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Data center design: Architecture and energy consumption
Data Centers have evolved from mainframe systems and enterprise networks into sophisticated networks of 100,000 or more servers for supporting next-generation Computing-as-a-Service (CaaS) and Cloud computing infrastructures. The evolution of data centers has resulted in an expected exponential increase in both data center network traffic and data center energy consumption. Thus, there is tremendous interest in state-of-the-art research in data centers with the objective of designing efficient data center network (DCN) architectures and developing strategies for minimizing energy consumption. In this dissertation, we propose HyScale and SlimNet, cost-effective and scalable DCN architectures using hybrid optical networks. The proposed architectures are fault-tolerant, recursively defined, and have low network complexity. Moreover, the proposed architectures also have several desirable graph-theoretic properties like high bisection-width and low diameter. We propose a non-selfish destination selection paradigm for containing the impact of the changes in the availability of the resources at a destination. We also present efficient non-selfish heuristics for minimizing the resource blocking rate. We adapt energy models for developing comprehensive and state of-the-art energy models for servers and network elements in data centers. Using the server and network energy models, we propose a green routing scheme that minimizes the total combined energy consumption of all servers and all network elements in a data center under dynamic traffic. The proposed green routing scheme uses dynamic voltage scaling, rate adaptation , and anycast transmission for minimizing the total energy consumption in a data center. Finally, we propose thermal-constrained energy-aware algorithms for partitioning tasks in multi-core multiprocessor systems using both worst-case execution time and actual utilization of the tasks.
Saha, Shivashis, "Data center design: Architecture and energy consumption" (2012). ETD collection for University of Nebraska - Lincoln. AAI3518157.