level • 50% of the total power • Additional thermodynamic work for cooling. • Non-uniform temperature and airflow patterns. • The data center has no well defined boundaries. • No control mechanism to dissipate high heat loads.

7. Contributions of the paper • Propose an energy-aware co-allocator that redistributes computing within the global network of data centers. • Examine the methods for evaluating energy efficiency and thermal management parameters applicable to any data center cooling infrastructure.

8. Globus resource managementarchitecture Ian Foster, Carl Kesselman :The Grid , Blueprint for a New Computing Infrastructure, 1999.

9. RSL (Resource Specification Language) • Application specifies resource needs. • Broker Infrastructure • Resolving higher-ordered RSL specifications into elementary, ground resource specifications. • Ground RSL specification • List of physical resources (machines, storage units, devices) needed to perform a computation. • GRAMs (Globus Resource Allocation Managers) • Allocates RSL ground resources from its resource pool for a scheduled time period. • Assigns them to a specific computation. • GIS • information about resources and their availability.

10. Energy Aware Co-Allocator • Has information about a data center • Resource Types – machines, OS etc. • Capacity of the resources. • Schedules of allocations and reservations. • The energy efficiency coefficientof the data center. • Represents the energy cost when placing a workload in a particular data center. • Data Center selection process • Co-allocator will choose one or more GRAMs • Necessary Conditions - • Functional: Appropriate types of resources available. • Quantitative: Sufficient amounts of resources available. • Schedule: Sufficient amounts of resource instances. • Constraints: Restrictions, if provided by the application.

11. Energy Efficiency Coefficient ( ) • A composite indicator of energy efficiency and thermal management of a data center. • Factors which affect the coefficient • Low condenser temperature. • Relative humidity (RH). • Cooling load. • Using ground as a heat sink. • Local Thermal Management. • The efficiency coefficient of ithdata center is given by χ i=ξ i⋅COPi • ξ is a factor of the Local Thermal Management. • COP is the Coefficient of Performance, based on the condenser temperature.

12. Vapor Condensation Mechanism • Heat extraction system in a data center is based on a variation of reverse power cycle (also known as vapor compression cycle). • Efficiency (η) • Pressure (P)- enthalpy (h) diagram for a vapor compression cycle • Heat addition in the evaporator (C-D) • Work input at the compressor (D-A) • Heat rejection at the condenser (A-B)

13. Coefficient of Performance • COP – Coefficient of Performance • the ratio of desired output (i.e. heat extracted from the data center, Qevap) over the work input (i.e. Wc). • Lower condenser temperature improves coefficient of performance of cooling system. • Heat can only be rejected to the ambient surroundings over a negative temperature gradient. • Workload placement in data centers located in regions with higher ambient temperatures can increase the energy consumption per unit workload.

14. Example • Comparison of temperatures of New Delhi and Phoenix. • Calculate the COP • Delhi – 3.32 • Phoenix – 7.61 • Workload placement in New Delhi will be 56% more energy intensive than that in Phoenix. • Energy-Aware Grid: Workload placement should be carried out based on lowest available heat rejection temperature.

15. Relative Humidity • Cooling of data center supply air also depends on the humidity. • Energy-Aware Grid: Workload placement should avoid the potential disadvantages associated with high ambient humidity conditions. • Regions with low seasonal humidity and ambient temperature can directly utilize outside air to provide cooling.

16. Cooling Load • COP of cooling systems varies with load. • COP can deteriorate by 20%, if the load drops to 50% of rated capacity. • Energy-Aware Grid : Workload placements across data centers should strive to maintain optimum load levels for highest possible coefficient of performance.

17. Using ground as a heat sink • Higher COP at a slightly higher initial cost. • Temperature variation is barely observable below a depth of 1 m. • Heat from the condenser is rejected to the ground • Underground piping with water/glycol. • Energy- Aware Grid: Aware of efficiency of these systems for prospective workload placement during adverse ambient conditions.

18. Local Thermal Management • Prevent local hot-spots by proper arrangement of rack and unit layouts. • Depends on the data center infrastructure. • Propose a data center-level thermal multiplier • Account for the ability of the data center infrastructure to cope with new workload placement. • Tref :air supply temperature to the data center. • SHI: denotes effect of hot air infiltration at the inlet to server or rack. • Higher ξ indicates a greater potential for vulnerability.

19. Energy-Aware Workload Distribution in a Grid • The co-allocator can choose those data center with the highest performance index at the time of the placement. • Need to consider migration costs across long distances, time zones etc. • Calculate a Workload Index • Use the WPI to efficiently allocate workloads.

20. Workload Example • Co-allocator follows a 3 step process • Search for data centers which can match the workload • Determine ξ for those locations. Eliminate centers (ξ < 4) • Use WPI to determine the final placement. • Calculations for Phoenix compared to New Delhi • Reduction in cooling resource power consumption of 56%. • Reduction in total energy consumption of 13%.

21. Conclusion • Energy-Aware policy for distributing computational workload in the Grid resource management architecture. • Data center energy coefficient.