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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]

With the development of mobile edge computing technology, edge caching provides a feasible solution for low‐latency and high‐reliability data forwarding in the Internet of Vehicles (IoV). Due to the limited number and storage capacity of the edge servers, and the limited communication range of a single vehicle, the delay and connection requirements are hard to be satisfied. A vehicle clustering based edge caching scheme is proposed in this paper, aiming at minimizing the content acquisition delay of the requesting vehicles (RVs) within the communication range of the edge servers. After getting the optimal vehicle association results, the minimization of content acquisition delay is transformed into a knapsack problem, which is then solved by a dynamic programming algorithm to obtain the optimal cache decisions. Simulation results show that the proposed scheme reduces task acquisition delay by at least about 100 s and increases the cache hit rate by about 50%, compared with the random cache scheme. [ABSTRACT FROM AUTHOR]

Parallel global optimization of expensive simulation models like nonlinear partial differential equations (PDEs) can speed up model calibration or project design decisions, but the impact of memory management on the efficiency of using parallel global optimization methods has not been previously studied. This paper quantifies cache memory limitations arising during parallel optimization of expensive PDE models. An efficient parallel optimization algorithm is applied to model calibration for two different, expensive real‐world PDEs (i.e., hydrodynamic and water quality analysis for a 250‐hectare lake). One of these two lake models takes 4.5 h per simulation in serial, but that PDE simulation time per simulation increases to 12 h with parallel optimization if default processor scheduling strategy is used on a modern nonuniform memory access multicore platform. We proposed a novel mixed affinity scheduling strategy for parallel simulation optimization that increases computational efficiency by as much as 20% over the default affinity strategy. [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]

The development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and relieve the burden of the backhaul link. Moreover, the introduction of recommendation mechanisms can reshape user's request and improve cache hit ratio. However, the optimization of recommendation and caching decisions is highly dependent on the users' preference information for files. Here, a joint optimization algorithm of recommendation and caching based on users' preference prediction with multiple base stations cooperative caching is proposed. To improve the caching efficiency, the Deep Crossing model is adopted to predict users' preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, an optimization problem to minimize the total transmission delay of the system is formulated. Then, the NP‐hardness of the proposed optimization problem is proved and it is decoupled it into three sub‐problems, namely recommendation, user access and caching optimization sub‐problems. Simulation results show that the proposed algorithm can effectively reduce the total transmission delay of the system. we propose a joint optimization algorithm of recommendation and caching based on users' preference prediction with multiple base stations cooperative caching. To improve the caching efficiency, we adopt the Deep Crossing model to predict users' preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, we formulate an optimization problem to minimize the total transmission delay of the system. [ABSTRACT FROM AUTHOR]

In general, it is accepted that gap formation significantly affects the placement of scatter‐hoarded seeds by small rodents, but the effects of different forest gap sizes on the seed‐eating and scatter‐hoarding behaviors of small rodents remain unclear. Thus, we examined the effects of a closed‐canopy forest, forest edge, and gaps with different sizes on the spatial dispersal of Quercus variabilis acorns and cache placement by small rodents using coded plastic tags in the Taihang Mountains, China. The seeds were removed rapidly, and there were significant differences in the seed‐eating and caching strategies between the stand types. We found that Q. variabilis acorns were usually eaten after being removed from the closed‐canopy forest and forest edges. By contrast, the Q. variabilis acorns in the forest gap stands were more likely to be scatter‐hoarded. The dispersal distances of Q. variabilis acorns were significantly longer in the forest gap plots compared with the closed canopy and forest edge plots. However, the proportion of scatter‐hoarded seeds did not increase significantly as the gap size increased. In small‐scale oak reforestation projects or research, creating small gaps to promote rodent‐mediated seed dispersal may effectively accelerate forest recovery and successional processes. [ABSTRACT FROM AUTHOR]

With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimise the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges with respect to the decision of cache contents. Here, a multi‐objective cache content strategy that aims to maximise the cache hit rate of SBSs in mobile edge networks (MENs) is proposed. The multi‐objective cache placement optimisation is formulated as a classification problem. Unlike previous work, mobility input attributes such as user locations, contact duration, communication ranges, contact probability between UTs and SBSs, etc. as well as content popularity and the correlation between these input attributes separating the decision space into two regions of cache and not cache are used. Stochastic gradient descent algorithm is used for the training of three supervised machine learning techniques: artificial neural network ANN, support vector machine (SVM), and logistic regression LR to define the hyperplane that separates the cache content decision space. Simulation results show that compared with the weighted‐sum approach, the SBSs cache hit rates increase on the average by 18.58%, 18.52%, and 18.2%, and the total energy consumption values decrease on the average by 33.49%, 53.19%, and 49.9% for ANN, SVM, and LR, respectively. [ABSTRACT FROM AUTHOR]