Congestion-Aware and Energy-Aware Virtual Network Embedding
Congestion-Aware and Energy-Aware Virtual Network Embedding
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DOI：
10.1109/tnet.2019.2958367
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发表时间：
2020-02-01
影响因子：
3.7
通讯作者：
Chaczko, Zenon
中科院分区：
文献类型：
--
作者：
Pham, Minh;Hoang, Doan B.;Chaczko, Zenon
网络虚拟化是未来互联网体系结构的固有组成部分。网络资源是从基础底物中虚拟化的，并提供了弹性的配置并提供给客户按需的客户。在利用率，服务质量和能源消耗方面，网络资源的最佳分配一直是一个挑战。现有的解决方案考虑单目标虚拟网络嵌入（VNE）问题中的拥塞控制。本文定义了一个多目标VNE问题，称为拥塞感，能量感知VNE（Cevne）。目的是寻求一种节省成本，节省能源并避免网络拥塞的解决方案。 Cevne建模技术和解决方案方法同时应用加权方法和约束方法来搜索帕累托最佳解决方案，这些解决方案为所有三个目标提供了最佳的折衷解决方案。但是，解决VNE问题是NP-HARD。提出了一种涉及两阶段协调的Cevne的启发式解决方案。节点映射算法搜索三个目标的子最佳解决方案。链接映射过程是基于SDN的启发式算法，该算法在SDN控制器上部署路径服务和资源监视应用程序。使用SDN，段路由和开放网络操作系统平台（ONOS）技术实现解决方案。能量最小化是通过注册表来跟踪主动节点并将不活动节点设置为睡眠模式的。评估结果表明，多目标CEVNE方法是可行的，并实现了优化资源分配，改善运行时，节省能源消耗并控制网络拥塞的目标。
Network virtualization is an inherent component of future internet architectures. Network resources are virtualized from the underlying substrate and elastically provisioned and offered to customers on-demand. Optimal allocation of network resources in terms of utilization, quality of service, and energy consumption has been a challenge. Existing solutions consider congestion control in a single-objective virtual network embedding (VNE) problem. This paper defines a multiple-objective VNE problem called the congestion-aware, energy-aware VNE (CEVNE). The aim is to seek a solution that saves cost, saves energy and avoids network congestion simultaneously. CEVNE modelling techniques and solution approaches apply both the weighting method and the constraint method to search for pareto-optimal solutions that produce the best compromised solutions for all three objectives. Solving VNE problem is, however, NP-hard. A heuristic solution is proposed involving a two-stage coordinated CEVNE. The node-mapping algorithm searches for the sub-optimal solutions for three objectives. The link mapping process is an SDN-based heuristic algorithm that deploys a path service and a resource monitoring application on an SDN controller. The solution is realized using SDN, Segment Routing, and open network operating system platform (ONOS) technologies. The energy minimization is implemented with a registry that keeps track of active nodes and sets inactive nodes to sleep mode. The evaluation results showed that the multiple-objective CEVNE approach is feasible and achieves its goals of optimizing the resource allocation, improving the runtime, saving the energy consumption and controlling the network congestion.