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Unformatted text preview: ElasticTree: Saving Energy in Data Center Networks Brandon Heller ⋆ , Srini Seetharaman † , Priya Mahadevan ⋄ , Yiannis Yiakoumis ⋆ , Puneet Sharma ⋄ , Sujata Banerjee ⋄ , Nick McKeown ⋆ ⋆ Stanford University, Palo Alto, CA USA † Deutsche Telekom R&D Lab, Los Altos, CA USA ⋄ Hewlett-Packard Labs, Palo Alto, CA USA ABSTRACT Networks are a shared resource connecting critical IT in- frastructure, and the general practice is to always leave them on. Yet, meaningful energy savings can result from improving a network’s ability to scale up and down, as traffic demands ebb and flow. We present ElasticTree , a network-wide power 1 manager, which dynamically ad- justs the set of active network elements — links and switches — to satisfy changing data center traffic loads. We first compare multiple strategies for finding minimum-power network subsets across a range of traf- fic patterns. We implement and analyze ElasticTree on a prototype testbed built with production OpenFlow switches from three network vendors. Further, we ex- amine the trade-offs between energy efficiency, perfor- mance and robustness, with real traces from a produc- tion e-commerce website. Our results demonstrate that for data center workloads, ElasticTree can save up to 50% of network energy, while maintaining the ability to handle traffic surges. Our fast heuristic for computing network subsets enables ElasticTree to scale to data cen- ters containing thousands of nodes. We finish by show- ing how a network admin might configure ElasticTree to satisfy their needs for performance and fault tolerance, while minimizing their network power bill. 1. INTRODUCTION Data centers aim to provide reliable and scalable computing infrastructure for massive Internet ser- vices. To achieve these properties, they consume huge amounts of energy, and the resulting opera- tional costs have spurred interest in improving their efficiency. Most efforts have focused on servers and cooling, which account for about 70% of a data cen- ter’s total power budget. Improvements include bet- ter components (low-power CPUs , more effi- cient power supplies and water-cooling) as well as better software (tickless kernel, virtualization, and smart cooling ). With energy management schemes for the largest power consumers well in place, we turn to a part of the data center that consumes 10-20% of its total 1 We use power and energy interchangeably in this paper. power: the network . The total power consumed by networking elements in data centers in 2006 in the U.S. alone was 3 billion kWh and rising ; our goal is to significantly reduce this rapidly growing energy cost. 1.1 Data Center Networks As services scale beyond ten thousand servers, inflexibility and insufficient bisection bandwidth have prompted researchers to explore alternatives to the traditional 2N tree topology (shown in Fig- ure 1(a))  with designs such as VL2 , Port- Land , DCell , and BCube . The re-...
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This note was uploaded on 12/08/2011 for the course CS 525 taught by Professor Gupta during the Spring '08 term at University of Illinois, Urbana Champaign.
- Spring '08