Abstract-Data centers consume significant amounts of energy. As severs become more energy efficient with various energy saving techniques, the data center network (DCN) has been accounting for 20% or more of the energy consumed by the entire data center. While DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns. The objective of this work is to improve the energy efficiency of DCNs during off-peak traffic time by powering off idle devices. Although there exist a number of energy optimization solutions for DCNs, they consider only either the hosts or network, but not both. In this paper, we propose a joint optimization scheme that simultaneously optimizes virtual machine (VM) placement and network flow routing to maximize energy savings, and we also build an OpenFlow based prototype to experimentally demonstrate the effectiveness of our design. First, we formulate the joint optimization problem as an integer linear program, but it is not a practical solution due to high complexity. To practically and effectively combine host and network based optimization, we present a unified representation method that converts the VM placement problem to a routing problem. In addition, to accelerate processing the large number of servers and an even larger number of VMs, we describe a parallelization approach that divides the DCN into clusters for parallel processing. Further, to quickly find efficient paths for flows, we propose a fast topology oriented multipath routing algorithm that uses depth-first search to quickly traverse between hierarchical switch layers and uses the best-fit criterion to maximize flow consolidation. Finally, we have conducted extensive simulations and experiments to compare our design with existing ones. The simulation and experiment results fully demonstrate that our design outperforms existing host-or network-only optimization solutions, and well approximates the ideal linear program.
Recently, energy efficiency or green IT has become a hot issue for many IT infrastructures as they attempt to utilize energyefficient strategies in their enterprise IT systems in order to minimize operational costs. Networking devices are shared resources connecting important IT infrastructures, especially in a data center network they are always operated 24/7 which consume a huge amount of energy, and it has been obviously shown that this energy consumption is largely independent of the traffic through the devices. As a result, power consumption in networking devices is becoming more and more a critical problem, which is of interest for both research community and general public. Multicast benefits group communications in saving link bandwidth and improving application throughput, both of which are important for green data center. In this paper, we study the deployment strategy of multicast switches in hybrid mode in energy-aware data center network: a case of famous fat-tree topology. The objective is to find the best location to deploy multicast switch not only to achieve optimal bandwidth utilization but also to minimize power consumption. We show that it is possible to easily achieve nearly 50% of energy consumption after applying our proposed algorithm.
A group of switch schedulers make packet scheduling decisions based on predefined bandwidth allocation for each flow. Allocating bandwidth for best effort flows is challenging due to lack of allocation criteria and fairness principles. In this paper, we propose sequential and parallel algorithms to allocate bandwidth for best effort flows in a switch, to achieve fairness and efficiency. The proposed algorithms use the queue length proportional allocation criterion, which allocates bandwidth to a best effort flow proportional to its queue length, giving more bandwidth to congested flows. In addition, the algorithms adopt the max-min fairness principle, which maximizes bandwidth utilization and maintains fairness among flows. We first formulate the problem based on the allocation criterion and fairness principle. Then, we present a sequential algorithm and prove that it achieves max-min fairness. To accelerate the allocation process, we propose a parallel version of the algorithm, which allows different input ports and output ports to conduct calculation in parallel, resulting in fast convergence. Finally, we present simulation data to demonstrate that the parallel algorithm is effective in reducing the convergence iterations.Index Terms-bandwidth allocation; max-min fairness; parallel processing
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