Finding $k$ Cuts within Twice the Optimal
SIAM Journal on Computing
IEEE/ACM Transactions on Networking (TON)
MapReduce: simplified data processing on large clusters
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A scalable, commodity data center network architecture
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Dcell: a scalable and fault-tolerant network structure for data centers
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PortLand: a scalable fault-tolerant layer 2 data center network fabric
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Proceedings of the ACM SIGCOMM 2009 conference on Data communication
BCube: a high performance, server-centric network architecture for modular data centers
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Energy proportional datacenter networks
Proceedings of the 37th annual international symposium on Computer architecture
Routing for energy minimization in the speed scaling model
INFOCOM'10 Proceedings of the 29th conference on Information communications
Energy-aware routing in data center network
Proceedings of the first ACM SIGCOMM workshop on Green networking
ElasticTree: saving energy in data center networks
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Hedera: dynamic flow scheduling for data center networks
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Improving datacenter performance and robustness with multipath TCP
Proceedings of the ACM SIGCOMM 2011 conference
PCube: Improving Power Efficiency in Data Center Networks
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Identifying and using energy-critical paths
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
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NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
The only constant is change: incorporating time-varying network reservations in data centers
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
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The popularization of cloud computing brings emergency concern to the energy consumption in big data centers. Besides the servers, the energy consumed by the network in a data center is also considerable. Existing works for improving the network energy efficiency are mainly focused on traffic engineering, i.e., consolidating flows and switching off unnecessary devices, which fails to comprehensively consider the unique features in data centers. In this paper, we advocate a joint optimization for achieving energy efficiency of data center networks by proposing a unified optimization framework. In this framework, we consider to take advantage of the application characteristics and topology features, and to integrate virtual machine assignment and traffic engineering. Under this framework, we then devise two efficient algorithms, TE VMA and TER, for assigning virtual machines and routing traffic flows respectively. Knowing the ncommunication patterns of the applications, the TE VMA algorithm is purposeful and can generate desirable traffic conditions for the next-step routing optimization. The TER algorithm makes full use of the hierarchical feature of the topology and is conducted on the multipath routing protocol. The performance of the overall framework is confirmed by both theoretical analysis and simulation results, where up to 50% total energy savings can be achieved, 20% more compared with traffic engineering only approaches.