ElasticTree: saving energy in data center networks

  • Authors:

  • Brandon Heller;Srini Seetharaman;Priya Mahadevan;Yiannis Yiakoumis;Puneet Sharma;Sujata Banerjee;Nick McKeown

  • Affiliations:

  • Stanford University, Palo Alto, CA;Deutsche Telekom R&D Lab, Los Altos, CA;Hewlett-Packard Labs, Palo Alto, CA;Stanford University, Palo Alto, CA;Hewlett-Packard Labs, Palo Alto, CA;Hewlett-Packard Labs, Palo Alto, CA;Stanford University, Palo Alto, CA

  • Venue:
  • NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
  • Year:
  • 2010

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Abstract

Networks are a shared resource connecting critical IT infrastructure, 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 power1 manager, which dynamically adjusts 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 traffic patterns. We implement and analyze ElasticTree on a prototype testbed built with production OpenFlow switches from three network vendors. Further, we examine the trade-offs between energy efficiency, performance and robustness, with real traces from a production 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 centers containing thousands of nodes. We finish by showing how a network admin might configure ElasticTree to satisfy their needs for performance and fault tolerance, while minimizing their network power bill.