Optimal Qos-aware network reconfiguration in software defined cloud data centers

被引:21
|
作者
Tajiki, Mohammad Mahdi [1 ]
Akbari, Behzad [1 ]
Mokari, Nader [1 ]
机构
[1] Tarbiat Modares Univ, Elect & Comp Engn, Tehran, Iran
关键词
QoS; Resource reallocation; Traffic engineering; SDN; Network reconfiguration overhead;
D O I
10.1016/j.comnet.2017.04.003
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Software-defined networking (SDN) as a new paradigm for networking provides efficient resource reallocation platform in emerging cloud data center networks. The dynamic nature of cloud data center network's traffic, as well as the existence of big flows make it necessary to periodically reprogram the network through the SDN controller. Therefore, it is critical for network researchers to minimize the side-effects of network reconfiguration. In this way, the most challenging issue is the number of rerouted flows that affect the network stability and QoS parameters. As a result, dynamic reconfiguration of the network with minimum overhead (i.e. minimum flow rerouting) is an interesting problem in SDN-based resource reallocation. In this paper, we mathematically formulated the resource reallocation problem as an optimization problem with minimum network reconfiguration overhead subject to QoS requirements of the applications' flows. In order to reduce the time complexity of solving the optimization problem, a forwarding table compression technique is devised making the proposed scheme an efficient resource reallocation method which can be used as an application on top of the SDN controller. Our Experimental results show that the proposed scheme decreases the network reconfiguration overhead dramatically while meeting the QoS constraints. Since the reconfiguration overhead of the proposed scheme is low, the controller can reallocate the resources more frequently based on the network condition. We also studied the impact of the proposed network reconfiguration scheme on packet loss and delay in the network. The results show that the proposed approach outperform the conventional methods. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:71 / 86
页数:16
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