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A distributed energy consumption optimization algorithm for content-centric networks via dual decomposition

机译:基于双重分解的面向内容的网络分布式能耗优化算法

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Due to the in-network caching capability, Content-Centric Networking (CCN) has emerged as one of the most promising architectures for the diffusion of contents over the Internet. Most existing works on CCN focus on network resource utilization, and the energy efficiency aspect is largely ignored. In this paper, we formulate the energy consumption issue as a Mixed Integer Linear Programming (MILP) problem, and propose a centralized solution via spanning tree heuristic and a fully distributed energy consumption optimization algorithm via dual decomposition (DD) to solve the problem for CCN. The dual decomposition method transforms the centralized energy consumption optimization problem into the router status, link status, and link flow subproblems. Simulation results reveal that the proposed scheme exhibits a fast convergence speed, and achieves superior energy efficiency compared to other widely used schemes in CCN.
机译:由于具有网络内缓存功能,以内容为中心的网络(CCN)已成为内容在Internet上传播的最有前途的体系结构之一。现有的有关CCN的大多数工作都集中在网络资源的利用上,而能源效率方面却被大大忽略了。在本文中,我们将能耗问题表述为混合整数线性规划(MILP)问题,并通过生成树启发式方法提出了集中式解决方案,并通过对偶分解(DD)提出了一种完全分布式的能耗优化算法来解决CCN问题。双重分解方法将集中式能耗优化问题转换为路由器状态,链路状态和链路流子问题。仿真结果表明,与CCN中其他广泛使用的方案相比,该方案收敛速度快,并且能效更高。

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