Green computing

Abstract

Digital information management is the key enabler for unprecedented rise in productivity and efficiency gains experienced by the world economies during the 21st century. Information processing systems have thus become essential to the functioning of business, service, academic, and governmental institutions. As institutions increase their offerings of digital information services, the demand for computation and storage capability also increases. Examples include online banking, e-filing of taxes, music and video downloads, online shipment tracking, real-time inventory/supply-chain management, electronic medical recording, insurance database management, surveillance and disaster recovery. It is estimated that, in some industries, the number of records that must be retained is growing at a CAGR of 50 percent or greater. This exponential increase in the digital intensity of human existence is driven by many factors, including ease of use and availability of a rich set of information technology (IT) devices and services. Indeed, it would be difficult to imagine how significant societal transformations that better our world could occur without the productivity and innovation enabled by the IT. Unfortunately, the energy cost and carbon footprint of the IT devices and services has become exorbitant. Moreover, current technological and digital service utilization trends result in a doubling of the energy cost of the IT infrastructure and its carbon footprint in less than five years. In an energy-constrained world, this consumption trend is unsustainable and comes at increasingly unacceptable societal and environmental costs. This presentation will first explain what is meant by green computing and how greenness of information processing may be quantified. Next, energy-efficient computing paradigms which utilize chip multi-processing, multiple-voltage domains, dynamic voltage/frequency scaling, and power/clock gating techniques will be reviewed. Finally, techniques for improving performance per Watt of large-scale information processing and storage systems (e.g., a data center), including hierarchical dynamic power management, task placement and scheduling, energy balancing, resource virtualization, and application optimizations that dynamically configure hardware for higher efficiency will be discussed.