CSR: Small: Understanding and Modeling the Trade-Offs in Data Centers for Next-Generation Sustainable Management

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The success of cloud computing as a business model has led to several commercial cloud services for storage, data mining and searching, and multimedia content delivery. As cloud services penetrate emerging markets -- notably the smart-phone market -- conventional utility services such as television are becoming ?cloudized.? As the economy becomes more dependent on the efficient operation of cloud data centers (DCs), there?s an increasingly urgent need to design ?green? and energy-adaptive management software that optimizes the operation of a geographically distributed set of data centers under varying workload and energy conditions.Cyber-physical, proactive and predictive decision making is essential for a DC operating system that efficiently predicts, e.g., the workload and the power consumption when running multiple jobs on the same server and makes decisions, e.g., for redistributing the servicing virtual machines among varying number of available DCs, electricity cost, percentage of renewable energy supply and capacity of energy storage. This project is addressing the scientific and engineering challenges involved in decision making of such DC management arising from an array of complicating factors -- e.g., server heterogeneity, cyber-physical interactions between DCs and their environment, varying workload and electricity pricing -- and trade-offs -- e.g., energy-delay, time-accuracy, and cooling-computing . The project is developing a Green cyber-physical Data Center Simulator, GDCSim, for studying the tradeoffs in practical scenarios with several complicating factors. Project aims to enable a more sustainable cloud computing and to train engineers for a greener economy. The project findings are available through ASU-IMPACT lab?s website (http://impact.asu.edu/).