期刊
IEEE TRANSACTIONS ON CLOUD COMPUTING
卷 10, 期 1, 页码 396-410出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCC.2019.2947900
关键词
Device activation; rule installation; data transmission; energy minimization management; software defined data center networks
类别
资金
- National Key R&D Program of China [2018YFB0803400]
- Fundamental Research Funds for the Central Universities [2019CDYGZD004]
- National Natural Science Foundation of China [61772432, 61772433]
- Technological Innovation and Application Demonstration Projects of Chongqing [cstc2018jszx-cyztzxX0014]
With the growth of cloud computing and video services, the demand for network resources has increased, resulting in a significant rise in network energy consumption, which hinders the development of data centers. This paper proposes a joint optimization approach for device activation, rule installation, and data transmission to minimize network energy consumption. The simulation results demonstrate that the proposed algorithm achieves energy consumption close to the optimal solution and has lower complexity compared to existing algorithms.
With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.
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