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As a key component of high‐performance switches and routers, the packet forwarding engine (PFE) is mainly responsible for selecting the appropriate output port for tens of thousands of packets within an extremely short time frame. However, the performance of PFE is determined by the selected group membership algorithm. This paper puts forth a hybrid strategy–caching scalar‐pair and vectors routing and forwarding (CSVRF), consisting of virtual output port bitmap caching (VOPBC) and fractional‐N SVRF to address major multicast forwarding issues such as scalability by using content addressable memory. In CSVRF, a virtual output port bitmap cache is introduced, which includes the most popular combinations of output port bitmap and divides the big scalar‐pair into N sub‐groups to achieve parallel compute and the reusability of less bit‐length prime. The results demonstrate that the memory space and the forwarding latency are effectively reduced compared with previous work. In space efficiency, it only required 10% memory space compared with the original SVRF/fractional‐N SVRF, decreased 10% memory usage compared with pure VOPBC and nearly improved 1 to 4 orders of magnitude of packet processing time compared with the original SVRF and the fractional‐N SVRF respectively. [ABSTRACT FROM AUTHOR]

Cache‐enabled device‐to‐device (D2D) network has been deemed as an effective technique to offload the data traffic. However, the gain of the caching schemes is closely related to the homogeneity among users' preference distribution. To tackle this issue, recommendation is a promising proactive approach. It increases the request probability of recommended contents, reshaping users' contents demand patterns, and improving caching performance. Moreover, considering the heterogeneous network settings, i.e. content retrieval costs vary, the routing design becomes a non‐negligible factor on caching performance optimization. On these grounds, the average system cost of D2D‐enabled wireless caching networks with multiple BSs is first described. Then the routing strategies are designed together with caching and recommendation policies by minimizing the average cost of these networks. The optimization problem is proven as NP‐hard. To facilitate the analysis, the original problem is decoupled into two sub‐problems and solve them respectively. Afterwards, all the variables are optimized in an alternating manner until the convergence is achieved. The proposed algorithm's convergence performance and benefits over benchmark strategies in terms of total cost and cache hit ratio are supported by Monte‐Carlo simulation results. [ABSTRACT FROM AUTHOR]