Global strategy and tools to use multiple information sources and to coordinate decisions at different levels in the data center aimed at a common objective: reducing the total energy consumption.

Innovative components of the GreenCPD architecture include:

• Automatic energy characterization and classification of the workload.
• Optimal resource selection taking into account cooling resources, computing resources and the applications.
• Leveraging the heterogeneity of the data center to reduce energy consumption.
• Automatic application-aware management of resources, low-power modes, just-in-time compilation, etc.
• Enhanced reliability by avoiding failures due to high temperatures or thermal stress.

“By taking advantage of the heterogeneity of the resources in the data center, the IT energy consumption can be reduced up to 30%.” 

• By 2000 data centers used 0.53% of the world’s total electricity consumption. By 2010 data centers consumed approximately 235.5 BkWh, representing 1.3% of worldwide electricity use.
• Due to the rapid growth of the data centers capacity, the power densities handled by their infrastructure and energy consumption have grown dramatically. The electricity bill, including computation and cooling costs, is over $7 billion only in the US and the power density exceeds 60 kW/m2.
• According to Gartner, 50% of current data centers already have a lack of power and cooling capacity to meet the needs of high-density equipment. A survey of 369 IT professionals performed by OnStor reported that 63% of survey respondents have run out of power or cooling without warning.
• Current data centers need:
• to take into account many different aspects at the same time in order to minimize the total energy consumption, as cooling is not dominant any more;
• to significantly decrease their energy consumption and their peak power needs to avoid power outages, specially in urban data centers during the summer;
• to apply reactive and proactive techniques to adapt the energy consumption to internal and external changes of requirements (peak load or outage avoidance systems of smart grids).

“Worldwide data centers produce 2% of global CO2 emissions” 

• Pike Research forecasts that the green data center will offer an overall market opportunity that exceeds $40 billion worldwide by 2015. While North America and Europe will lead the way over the next two years, the Asian market will catch up quickly due to the continued rapid expansion of its data center capacity and eventually a growing commitment to the principals of the green data center.
• Microsoft predicts that capital expenditures in new data centers will likely flatten over the next few years as a result of innovations in new designs that bring down cost. However, once these innovations become prolific and further improvement opportunities diminish, the growth will continue at historic rates.
• In 2000, the annual construction market size globally was three times that in the US. By 2020, it will likely be four times as large. This should be no surprise that the growth will be higher globally, than in the US. By 2020, we can expect the construction market size for datacenters to be about $18 billion in the US and $78 billion globally.

“US data centers consume 1.5 times the electricity consumed by New York City” 

• Holistic approach. We optimize energy consumption by taking into account the thermal state of the datacenter, workload energy requirements, thermal and energy characteristics of the cooling and computing resources, and recent execution history.
• Proactive strategies besides reactive ones.  We use knowledge about workload and resources to anticipate the energy requirements and to plan in advance.
• Application-aware. We have proven that, by taking advantage of heterogeneity in IT resources, resource allocation algorithms can be improved to decrease the total IT energy consumption by 30%.
• Non-intrusive. Our approach does not require radical changes in the infrastructure nor the management software. Data gathering, data analysis, and automatic control modules can be added gradually with no impact in performance or service discontinuation.
• Adaptable. The flexible architecture of our dynamic optimization system can be easily customized to specific needs. The decision support system is continuously learning what works and what doesn’t.