Topology Based Caching Optimizing Strategy in Named Data Networking
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摘要: 针对命名数据网络(NDN)存储空间的有效利用和应答内容的高效缓存问题，该文建立了模型并基于拓扑信息采用贪心算法求解，执行过程中考虑兴趣热度对其优化，从而有效缩短网络整体的缓存命中距离。该文基于ndnsim及一些真实拓扑数据完成了仿真实验，并对提出的算法与传统的prob算法，默认的沿途全部缓存(CEE)算法及基于度的差异缓存算法(HSS)做出了对比及分析，验证了算法的有效性。Abstract: In order to utilize storage space and fetch content effectively in Named Data Networking (NDN), this paper constructs a model for caching problem and proposes a greedy algorithm based on topology information. To optimize the algorithm, content popularity is introduced into execution. Furthermore, content hit distance is shortened effectively. This paper simulates a NDN network based on some real topology data with ndnSIM, and compares the proposed algorithm with traditional prob algorithm, default Cache Everything Everywhere (CEE) algorithm and degree based Heterogeneous Storage Size (HSS) algorithm through simulation. The results show that the algorithm proposed in this paper has better performance.
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Key words:
- Internet /
- Named Data Networking (NDN) /
- Content cache /
- Topology
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表 1 算法流程
第1部分：依据拓扑信息为节点分配缓存 节点计算路由表信息时： for all ${v_i} \in V$\; do for all ${v_j} \in V$\; do 计算最短路径树得到 $p(i,j)$ and $d(i,j)$ for all ${p_j} \in p(i,j)$ do for all ${v_k}$ in ${p_j}$ do $w(i,k)$+=1 for all ${v_j} \in V$\; do 计算 $w(i,j) \cdot d(i,j)$ 降序 $w(i,j) \cdot d(i,j)$ while $\sum\limits_{{v_j} \in V} {{c_i}{x_{j,{f_i}}} \le kC} $ ${x_{j,{f_i}}}$=1 end while 第2部分：执行过程中的优化 节点收到内容 If $({x_{j,f}}=1)$ then 在[0, 1]区间生成随机数 if (num<p) then cache f 
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表 2 真实网络拓扑参数
网络 拓扑性质 核心网规模 时间 GEANT 欧盟骨干网 37个节点，58条链路 2012年 GARR 意大利骨干网 48个节点，62条链路 2011年 DFN 德国骨干网 51个节点，80条链路 2011年 Janet 英国骨干网 28个节点，43条链路 2011年
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表 3 实验设置参数
实验参数 实验设置 缓存概率(prob) 0.5 缓存概率(p) 0.5 每节点产生内容数 100 每节点请求内容频率 10 个/s 每节点缓存空间 无限 内容流行度 Zipf(q=0.7) 仿真持续时间 10 s
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表 4 基于DC的HSS策略实现伪代码
for all ${v_j} \in V$\; do 计算经过 ${v_j}$的路径数 ${m_j}$ 更新最大路径数M ${v_i}$收到内容 在[0, 1]区间生成均匀分布的随机数 ------------------------if (num< $k{m_i}/M$) then cache f
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