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The optimization of distributed cloud computing network routing model has become an important way to improve the efficiency of data access. It can realize data information docking through function of C-API, the adopted distributed computer network routing environment can optimize the supply management of routing control, so as to write test bench and implement distributed response for data. The research on the technology of MDA distributed cloud computing network as well as methodology can improve the optimization function of distributed cloud computing network routing.

With the development of Internet and information technology, cloud computing has attracted extensive attention from industry and academia. The large scale of resources, concurrent execution of multiple tasks and dynamic changes of application resource requests make the resource allocation of data center face severe challenges. To solve the problem of low balance of traditional resource allocation, this paper focuses on the resource allocation optimization of data center, and proposes the resource allocation strategy of data center based on cloud computing, so as to complete the effective resource allocation and assignment. This paper also verifies the designed resource allocation method through example research. The research shows that the distribution balance degree of resource allocation strategy based on cloud computing is significantly higher than the control group, which proves that the designed resource allocation strategy can solve the problem of low balance of traditional resource allocation.

To guard the communication quality of cell edge users (CEUs) and simultaneously improve the energy-spectrum efficiency, a novel 3-tier heterogeneous network (HetNet) model is proposed, which consists of macro cells, femtocells, and device-to-device (D2D) networks. Specially, with a predefined cell split factor R, the macro cell users are split into as cell center users (CCUs) and CEUs, respectively. Correspondingly, the total available spectrum band consisting of N channels is divided into CCU band and CEU band with a given coefficient p m . The CCU band containing p m N subchannels is shared by CCUs and femtocell users (FUs), and the CEU band containing(1 − p m ) N subchannels is shared by D2D users and CEUs. The perfect network synchronization is assumed, and a communication round consists of downlink transmission and uplink transmission phases. The battery-free D2D terminals harvest energy from the ambient radio frequency interference in the downlink transmission phase based on inverse power control scheme and communicate in the uplink transmission phase only when they harvest enough energy to perform channel inversion toward the receiver. For such 3-tier HetNets, by modeling the network elements as independent Poisson point processes (PPPs) and using stochastic geometry method, we first investigate the sufficiency probability that a D2D transmitter harvests enough energy to establish a communication link. Then, by combining sufficiency probability and channel access probability, the thinned independent PPPs for the locations of CCUs, CEUs, FUs, and D2D users are modeled. Based on these thinned PPP models, we perform a comprehensive investigation on the coverage probabilities of CCU, CEU, and FU uplinks as well as the D2D transmission. The simulated and numerical results show that using the presented cell split strategy enhances the performance of CCUs and CEUs because of the decrease of interference. The presented comparison analysis displays that the effect of D2D networks on the macro cell or the whole HetNets is limited and can be omitted. Therefore, the energy and spectrum efficiencies of networks are enhanced, simultaneously. At the same time, our results indicate that by using our derivations, we can perform the optimal design of the HetNets. KEYWORDScell center user, cell edge user, cell split, energy harvesting, heterogeneous networks 1 | INTRODUCTION Nowadays, with the proliferation of smart phones, tablets, and data-hungry multimedia applications, there has been exponential growth in mobile data and traffic. In recent report, 1 Cisco predicts that mobile data traffic will hit an annual run rate of 367.2 exabytes by 2020. More specifically, a compound annual growth rate to the data traffic from 2015 to 2020 is 53%. The fast and continuous growing mobile data traffic causes the deficiency of cellular network capacity. To mitigate this problem and meet the exponential growth of mobile traffic, the network operators have already started to devise new solutions. An advanced approach is strongly required ...