An Energy-aware Routing for Optimizing Control and Data Traffic in SDN

Wu, Zhanwei; Ji, Xilin; Wang, Ying; Chen, Xingyu; Cai, Yibin

doi:10.1109/icseng.2018.8638019

Cited by 6 publications

(8 citation statements)

References 8 publications

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“…Table 3 gives the optimal data path for the eight flows obtained using the DQN-EER algorithm, along with the activated switches and links (which include the links of control paths). The number of all activated switches is 17, including switches (0, 1,2,4,6,7,8,9,11,12,13,14,15,16,17,18,19), and the number of all active links in green is 18, including links (0, 1,3,5,8,9,12,14,15,16,18,20,21,24,25,26,30,31), except the two links which are used to directly connect the switches and controllers and the two links between the controllers with the overall controller 0. And the link 21 is the added one when calculating the control path.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Second, in order to evaluate the energy-saving effect of using the DQN-EER algorithm, we select the CPLEX solver and the control-path-based energy-aware routing algorithm (CERA) 17 greedy algorithm as the comparison algorithms. Since CERA does not consider the problem of load balance between controllers, we will use the DQN algorithm to compare energy savings without considering the load balance problem.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…Shang et al formulated the network in the in-band control mode and proposed a heretical method for the energy efficiency. Wu 17 proposed a heuristic energy-efficiency routing algorithm based on dynamic weight of nodes, in which data plane control paths and data traffic routing were coordinated. The energy-saving working path was obtained by iteratively updating the dynamic weights, thereby reducing the energy consumption of the network.…”

Section: Related Workmentioning

confidence: 99%

“…The control path can be divided into two modes: out-of-band 14 and in-band control modes. [15][16][17] The out-of-band control mode has a special control link, which does not require the special calculation of the control path. In the in-band control mode, the control information and the data information can share the links, so the control path needs to be calculated.…”

Section: Introductionmentioning

confidence: 99%

“…The existing literature separately calculates the data path and control path. Wu 17 proposes an energy-efficient routing strategy, which uses a heuristic algorithm to coordinative calculate the paths for the switches and data traffic. However, the proposed dual optimal problem has a very large solution space, and heuristic algorithms are close to traversal and they will need a certain time.…”

Section: Introductionmentioning

confidence: 99%

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DQN-based energy-efficient routing algorithm in software-defined data centers

Yao

Wang

Qiu

2020

International Journal of Distributed Sensor Networks

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With the rapid development of data centers in smart cities, how to reduce energy consumption and how to raise economic benefits and network performance are becoming an important research subject. In particular, data center networks do not always run at full load, which leads to significant energy consumption. In this article, we focus on the energy-efficient routing problem in software-defined network–based data center networks. For the scenario of in-band control mode of software-defined data centers, we formulate the dual optimal objective of energy-saving and the load balancing between controllers. In order to cope with a large solution space, we design the deep Q-network-based energy-efficient routing algorithm to find the energy-efficient data paths for traffic flow and control paths for switches. The simulation result reveals that the deep Q-network-based energy-efficient routing algorithm only trains part of the states and gets a good energy-saving effect and load balancing in control plane. Compared with the solver and the CERA heuristic algorithm, energy-saving effect of the deep Q-network-based energy-efficient routing algorithm is almost the same as the heuristic algorithm; however, its calculation time is reduced a lot, especially in a large number of flow scenarios; and it is more flexible to design and resolve the multi-objective optimization problem.

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