數據中心網路之基於基因演算法的能源考量流量排程

馬毓晴

doi:10.6842/NCTU.2013.00056

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數據中心網路之基於基因演算法的能源考量流量排程

Energy Aware Flow Scheduling for Data Center Network Using Genetic Algorithms

馬毓晴 , Masters　　Advisor：田伯隆　　

繁體中文 DOI： 10.6842/NCTU.2013.00056

基因演算法；數據中心網路；能量； Genetic algorithm ； data center network ； energy aware

Abstract │ Reference (15) │ Times Cited (1) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

Cloud computing is one of the growing technology in recent years. Data center as the core of the supporting of the entire cloud services, the topic of how to build a data center is very important. Data center should support a large number of computing and the storage and transmission of data, which needs unblocked network. However, it’s not a simple question to choose a path of the Internet which can achieve targets such as high throughput and low delay. At the same time, data centers consume huge amounts of energy to ensure performance, which causes high operational costs, and huge carbon footprints are unfriendly to the environment. Therefore, we have to consider how to reduce energy consumption and keep high performance.
    This thesis focus on network equipments in the data center which have rapidly growth of energy consumption recent years. The switches contribute the largest propotion of energy consumption of network equipments, so turn off unneeded switches reduce energy consumption effectively. We can develop good routing algorithm to improve energy consumption of network equipments.
    It’s a complicated problem to decide routing path in a short period, so we choose genetic Genetic Algorithm to achieve our goals. Genetic algorithm is one of a heuristic algorithm. It solves the optimization problem quickly by imitating the way of the natural selection. We use fat-tree topology in our simulation, and make some improvements of GA in order to fit our problem and raise the correctness of its solution.

Reference ( 15 ) 〈TOP〉

[4] U.S. Environmental Protection Agency. Report to Congress on Server and Data Center Energy Efficiency. http://tinyurl.com/2jz3ft
連結：
[7] C. E. Leiserson, “Fat-Trees: Universal Networks for Hardware-Efficient Supercomputing,” IEEE Transactions on Computers, vol. 34, no. 10, October 1985, pp. 892-901.
連結：
[8] M. Al-Fares, A. Loukissas, and A. Vahdat. A Scalable, Commodity Data Center Network Architecture. In ACM SIGCOMM, pages 63-74, 2008.
連結：
[9] Holland, J. H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
連結：
[12] D. Wicker, M. M. Rizki, and L. A. Tamburino, “The multi-tiered tournament selection for evolutionary neural network synthesis,” Proc. Int. Conf. Combinations of Evolutionary Computation and Neural Networks, pp.207-215, 2000.
連結：

Times Cited (1) 〈TOP〉

陳宥臻(2015)。基因演算法和差分演算法在逐步移除型一區間設限資料上之可靠度評估應用。淡江大學統計學系應用統計學碩士班學位論文。2015。1-52。

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