A hierarchical framework for energy-efficient resource management in green data centers - Jonardi, Eric, author; Pasricha, Sudeep, advisor; Siegel, H. J., advisor; Howe, Adele, committee member (Colorado State University. Libraries, 2015)
Data centers and high performance computing systems are increasing in both size and number. The massive electricity consumption of these systems results in huge electricity costs, a trend that will become commercially unsustainable as systems grow even larger. Optimizations to improve energy-efficiency and reduce electricity costs can be implemented at multiple system levels, and are explored in this thesis at the server node, data center, and geo-distributed data center levels. Frameworks are proposed for each level to improve energy-efficiency and reduce electricity costs. As the core count in processors continues to rise, applications are increasingly experiencing performance degradation due to co-location interference arising from contention for shared resources. The first part of this thesis proposes a methodology for modeling these co-location interference effects to enable accurate predictions of execution time for co-located applications, reducing or even eliminating the need to over-provision server resources to meet quality of service requirements, and improving overall system efficiency. In the second part of this thesis a thermal-, power-, and machine-heterogeneity-aware resource allocation framework is proposed for a single data center to reduce both total server power and the power required to cool the data center, while maximizing the reward of the executed workload in over-subscribed scenarios. The final part of this thesis explores the optimization of geo-distributed data centers, which are growing in number with the rise of cloud computing. A geographical load balancing framework with time-of-use pricing and integrated renewable power is designed, and it is demonstrated how increasing the detail of system knowledge and considering all system levels simultaneously can significantly improve electricity cost savings for geo-distributed systems.
Keywords: energy efficiency; high-performance computing; resource allocation; heterogeneous computing; co-location; optimization