1. A Dynamic Workload Management Model for Saving Electricity Costs in Cloud Data Centers
Narander Kumar and Shalini Agarwal
Dept. of Computer Science, BBAU, Lucknow
ICACCI-2014

2. Presentation Outline Introduction Motivation The CRAC unit
Challenges for Service Providers
Proposed Model and its formulation
Proposed Algorithms
Experiment Results
References

3. What is Cloud Computing?
An “everything-as-a-service” model of computing.
A variety of resources are provided over the Internet as ‘Services’ by Cloud Service Provider (CSP).
Advantages : On-demand provisioning, Pay-per-use pricing, Scalability, Resource Utilization.

4. Motivation
Geographically distributed data centers of cloud provider incur heavy electricity costs due to high prices as well as inefficient workload management among the data centers.
To bring down the operational costs dynamic power management is used as the basic approach where different power modes exist for a server.
However the air flow pattern of the cooling systems is not taken into consideration in the existing works.
This paper investigated that the power consumption and hence the electricity costs of the active servers in the data center is influenced by server utilization as well as output temperature of the cooling unit and proposed two algorithms, Electricity Cost Saving Workload Management Algorithm(ECSWMA) and Electricity Price Aware Workload Management Algorithm (EPAWMA) that jointly manage the workload of all the data centers run by a cloud provider.

5. Layout of CRAC fitted Data Center

6. Challenges for Service Provider
The annual cooling costs incurred in deploying such CRAC units to cool a 30,000 ft square data center with 1000 racks, each consuming 10kW, at an average electricity cost of $100MWhr, is 4 to 8 million dollars.
Under the same configuration, a 5 degree centigrade cooler room temperature lowers CRAC power consumption up to 40 %.
Therefore in hot zones (top shelves and side racks) more cooling power is required than in the cool zones (bottom selves and inside of the racks).

7. The COP Curve
In order to quantitatively measure the efficiency of the cooling system, the metric, Coefficient of Performance (COP) is used.
COP is defined as the ratio of the heat removed (HR) to the amount of work done (WD) to remove that heat.
 As the temperature of the air that is pushed by the CRAC unit increases the COP also increases.
Higher COP indicates more efficient process. Therefore considerable costs can be saved by raising the temperature of the air pushed into the data center without crossing the Safety Temperature

8. Data Center Utilization vs. Cooling Costs
Maximizing server utilization results in higher heat generation which in turn produces higher cooling costs.
The opportunities lying between the best and worst workload placement curves can be utilized to design energy consumption aware workload scheduling policy.

9. Workload Management Model

10. Main Parameters Electricity Cost (EC) incurred by the active servers
Electricity cost of cooling system (ECC)
Accumulated Electricity Cost (AEC)
Scmax is the maximum number of servers in the cool zone
Swmax is the maximum number of servers in the warm zone
Shmax is maximum number of servers in the hot zone
Ti is the maximum safety temperature of the ith zone where i=c, w, h
Tserver is the output temperature of a server
Load Percentage of the ith zone = Si/Simax, where i=c,w,h
Maximum Load Percentage of ith zone Li, where i= c, w, h

11. Formulation of the problem
Minimize Accumulated Electricity Cost (AEC) of the active servers in the data center
Subject to-
Load Percentage of cool zone<= Lc
Load Percentage of cool zone<= Lw
Load Percentage of cool zone<= Lh
Temperature of cool zone<= Tc
Temperature of warm zone<= Tw
Temperature of hot zone<= Th
Σ Tserver(cool zone)<= Tc
Σ Tserver(warm zone)<= Tw
Σ Tserver(hot zone)<= Th
Sc<= Scmax
Sw<= Swmax
Sh<= Shmax

12. Electricity Cost Saving Workload Management Algorithm (ECSWMA)

13. Electricity Price Aware Workload Management Algorithm (EPAWMA)

14. AEC of the data centers (ECSWMA-EPAWMA)
Experiment Results
AEC of the data centers (ECSWMA-EPAWMA)
AEC of the data centers
(Existing Approach)
Conclusion: With the same rise in the input workload of a data center, the rise in the electricity cost is considerably lower when ECSWMA-EPAWMA approach is implemented for workload distribution.

15. References
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16. Any Questions?

17. Thank You