Chandrakant Patel, Ratnesh Sharma, Cullen Bash, Sven Graupner

HP Laboratories Palo Alto

Energy Aware Grid: Global Workload Placement based on Energy Efficiency

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.

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.

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.

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”.

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.

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.

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

• 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.

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 coefficient of 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.

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 ith data 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.

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)

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.

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.

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.

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.

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.

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.

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.

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%.

Conclusion

• Energy-Aware policy for distributing computational workload in the Grid resource management architecture.

• Data center energy coefficient.